Banking industry volatility and economic growth

Problemsstatement

Research on the banking sector, a critical component of the modern economy, is surprisingly limited despite its recognized importance in driving economic growth through financial development Banks are essential for allocating funds across various sectors, including primary, secondary, and tertiary industries While much of the existing literature focuses on bank performance indicators such as liquidity, market capitalization relative to GDP, and credit to the private sector, few studies directly assess how banking operations impact economic growth Serwa (2010) highlights that banking crises can lead to a slowdown in output growth, emphasizing the need for a well-functioning banking system to support the infrastructure of other sectors Additionally, the returns on banking stocks are indicative of the quality of bank credits, as noted by Bruner & Simms (1987) and Cornell & Shapiro (1986), suggesting that the market for commercial bank securities is efficient and reflects the health of bank loan portfolios.

Therearecloserelationshipbetweenbankstockreturnsandeconomicgrowth.Baseon asset - pricingtheory,andonmanyresearchesofeconomistsCole,etal(2008);Moshirian& Wu,(2012);Lin&Huang,

(2012),p r o v e thatstockreturnsofbankingindustryreflectingt h e performanceo f t h e b a n k canp r e d i c t e c o n o m i c g r o w t h I n a d d i t i o n , accordingtotheviewofmark etefficiency,atanypointintime,pricesofsecuritiesinefficientmarketsreflectallknow ninformationavailabletoinvestors.I n o t h e r w o r d s , t h e e x p e c t e d futurecashf l o w s oft h e banksa r e reflectedi n t h e presentstockprice.Thisdependsonefficiencyof loanprojects.Bankstockreturnsw i l l reflecttheefficiencyofthemarketinusingfunds toinvestment.Furthermore,i n mostofcountries,commercialbanks,PLCs,arebroad lyrepresentativeofcountry’sb a n k i n g s e c t o r sincet h e y accountf o r v e r y h i g h positionint h e w h o l e b a n k i n g system.

InsomeresearchofCornett(2010)andNaceurandGhazouani(2007),institutionalframe work,s u c h a s c o u n t r y s p e c i f i c , f i n a n c i a l s y s t e m i n d i c a t o r s alsoh a v e significantinfluenceonbankingoperations.Moreover,intheinvestigationofAsan te,S , A g y a p o n g , D , & A d a m , A M.

( 2 0 1 1 ) , c o u n t r y c h a r a c t e r i s t i c s h e l p banksoperatesmoothlyaswellasi mprovetheirservices.Thispromoteseconomicg r o w t h significantly.Intheindicato rsrepresentativecountryspecific,thenegativee f f e c t s o f i n f l a t i o n h a v e been s t u d i e d i n a l o t o f modelso f e c o n o m i c g r o w t h , i t u n d e r m i n e s theconfidenc eofdomesticandforeigninvestorsaswellasconsumersa b o u t t h e futuree c o n o m i c g r o w t h (Andrés& Hernando,

1999).Secondly,t h e sustainablei n c r e a s e i n l i v i n g standardfora c o u n t r y meansa l a r g e r v o i c e o n t h e w o r l d stage.T h e r e a r e a l o t o f measuresoft h e q u a l i t y of g o v e r n a n c e h a v e b e e n b u i l t t o evaluateo f t h e q u a l i t y ofg o v e r n a n c e , amo ngt h e s e a r e t h e WorldwideG o v e r n a n c e Indicators, sixinstitutionalvaria blesrank countrieson s i x aspectsofg o od governance(Kaufmann,2013).Beside s,theimpactofbankingstockreturnso n economicgrowthiscapturedbycountrychar acteristicsandfinancialdevelopment(cole,etal,2008;Moshirian&Wu,2012)

Accordingtothepointofviewthatbankingoperationcontainsinformationabou tperformancesofalo t ofsectorsreflectingthe healthoftheeconomy( c o l e , etal, 2 0

Research indicates that the volatility of the banking industry is independent of overall market returns and economic growth (Moshirian & Wu, 2012; Lin & Huang, 2012) This volatility relates specifically to individual bank stock returns, suggesting that the information derived from these returns does not reflect the excess returns of publicly limited companies (PLCs) in the stock market Additionally, Naceur & Ghazouani (2007) found that the equity market's impact on growth operates independently of bank development's influence on growth Given that publicly traded banks constitute a significant portion of the financial sector, their stock returns often serve as a representative indicator of the entire banking sector across various countries.

Intheresearchesonbankingindustryvolatilityandeconomicgrowthtopic.Mos hirian&Wu,(2012),theinvestigatorsconcentratemoreoncountryclassified

9 criteriab y c o m p a r e t w o s u b s a m p l e s , d e v e l o p e d marketsa n d e m e r g i n g markets,which a r e n a t i o n s w i t h socialo r businessa c t i v i t y i n t h e processo f r a p i d growthandindustrialization,andPei-ChienLin,Ho-

ChuanHuang,2012paymoreattentiononthesampleindevelopedcountriesandmid dleincomecountries.Whereas,samplessurveyedinmystudyhavedifferentapproa ch,collecteddataisbasedonincomecriteriaandgeographicalregioncriteria,a nddatainvestigatedisd i v id e d intoonemainsampleincludingall22marketsandfi vesubsamplesi n c l u d i n g 1 1 uppermiddleincomegroup,11lowincomeandlower middleincomeg r o u p , 8 S u b -

The extreme volatility of banking industry stocks has triggered confusion in financial performance and contributed to the economic crisis Various components of volatility, including firm, industry, and market volatility, are countercyclical and can lead to fluctuations in GDP (Campbell, 2001) This study investigates the uncertainty of bank stock prices in relation to erratic market behavior and economic growth We also explore the effects of low and high inflation on economic growth in conjunction with banking volatility Additionally, we examine how country characteristics, such as Worldwide Governance Indicators and financial development variables, impact the relationship between bank volatility and future economic growth rates Through this research, we aim to fill gaps in current studies and contribute valuable insights to these economies.

Researchobjectives

- Toinvestigatetherelationshipbetweenbankingindustryvolatilityandeconomicg r o w t h i n fullsamplea n d fivesubsampled i v i d e d basingo n i n c o m e c r i t e r i a a n d g e o g r a p h i c a l regioncriteria.

Mainresearchquestions

- Whetherc o u n t r y c h a r a c t e r i s t i c s a n d financiald e v e l o p m e n t c h a r a c t e r i s t i c s strengthenorweakentherelationshipbetweenbankvolatilityandecono micgrowthi n selectedsamples?

Structureofthethesis

Chapter2 i n t r o d u c e b a n k i n g i n d u s t r y v o l a t i l i t y a n d economicg r o w t h , f i n a n c i a l i n d i c a t o r s andrealactivity,indicatorsofcountrycharacteristics, stockmarketsandeconomicgrowth,relevantliteraturereviewsoftheserelationships

Chapter3statesmethodology,modelspecificationanddatascopeused.Thischaptera l s o g i v e s r e a d e r s c l e a r e x p l a n a t o r y v a r i a b l e s u s e d , s u g g e s t e d s t a t i s t i c a l diagnosticofs i g n i f i c a n c e o f v a r i a b l e s Simultaneously,d a t a s c o p e a n d s o u r c e s t o g e t h e r withmodelconceptualframework.

Chapter4interpretsresultsandfindingsofthesisregressionmodel.Chapter5conclu deswiththesislimitationandfurtherresearchsuggestion.

Thischaptershowsoutsomeappropriatedefinitionsofbankingindustryvolatility,i n d i c a t o r s ofcountrycharacteristics,suchasVoiceandAccountability,Freeelecti on,P o l i t i c a l S t a b i l i t y a n d AbsenceofV i o l e n c e , G o v e r n m e n t Effectiveness ,R e g u l a t o r y Q u a l i t y , R u l e o f L a w , h i g h i n f l a t i o n , l o w i n f l a t i o n , f i n a n c i a l developmentf r o m t h e perspectiveofu n d e r s t a n d a b i l i t y a n d d a t a a v a i l a b i l i t y Simultaneously,thissectionalsodemonstratesanddiscussesintermsof a p p r o a c h i n g d a t a s e t s c o l l e c t e d , researcher,methodsapplied.Therefore,thischapteri n c l u d e s fiveparts.

Bankingindustryvolatilityandeconomicgrowth

According to asset-pricing theory, integration with the global stock market enhances price risks and promotes stock market development Devenez and Smith (1994) and Obstfeld (1994) argue that an integrated stock market increases risk diversification, thereby fostering economic growth Additionally, the concept of overvalued prices relates an asset's price to its future dividends, providing a corporate method to assess time and risk in asset valuation This indicates that stock returns in the banking industry, reflecting the performance of banks, can serve as predictors of future economic growth.

According to the Efficient Market Hypothesis (EMH), share prices are always traded at their fair value, reflecting all relevant information available in the market In efficient markets, the current prices of securities encapsulate known information accessible to investors, meaning that the expected future cash flows of banks are mirrored in their present stock prices The efficiency of loan projects directly influences bank stock returns, which are indicative of how effectively markets allocate funds for investment Additionally, in many countries, commercial banks and public limited companies (PLCs) represent a significant portion of the banking sector, highlighting their crucial role in the overall financial system Consequently, there is a correlation between bank stock returns and future economic growth.

Theg l o b a l financialc r i s i s l a u n c h e d f i r s t l y i n t h e U S spreadt o financialm a r k e t s a r o u n d t h e w o r l d w i t h b a n k i n g c r i s i s i n 2 0 0 8 T h i s l e a d s t o weak o p e r a t i o n i n supplycommoditiestoout- bankingsectorsandharmtheaggregateoutput.Laeven

&V a l e n c i a ( 2 0 1 0 ) f i n d t h a t r e d u c i n g i n o u t p u t h a p p e n i n g after b a n k i n g c r i s i s averagearound37%ofpotential output.Campelloetal., (2010)survey1,050

ChiefFinancialO f f i c e r s i n American.T h e y i n d i c a t e t h a t firmsd o n o t h a v e a b i l i t y t o b o r r o w externallyduringthecreditcrisisof2008.Thisleadsthemt ofailtocatchinvestmento p p o r t u n i t i e s , w i t h 8 6 % o f c o n s t r a i n e d U S I n short,t h e weakenedfunctionofbankingsystemcausenegativeeffectontheflowofecono micactivities.T h i s financialshockmaycause veryhighinflationrate ineconomiessurveyedaftert h a t , andhighinterestratesimultaneously.

IntheresearchonBankingindustryvolatilityandgrowtho f Pei-Chien Lin,Ho- Chuan(River)Huang,2012.TheyuseIndustry,Country

This study analyzes the impact of banking and stock market volatility on economic growth across developed and middle-income countries from 1980 to 1999, utilizing the difference-in-difference framework established by Rajan and Zingales (1998) It finds that banking industry volatility negatively affects the growth of industries that are more reliant on external finance; however, this detrimental effect diminishes when focusing on the relatively stable year of 1980 The 1990s, characterized by increased economic integration and unpredictable financial crises, highlight the importance of banking stability for long-term growth Additionally, the research by Moshiriana and Wu (2012) indicates that banking volatility serves as a significant indicator of future economic health, revealing a negative relationship with future growth Factors such as banking crises and accounting standards influence the relationship between bank excess returns and economic growth, while government ownership and insider trading laws further modify these dynamics Overall, the findings emphasize the critical role of banking stability in fostering economic growth in an integrated global economy.

Indicatorsofcountrycharacteristics

Research by Cornett (2010) indicates that the institutional framework significantly affects the banking system, with political bureaucrats undermining the performance of state-owned banks, which typically operate less profitably, maintain lower core capital, and exhibit higher credit risk compared to private banks Additionally, Naceur and Ghazouani (2007) demonstrated through panel data and GMM estimations across 11 MENA countries that the growth impact of equity markets is independent of bank development, while financial development plays a minimal role in economic growth This weak relationship can be detrimental to the economy, and there is a negative correlation between bank development and economic growth when controlling for stock market developments, highlighting the challenges posed by poorly functioning financial systems and inadequate institutional environments.

T h e r e f o r e , institutionalenvironment,s u c h a s c o u n t r y specific,financials y s t e m i n d i c a t o r s h a v e i m p o r t a n t c o n t r i b u t i o n s t o institutionalframeworkaswellasthebankingoperation They maketh erelationship between bankdevelopmentandeconomicgrowth negativeincondition ofcontrollingforthestockmarketdevelopmentsinceunwell- functioningfinancialsystemaswellaspoorinstitutionalenvironment.Besides,insomeres earch(Perez,etal.,2015;Filippidis,e t a l , 2 0 1 4 ; Cole,e t a l , 2 0 0 8 ) t e l l u s t h e sameresultt h a t u n - w e l l financialenvironmentharmsthebankingsectordevelopment.Thisdependsonspe cificstructurea s w e l l a s i n s t i t u t i o n a l f r a m e w o r k i n eachcountry.Furthermore,i n t h e investigateofA s a n t e , S , Agyapong,D , & Adam,A M.

( 2 0 1 1 ) w h e n t h e y usemarketcapitalization,bankcompetition,Domesticbankcredita spercentofGDPasindependentvariablestocoverthe studyin17yearsfrom1992to2009 inGhanabyA u t o r e g r e s s i v e DistributedLag(ARDL)andDynamicOLS(DOLS) method.Thisresearchp r o v e t h a t b a n k i n g institutionalc h a r a c t e r i s t i c , b a n k i n g c o m p e t i t i o n a n d stockmarketd e v e l o p m e n t makesmoothingo p e r a t i o n , improvings e r v i c e s int h e banks.Thisaffectseconomicgrowthpositivelyasaresults.

Macroeconomic theory has long examined the relationship between inflation and economic growth, particularly through the lens of the Phillips model and Keynesian perspectives These theories suggest a strong correlation between economic growth and inflation rates across different countries High inflation typically shifts aggregate demand to the right, indicating a positive relationship; however, when inflation rates are excessively high, aggregate demand can shift to the left, resulting in a negative correlation This dynamic is often influenced by factors such as monopolistic pricing, exchange rate volatility, and supply shocks, which can hinder economic performance Notably, when inflation exceeds 2%, it may lead to increased unemployment rates, posing significant challenges for the economy.

(1998),theseparatethehighgradei n theinflationrate(thegradeatwhichtheinflationrateexc eedcalculatedthresholdi s estimated40percentyearly)weaken thepreviousresearc hresultsandgiveouto n e newexploration.Thatisthehighgradeininflationharmthe economicgrowth,a n d otherwise,thelowgradeininflationboosttheeconomicinonec ountry.Thesevariablesh a v e n e g a t i v e relationship,e s p e c i a l l y i n t h e l o n g stag ei s s u p p o r t e d b y A n d r é s &Hernando,(1999).

Thesustainableincreaseinlivingstandardforacountrymeansalargervoiceontheworldsta ge.T h e r e a r e a l o t o f measuresoft h e q u a l i t y of g o v e r n a n c e h av e b e e n b u i l t t o evaluateo f t h e q u a l i t y ofg o v e r n a n c e , a m o n g t h e s e a r e t h e WorldwideG o v e r n a n c e Indicators,sixinstitutional variablesrank countries ons i x asp ectsofg o o d governance(Kaufmann2013).Theeffectofgovernancequalityonecono micperformanceissupportedbyKong,T(2011).Miyazawa,I.,&Zusman,E.

(2015)p r o v e thatgovernmenteffectiveness andruleoflawhavesubstantively andstatistical ly significanteffectsonprogresscountries,controlofcorruptionhaspos itivecor relation s wi t h the governmenteffectiveness.Ho wever, v o i c e andaccoun tability”and“controlofcorruption”donothavethesignificant effectsonactivities ofonecoun try In con trast ,Kaufmann,D., Kraay,A.,& Mastruzzi,M.

(2014).Authorevidenceoutdifferenceresults,politicalstabilityandregulatoryvariablesim pactoneconomicg r o w t h i n t h e s e p a r a t e r e g i o n ( S u b - s a h a r a n A f i c a ) p o s i t i v e l y a n d significantly,thisismorestronglyforthevariables ofvoiceandaccountabilityandr u l e oflaw,butgovernmenteffectivenesshavenegativei mpact.

Free media and freedom of expression are essential in mitigating asymmetric information problems, especially in countries with uncontrolled media, where rumors can significantly impact financial markets The presence of free media helps to alleviate the volatility of the banking industry, thereby supporting economic growth Moreover, free elections and the freedom of association, in conjunction with free media, create checks and balances on political power, preventing government officials and interest groups from enacting harmful macroeconomic policies that could make the country more vulnerable Ultimately, free elections and free media play a crucial role in constraining politicians and fostering a more stable economic environment.

Political_StabilityandAbsence_of_Violence:“Thevariablemeasureperceptionsofthel ikelihoodthatthegovernmentwillbedestabilizedbyunconstitutionalorv i o l e n t m e a n s , includingpolitically_motivatedviolenceandterrorism”.Po l it ic a l _ st a b il i ty a n d a b s e n c e _ o f _ v i o l e n c e mayl e a d t o l e s s volatilityi n p o l i c i e s

16 towardb u s i n e s s e s ( b a n k i n g a n d n o n - b a n k i n g ) i n t h e future,l e s s v o l a t i l i t y i n t h e economy.Itislesslikelytoleadtoca pitalflightorpanicsellingofcurrency.ThusP o l i t i c a l StabilityandAbsenceofVi olenceweakentheeffectofbankingindustryv o l a t i l i t y oneconomicgrowth.

Government_Effectiveness:“Thevariablemeasurethepublicqualityinservingitscitiz ens,andtheextentofitsindependence,thepolicyquality,andthecommitmento f a u t h o r i t i e s t o maketheirpolicy occurinthereallife.Themoreeffectiveness,thelessvu ln er ab i li t y offinancialf i e l d ”

A h i g h e r l e v e l of qu al i ty ofp ub li c s e r v i c es , h i g h e r degreeofitsdependencefr ompoliticalpressure,therewillbelessvolatilityi n t h e governmentc o n d u c t o f econ omicp o l i c i e s / a c t i v i t i e s T h e f i n a n c i a l marketmayb e l e s s v u l n e r a b l e t o p a n i c a n d h e r d i n g Thus,G o v e r n m e n t Effectivenessweakentheeffectofbankingindustr yvolatilityoneconomicgrowth

Regulatory_Quality:“Thev a r i a b l e measuret h e a b i l i t y o f a u t h o r i t i e s t o maket h e r e g u l a t i o n s feasibleandimprovethe privatefield”.Inemergingmarketeconomies,t h e o p e r a t i o n ofstate owned firmsisnormallyinefficiency.Thisaffectsoutcomeoft h e economynegatively.Soundpo liciesenhancingprivatesectordevelopmentw o u l d boosteconomicperformanceofade velopingeconomy,improveconfidencei n t h e financialm a r k e t T h u s R e g u l a t o r y Q u a l i t y w e a k e n t h e effecto f b a n k i n g industryvolatilityoneconomicgro wth

Rule_of_Law: “Thevar iable measurethe degr ee towhich gover nmentmakethe q u a l i t y ofpolicy,c o u r t s , crimel a w s , v i o l e n c e , enforcemento f c o n t r a c t , p r o p e r t y r i g h t s f e a s i b l e ” H i g h e r confidencel e v e l i n t h e l a w s y s t e m isf o r m e d f r o m t h e h i g h e r g r a d e o f R u l e _ o f _ L a w , effectivec o n t r a c t enforce ment,f a i r a n d impartialc o u r t , protectionofpropertyright.Highergradeofthisvaria blestrengthenstrustinth e economy.Thiscontributeinsignificantlytovolatilityinf inancialmarket.ThusRuleofLawweakentheeffectofbankingindustryvolatilityoneconomi cgrowth

Control_of_Corruption:“Thevariablemeasurethedegreetowhichauthoritiescarryo u t p o w e r t o p u b l i c ”.Economica c t i v i t i e s , sucha s i m p o r t / e x p o r t activities,manufacturingactivities,governmentactivitiestobankingacti vities,areimpacteds e r i o u s l y byc o r r u p t i o n , fromt h a t mayd e c l i n e t h e e f f i c i e n c y ofeconomicperformance.Highlevelofcorruptionmaycauseinefficienciesandmi sallocationof

Financialindicators andrealactivity

The four theoretical views on the impact of financial development on economic growth include bank-based theory, market-based theory, finance and services theory, and law-finance theory These theories emphasize that financial development is characterized by improvements in the quality, quantity, and efficiency of the financial system, which connects various activities and institutions Efficient financial instruments, markets, and intermediaries enhance the development of the financial system, ultimately influencing information, enforcement, and transaction costs (Levine, 2005) Financial development facilitates better resource allocation, investment monitoring, savings mobilization, risk diversification, and management Additionally, improvements in corporate governance through financing contribute to the efficient exchange of goods and services Each of these factors significantly impacts savings and investment decisions, thereby affecting economic output The critical role of financial intermediaries in fostering economic growth is supported by research from King & Levine (1993a) and Levine et al.

Threeindicatorsrepresentforthedevelopmentoffinancialsystem:(1) Domestic- Creditt o p r i v a t e field,( 2 ) Liquid-Liabilities(M3)a s % ofG D P ,

C a p i t a l i z a t i o n toGDP.Firstly,financialresourcesmostlyofc o r p o r a t i o n s are fundedtoprivatefieldinthewayofloans,non- equitysecurities,c r e d i t s forcommercial,accountsreceivable.TheseformDomestic-Credittoprivatefield.Secondly,theratioofLiquid-

The relationship between liabilities in the financial system and GDP is crucial for understanding economic growth M0 (total currency and deposits in the central bank), M1 (deposits and electronic currency), and M2 (time and savings deposits, transferable foreign currency, certificates, and securities) all contribute to the depth of financial intermediaries, which positively affects the provision of financial services The stock market plays a significant role in developing financial sectors and fostering economic growth, as it allows investors to reduce transaction costs and diversify risks This risk diversification encourages investors to allocate more of their assets into productive capital, thereby boosting economic growth A key indicator of financial development is the ratio of stock market capitalization to GDP, which reflects the total value of all listed shares in a stock market as a percentage of GDP.

Stockmarketsandeconomicgrowth

Forthedevelopingcountries,intheresearchofWang&Ajit(2013)usingunitroottestandt hecointegrationframworkprovethatstockmarketdoesnotaffecteconomicgrowth positiveinChina,thisresultfitwiththereportofHarris(1997)ford ev el o p i n g c o u n t r i es ifthe s t o c k marketi s m a i n l y ana d m i n i s t r a t i v el y d r i v e n market.Osam wonyietal.,

(2013),authorsfindforGhanaandNigeria,thereisnocausalrelationship b et we en stockmarketd ev el op men t andeco nomicgrowth,but t h e y haveabidirectio nalcausalrelationshipwhentheyuseGrangerCausalitytestprocedureasdevelop edinGranger.ThisreportisthesametoRahimzadeh,Farzad( 2 0 1 2 ) usingdatarelate dtotheMiddleEastandNorthAfricaintheperiod1990-

(2010)theempiricalf i n d i n g s o f t h e s t u d y showt h a t p o l i c y measurest a k e n t o increaset h e o u t p u t , liquidityandactivityofthestockmarketwillfurtherincreaseoutputg rowth.

In their 2013 study, the authors highlight the significant interaction between banking development and stock market growth, emphasizing that while both contribute to output growth, banking development plays a more crucial role in low-income countries, whereas stock market development is more influential in high-income or low-inflation countries They identify a positive relationship where banking development fosters stock market growth, alongside a negative impact of stock market development on banking growth Additionally, the research presents evidence of feedback effects, indicating that growth influences both banking and stock market development Rabiul (2010) further supports these findings by employing the Generalized Method of Moments (GMM) to analyze data from 80 developing countries over the period from 1973 to 2000.

2 provethatbanksandstockmarketshavepositiveimpactsoneconomicgrowthseparatelyan dpositively,theyareimportanttoboostlong-rungrowthindevelopingcountries.In addition,authoralsoindicatestherelationbetweenfinanceandgrowthi s non-linear.In otherwords,financialdevelopmentismorebeneficialforcountriesdevelopingfromavery lowstage;howeverthemarginalbenefittorichcountriesislessthanforlowincomecountrie sduetodiminishingmarginalreturns.

Domestic Credit to PrivateSector Performance of bank

Liquid Liabilities (+) Moshiriana and Wu, 2012

Conceptualframework

Inprevioussections,the impactofbankingvolatility,bankingexcessreturn,marketexcessr e t u r n , c o u n t r y specif ics,financiald ev e l o p m e n t h a v e beeni n t r o d u c e d a n d discussedwiththepurposei sthatthereaderswillhaveoverlookonfactorsimpactingonGDPgrowthrate.Thiss ections h o ws outsomefollowingissue: (1)d a t a andsamplesize,

Data

Thedatasetsincludeinformationinincomecriteriaandgeographicalregioncriteriab y resear chingindetail in on e mainsampleandfivesubsamples:all22markets,a n d 1 1 u p p e r middlei n c o m e markets,1 1 l o w incomea n d l o w e r middlei n c o m e markets,8

S a h a r a n A f r i c a markets,6 S o u t h A s i a a n d EastA s i a markets,5 L a t i n Ameri camarketsassubsamplesovertheperiodfrom2003to2014inquarterdata,w i t h t h e l o n g e s t times e r i e s b e i n g 1 1 yearsa n d t h e s h o r t e s t 0 2 y e a r s T h e samplep e r i o d fore a c h marketisshowni n column4 o f T a b l e ( 3 ) T h e s e l e c t e d economies’d a t a i s b a s e d o n t h e a v a i l a b l e datao n b a n k _ e q u i t y _ p r i c e , q u a r t e r l y macro economictimeseriesandshort_terminterestrates.Table(1)summaryv a r i a b l e c alculatedandtheirsources.

The primary variable in this study is banking industry volatility, which can be estimated through various methods This research employs a detailed disaggregate approach based on the methodology of Campbell et al (2001) to calculate banking volatility We begin by calculating the portfolio of listed banks for each market using data from International Datastream sources For Indonesia, we analyze a maximum of 30 listed banks, while Mauritius and Uganda have a minimum of 2 In the Philippines, despite having a complete dataset of market prices for 19 banks, only one bank's market capital data is available Our analysis focuses on the available data for individual banks, as all 22 countries examined have market economies, and we only collect data from domestic stock exchange markets.

The study focuses on banks operating in domestic and foreign markets, specifically those listed on international stock exchanges, while excluding certain banks from the sample Consequently, only a limited number of banks can accurately represent the entire market Key variables such as interest rates, GDP series, and market price indices for each country were sourced from Datastream The research incorporates diverse data sources, as summarized in Table 1, which details the variables analyzed To extend the time-series data for this study, we employed an overlapping method to handle yearly data with quarterly observations.

Secondofall,thispaperusethecontinuousreturnoverRf(risk-freerate)tomeasuretheexcess- returninweightedvalueontheportfolioslistsofthebankfortheeveryeconomyw hichisestimated.ThisresearchcollectTreasury-

Billrateint h r e e monthorDeposit- rate inthree monthdepending onthe d at a inDatastream.We useM C ( M a r k e t -

Capitalizationofthebankingfieldatt h e endoftheperiod(t-1) andremainconstantwithin period (t)buildtheweightforbank-j.Thirdofall,Excess- Returnisbuiltonthemarketindexofeacheconomies,i n t h e n e x t stage,t h e r e g r e s s i o n s ofE x c e s s - R e t u r n o f t h e b a n k inq u a r t e r d a t a againsttheExcess- Returnofthemarketinquarterdataisconductedtogetthebêtao f e a c h e c o n o m y , w h i c h i s supposedc o n s t a n t o v e r t h e samplep e r i o d foreacheconomy,b u t i t i s assumedt o v a r y t i m e l y inl o n g r u n Nonetheless,t h i s s t u d y analyzeslargeamo untofeconomies.Itismore sensetosimplifyforourassumptiona n d runningthesamemodelfordifferenteconomiesint hemostconsistentway.

Afterallaboveprocess,wehavecompletedatasetson22economies.Wedividethepaneldatai ntoonemainsampleandfivesubsamples:all22markets,and11uppermiddlei n c o m e g r o u p , 1 1 l o w incomea n d l o w e r middleincomegroup,8 S u b -

S a h a r a n Africagroup,6SouthAsiaandEastAsiagroup,5LatinAmericagroupassubs amples.WefollowthethresholdlevelsofGNIpercapitaarethosecalculatedb y t h e WorldBanki n 2 0 1 2 Countriesw i t h l e s s t h a n $ 1 , 0 3 5 G N I p e r capitaa r e b e l o n g t o l o w - i n c o m e c o u n t r y g r o u p , t h o s e w i t h between$ 1 , 0 3 6 a n d $ 4 , 0 8 5 a s lowe rmiddlei n c o m e c o u n t r y g r o u p , t h o s e w i t h between$ 4 , 0 8 6 a n d $ 1 2 , 6 1 5 a s im uppermiddleincomec o u n t r y g r o u p G N I p e r capitai n d o l l a r termsi s estimatedus ingtheWorldBankAtlasmethodin2012.

Int h e t h i r d step,w e c a l c u l a t e q u a r t e r l y b a n k v o l a t i l i t y ( V O L i t ) i n monthly d a t a , whereas,Moshirian&Wu(2012)calculateitonweeklydata,asfollowformula:

VOLit=Var(Rit)=β 2 VAR(Rmit)+σσ ^2it

The Value at Risk (VaR) for the banking industry is calculated using the formula VAR(Rmit) = ∑m∈t(Rmim - àmit)², where σ²it = ∑m∈t(Rim - βimRmim)² Here, βimi represents the beta of the banking sector in relation to the market, and Rmimi denotes the monthly excess market return The term àmit refers to the moving average of monthly excess market returns for country i over a specified period, t, which is defined as a quarter This calculation method differs from the approaches outlined by Moshirian & Wu (2012) and Lin & Huang.

Rimisthemonthlyexcess- returninweightedvalueontheportfolioslistsofthebankfortheeconomy- i.Thisstudydeductthemonthlyrateofrisk-free,whichisgettingb y d i v i d i n g theyearlyinterestrateinshortrunby 12weeks,asaresultwehavetheexcess- returnforeachmonth.MakereferencetothemethodofColeetal.

(2008),mostofv a r i a b l e s i s estimated.Thesea r e c o n t i n u o u s realg r o w t h r a t e ( G r o w t h ) , w h i c h makeu p d e p e n d e n t v a r i a b l e , l a g g e d E x c e s s -

R e t u r n oft h e market(Rm),whichisc o n t r o l l e d v a r i a b l e , t h e c h a r a c t e r i s t i c s ofeachcountry,w h i c h a r e sixg o v e r n a n c e i n d i c a t o r s , lowinflatio n,h i g h i n f l a t i o n , a n d i n d i c a t o r s o f f i n a n c i a l development,whicharedomesticcredittoprivatesector,liquidliabilities

Theindicatorspresentingcharacteristicsofeachcountryrelatetoeconomicgrowtho r t h e e f f i c i e n c y oft h e e c o n o m y i n o n e c o u n t r y i n l o n g s t a g e T h e s e i n d i c t o r s respectthedifferenceinthe crosssectionininstitutionalframeworkofonecountry.Next,w e estimatet h e effecto f t h e d i f f e r e n c e s i n i n s t i t u t i o n a l frameworko f o n e c o u n t r y ontherelationshipbet weenbakingindustryvolatilityandeconomicgrowthr a t e

- Thebankingindustryvolatility(primaryvariable)isexpectedtocreatenegative effectongrowth(Pei-ChienLin,Ho-

- ThedependentvariableisGDPgrowthrate(Growth).Itiscalculatedbytaki nglogarit oftheratioofGDPatperiodtandGDPatperiodt-

1attheconstantprices( Growth=LOG(GDPt/GDP t-1 ))

- Thec o n t r o l v a r i a b l e isl a g g e d markete x c e s s r e t u r n I t i s defineda s t h e e x c e s s r e t u r n onthemarketindexincountry- i.Itisestimatedbytakinglogaritoftheratioo f marketpriceindexattheendofperiodtand marketpriceindexatperiodt-1ofc o u n t r y i,tisinquarter,thenminustherisk- freerate(Rf),whichisTreasury-Billr a t e i n t h r e e montho r d ep o s i t - r a t e i n t h r e e m o n t h T o compromiset h e empiricalresults,t h e impacte x p e c t e d directiona m b i g u o u s i s i n d i c a t e d byWang& A j i t ( 2 0 1 3 ) , Harris(1997), Osamwonyietal.,(2013),Rahimzadeh,Farzad(2012),Cooray.A,(2010)

( Rm=Rmit=log(Pmit/Pmi(t-1)-Rfit ))

SixWorldwideGovernanceIndicators(WGI),theyaredatasetcoversomequalifyi n d i c a t o r s presentedthehealthofGovernmentovertheworld.Theyrangeinunits froma r o u n d -

2 5 t o 2 5 , w i t h h i g h e r v a l u e correspondingt o b e t t e r g o v e r n a n c e o u t c o m e s (Kaufman,2013).Thedataisinannual format, soweusetheoverlappingannualdatawithquarterlyobservationstoprolongthetime-series.

Voice_and_Accountability:T h i s v ar i ab l e d o n o t h av et h e significanteffectso nacti vitiesofo n e c o u n t r y (Miyazawa,I , & Zusman,E , 2 0 1 5 ) Buti n o n e o f t h e resear chofOmoteso,K , & IsholaM o b o l a j i , H

( 2 0 1 4 ) , t h e y e v i d e n c e t h a t t h i s indicatora n d economicg r o w t h h a s positiv er e l a t i o n s h i p forS u b - s a h a r a n Africar e g i o n

- Political_StabilityandAbsence_of_Violence:Itisexpectedtoboosttheeconomya n d weakentheeffectofbankingindustryvolatilityoneconomicgrowth.

- Government_Effectiveness:Ithaspositivelyandstatisticallysignificanteffectsonprogress countries(Miyazawa, I.,&Zusman,E.2015),butthisisnegativeinthe researcho f O m o t e s o , K , & I s h o l a M o b o l a j i , H ( 2 0 1 4 ) forSub- saharanA f r i c a region.

- Regulatory_Quality:Itisexpectedtoimprovetheeconomyandweakentheeffecto f ban kingindustryvolatilityoneconomicgrowth

- Rule_of_Law:I t h a s p o s i t i v e l y a n d s t a t i s t i c a l l y significanteffectso n pro gressc o u n t r i e s (Miyazawa,I.,&Zusman,E,2015).Itisexpectedtoweakentheeffect ofb a n k i n g industryvolatilityoneconomicgrowth

- Control_of_Corruption:It hasp o s i t i v e correlationsw i t h t h e governmente f f e c t i v e n e s s (Miyazawa,I.,&Zusman,E,2015)

- Inflation(1)isthedummyvariable,i t t a k e s o n t h e v a l u e o f o n e w h e n t h e va lu e ofinflationissmallerthanthesamplegroup(alleconomies)medianandav a l u e ofz eroot h er wis e T h e d at ai s inannualformat,s o we useth e o v e r l a p p i n g annualdata withquarterlyobservations.Thepositivesignisexpectedfortheeffecto f thisvariableo ngrowth(Bruno,M.,&Easterly,W,1998;Andrés&Hernando,1 9 9 9 )

- Inflation(2) isthed u m m y v a r i a b l e t h a t t a k e s o n t h e v a l u e ofo n e i f inflationi s greatert h a n t h e sampleg r o u p ( a l l e c o n o m i e s ) mediananda v a l u e ofz e r o otherwise.Thedataisinannualformat,soweusetheoverlap pingannualdatawithq u a r t e r l y observations.T h e negativesignisexpectedforthi svariablesongrowth(Bruno,M.,&Easterly,W,1998;Andrés&Hernando,1999)

Credittoprivatefieldisdefinedasfinancialresourcesmostlyofc o r p o r a t i o n s , w h i c h a r e f u n d e d t o p r i v a t e f i e l d i n t h e w a y ofl o a n s , n o n - e q u i t y securities,creditsforcommercial,accountsreceivable.Itisbenefittoimpro vetheeconomic.Thus,thepositivesignisexpectedforthisindicator.

LiabilitiesinfinancialsystemtoGDP.M0(thetotalvalueofc u r r e n c y anddepositint hecentralbank)plusM1(depositsandelectroniccurrency)p l u s M2(timea n d s a v i n g s d e p o s i t e s a n d o t h e r depositfortransferableforeigncurrency,certificatesaswe llassecurities repurchaseagreements)pluschecksfortravelers,paper for trades,timedepositsforforeigncurrency,andshareoffundsfor themarket.Itplaystheimportantroleinimprovingthehealthofthebankingsystema s w e l l a s w h o l e t h e economy.C o n s e q u e n t l y , positivesigni s e x p e c t e d fort h i s i n d i c a t o r

CapitalizationtoGDP.Itequalsthetotalvalueofalllistedsharesinastockmarketas apercentageofGDP.I t isbenefittoboosttheeconomicgrowth,sotheexpectedsignoft herelationshipbetween thisvariable andeconomicgrowthispositive(Jun,2012;Levine&Zervos,1996;Ndlovu,2013;Wue t al.,2010).

Political PoliticalstabilityandA bsenceofviolence Positive WGIAnnually

Regu_qua Regulatoryquality Positive WGIAnnually

Withthepurposeofprolongthetime- seriesinformationinthisresearch,thisstudyh a n d l e yearlydatabyoverlappingmet hodwithobservationsinquarter.Thedescriptivestatisticsandcorrelationmatri cesforGrowth(GDPgrowthrates),vol( b a n k i n g i n d u s t r y v o l a t i l i t y ) , Rb( l a g g e d b a n k excessr e t u r n ) , a n d

Methodology

Wea p p l y t h e g e n e r a l i z e d _ m e t h o d _ o f _ m o m e n t s (GMM)econometri ctechniquesdevelopedfordynamicpanel- datamodels.BaseonthemodelofColeetal.,2008,Campelloetal.,2010,Cornettetal.,20 10;Moshirian&Wu2012;Arellanoan d Bond,1 9 9 1 ;

A r e l l a n o andBover,1 9 9 5 ; A h n a n d Schmidt,1995;Blundell andBond,1 9 9 8 ) T h e differenceGMMandsystemGMMbecomemorepopularduetoi t s s i mp l e i m p l e m e n t a t i o n a n d t h e w e a k a s s u m p t i o n o n i n s t r u m e n t variable s.Weexamineaﬁxed- effectdynamicmodelforfullsampleandfives ub s amp l es atthebeginning:

Yit=α+σYi(t-1)+σ’Xi(t-1)+σni+σ it

Inabovemodel,iandtisusedtoindicatecountryandtimeperiod;YitistheGDPg rowt h rateforselectedsamplesatthetimet;Yi(t-

The study examines the relationship between banking industry volatility (VOLit) and various explanatory variables, including lagged market excess return (Rm) and interaction terms that incorporate banking institutional characteristics and country-specific factors The model accounts for unobserved effects specific to each country and includes an error term (εit) By utilizing these interaction terms, the research aims to analyze how indicators of country characteristics and financial development influence economic growth in relation to banking volatility To apply the Generalized Method of Moments (GMM), the study effectively removes group effects from the fixed-effect model through a simple technique known as first differencing.

Endogeneity in the model leads to significant issues, resulting in inconsistent and biased outcomes due to the relationship between the lagged dependent variable and the error term To address these challenges, we utilize properly instrumented variables, as suggested by Arellano and Bond (1991), which recommend using lagged values of explanatory variables at their original level The authors also assert that disturbances in the time-varying components should not be correlated Specifically, the assumption E(εitεis) = 0 holds for i = 1, ,N and for all t ≠ s Furthermore, the initial condition Yi1 must not be correlated with future realizations of the error term, expressed as E(Yi1it) = 0 for i = 1, ,N and t = 2, ,T This allows us to apply a model with m = 0.5(T−1).

In the context of GMM estimators, the matrix Wi represents the (T−2)×q matrix of regressors, while Zi is the (T−2)×m matrix and yi is the (T−2) vector The weighting matrix AN plays a crucial role in deriving a set of GMM estimators based on moment conditions The difference GMM, known as the original estimator, tends to be less effective at minimizing sample length and may not adequately capture the extensive information present in the levels of variables and their first differences, leading to inefficient calculations (Ahn and Schmidt, 1995) To address these issues, Arellano and Bover (1995) introduced System GMM, which offers lower bias and higher precision in infinite samples In System GMM, the original levels are linked to first-differenced regressions, utilizing lagged first-difference variables as instruments in the level regressions and lagged level variables in the first-differenced regressions, thereby enhancing the estimation process with additional moment conditions.

Ino u r study,t h e r e a r e t w o maint e c h n i q u e s employedforp a n e l d a t a : t h e f i r s t - differencedG M M m e t h o d , G M M ( D I F ) , a n d t h e u p d a t e d o n e ass y s t e m G M M , GMM(SYS).G M M m e t h o d p r o v i d e c o n s i s t e n t estimators,sot h e y h a s a l o t o f a d v a n t a g e s toothermethodwhenapplyingforthedynamicpa nel.AlthoughBlundellandBond,(1998),andArellano andBover,

(1995)provethat t h e GMM(SYS)methodh a s somew e a k p o i n t s comparedt o

G M M ( D I F ) one,particularly,i n t h e caseo f lo w a u t o r e g r e s s i v e p a r a m e t e r , l e s s t h an 0 8 , a n d l a r g e numbero f t i m e - s e r i e s o b s e r v a t i o n s T h e d a t a o f t h i s researchcomprise22economies.Theshort esttime- seriesobservationhas07quarter,andthelongestoneh a s 47quarter.Ingeneral,theautor egressiveparameterissmall,andthenumberoftimeperiodsislarge.Therefore,theresu ltsofmeasuringinbothGMM(SYS)andGMM(DIF)methodsaregivenout

In ordertotesttheconsistencyoftwoGMMtechniquesabove,twodiagnostictestsareused Firstly,Arellano-

Bondtestisapplied tochecksecond orderau to co rr el at ion i n t h e firstdifferen cedr e s i d u a l s A n o t h e r k i n d o f t e s t t h a t i s J-statistic-

Hansent e s t , iti s a p p l i e d t o c h e c k over-identifying- restrictions.I t s performanceistoexaminethesuitabilityofthemodel.Thenullhypothes isoftheJstatisticimpliesthatinstrumentsareendogenous.Thismeanthat,theseinst rumentv a r i a b l e s arecorrelatedwiththeerrorterm.Inthecasethatthenullhyp othesisisrejected,theinstrumentssatisfytherequirementoforthogonalityconditions.

Thissectionw i l l b e d i v i d e d i n t o t w o fields.T h e firstfieldi l l u s t r a t e s descriptive s t a t i s t i c s ofvariablesanditseconomicmeanings.ThenextfieldshowsoutEcon ometricr esu l ts a n d i t s economicme an i n g s T h i s fieldalsog i v e o u t t h e bestmodel schosen,andtheunsuitablemodelsuncollected.

Descriptivestatistics ofvariables

A l l economies Uppermiddleincome LowandLower middleincome growth rm Rb vol growth rm rb vol growth rm rb vol

Africa SouthAsia&EastAsia LatinAmerica growth rm rb vol growth rm rb vol growth rm rb vol

Mean 0.007 -8.119 -8.056 0.017 0.006 -6.455 -6.296 0.010 0.007 -8.691 -9.305 0.074 Std Dev 0.027 4.710 4.919 0.061 0.023 3.765 3.759 0.023 0.020 6.054 6.287 0.248 Min -0.087 -25.836 -25.776 0.000 -0.073 -18.770 -18.741 0.000 -0.042 -25.464 -48.850 0.000 Max 0.104 -0.725 0.000 0.634 0.126 0.020 0.053 0.226 0.074 0.138 1.150 1.978

Economy Region Sampleperiod Interestrate(Year) Year No.of

F orthefullpanelcomprise22economies,theaverageGDPgrowthrateisaround0 7 % withar angeof-

20.1%to12.6%.Whereas,theaveragebankingindustryv o l a t i l i t y isthehighest(3.2%)wit harangeof0.00381%to197.8%.Theaveragemarketexcessreturnisnearlythesametheavera gebankexcessreturn,around-7 3 4 9 % witharangeof-

Fortheuppermiddleincomeeconomies,theaverageGDPgrowthrateaccountsforl o w e r le vel(0.7%)witharangeof-

20.1%to12%.Incontrast,theaveragebankingindustryvolatilitymakeupthehighestd egree(4.6%)witharangebetween0 0 0 4 7 9 % a n d 1 9 7 8 % , t h e averagemarket e x c e s s r e t u r n i s n e a r l y t h e samet h e averagebankexcessreturnaround-

7 5 % t o 1 2 6 % , t h e averageb a n k i n g industryvolatilityis1.4%.Ithavearange between0.00381%and63.4%.Conversely,t h e v a l u e o f t h e a v e r a g e marketex cessr e t u r n a n d t h e a v e r a g e b a n k excessr e t u r n a r e a l m o s t e q u a l I t a c c o u n t s f o r t h e l o w e s t l e v e l a p p r o x i m a t e l y -

8.7%to10.4%,theaveragebankingindustryvolatilityishighest(1.7%)witha r a n g e o f 0 0 0 5 7 4 % t o 6 3 4 % , t h e averagemarketexcessr e t u r n makeu p lowestd e g r e e ( - 8 1 1 9 % ) w i t h a r a n g e b e t w e e n - 2 5 8 3 6 % a n d -

Fort h e S o u t h A s i a andEastAsiae c o n o m i e s , t h e averageG D P g r o w t h r a t e isaround0.6%witharangebetween-

7.3%and12.6%,theaveragebankingindustryv o l a t i l i t y i s a r o u n d 1 % w i t h a r a n g e f r o m 0 0 0 4 7 9 % t o 2 2 6 % T h e v a l u e oft h e averagemarketexcessreturnandtheaveragebankexcessreturnarealmostequal.I t is approximate-645.5%,andhavingrangefromabout-1877%toabout2%.

Fort h e L a t i n Americaeconomies,t h e averageG D P g r o w t h r a t e i s a r o u n d 0 7 % w i t h a r an g e of-

4 2 % t o 7 4 % , t h e averageb a n k i n g i n d u s t r y vol at ility isa r o u n d 7.4%withar angeof0.00975%to197.8%.Thevaluesoftheaveragemarketexcessre t u rn and t h e avera geb an k excessreturn a r e nearlythesame.It acco u n t fort h e lowestdegree(-

Insummary,theaverageGDPgrowthratesforthesixsamplesarenearlythesamea r o u n d 0.7%withthesmallestrange.Whereas,thevalueoftheaveragebankingindustry volatilityispositiveandhigherwithlargerrange.In contrast,thevaluesoft h e averagemarketexcessreturnandtheaveragebankexcess returnar e negativea n d fluctuatedwidelyamongsamples.Theyhavethelargestrangeinge neral.

2.4%foralleconomies,u p p e r middleincomeeconomies,lowincomeandlowermiddlei ncomeeconomies,Africaeconomies,L a t i n Americae c o n o m i e s , respectively.N e v e r t h e l e s s , b a n k i n g v o l a t i l i t i e s areslightlycorrelatedwithmarketexcessret urns,withthesimplecorrelationo f 0 0 6 4 , 0 0 9 8 , -

0 1 5 1 fort h e sampleo f a l l markets,u p p e r middlei n c o m e markets,lowincome andlowermiddleincomemarkets,respectively.Thisc o r r e l a t i o n i s s l i g h t l y h i g h e r i n g e o g r a p h i c r e g i o n g r o u p s , w i t h t h e s i m p l e correlation of-

0.219,0.198 fort h e sampleofAfricamar k et s , SouthAsiaandEastAsiamarkets, LatinAmericamarkets,respectively.

Thefinancialshock ineachgrouporeach countrymaycaus e very highinflationr a t e ineconomiessurveyedafterthat,andhighinterestratesimultaneously.

Econometricresults

Themaino b j e c t i v e oft h i s s t u d y ist o e x a m i n e whethert h e r e i s t h e relationshi pbetweenbankingindustryvolatilityandeconomicgrowthinonemainsampleand fivesubsamples.Weaddressthisissuebylookingatthesignificanceofthecoefficientso frelevantvariablesratherthanthescaleoftherelevantcoefficients.

Sysestimationsfortheallpanelcomprising22marketsample.Inthesecondstage,we repeatestimationsofeachofthetwomethodsusing11uppermiddleincomeeconomie s,11lowincomea n d l o w e r middleincomeeconomiesastwosubsamples.Inthenextstep,werepeatestimatio nsofeachofthetwomethodsusing8Sub-

Inthissection,wetesttheabilityofbankingindustryvolatilitytopredicteconomicg r o w t h i n a panela n a l y s i s combiningd a t a f o r a l l 2 2 m a r k e t s , a n d t h e n e x a m i n e wh etherthisrelationship isinfluencedbyaseriesofcountry- specificcharacteristicso f eachcountry.Toobservethiseffect,weinteractbankingindus tryvolatilitywiththesecountrycharacteristicsvariables.Thesignsonthecoefficient softheseinteractiontermsistheevidenceforustorecognizewhetherthesevariablesstr engtheno r w e a k e n t h e impacto f bankingindustryv o l a t i l i t y ont h e e c o n o m i c acti vities.

5 presentt h e d i f f e r e n c e GMMresults,a n d P a n e l s A a n d B ofT a b l e 6 presentt h e systemGMMresultsforuppermiddleincomemarkets,andlowincomeandlowermiddl ei n c o m e m a r k e t s , respectively.P a n e l s A , B a n d C ofTab l es 7 presentt h e differe nceGMM results,wh i l eP an els A, BandC ofTable8 presentth e system GMMr e s u l t s forSub-

SaharanA f r i c a group,S o u t h A s i a a n d E a s t Asiag r o u p , LatinAmericagroup,r espectively.

Dynamic panel GMM estimationresultsforfullsampleof 22 economies 1

Dynamic panel GMM(DIF) estimationresultsforboth subsamples 1

Table 6a: Dynamic panel GMM(SYS)estimation results forbothsubsamples

Table 7a: Dynamic panel GMM (DIF) estimation resultsforthreesubsamples

Table 8a: Dynamic panel GMM(SYS)estimation resultsforthreesubsamples

Table4areportstheresultsfromaregressionofGDPgrowthratesonthelagged v a lu e s oftheGDPgrowthrates,bankingindustryvolatility,marketexcessreturns,a ndo t h e r interactionterm.Thecoefficientsf o r a l l v a r i a b l e s a r e n o t s t a t i s t i c a l l y s i g n i f i c a n t , exceptforlaggedvaluesoftheGDPgrowthratevariablefor fullsampleof2 2 e c o n o m i e s u n d e r differentGMMmethodsa l t h o u g h a l l t h e s e m o d e l s a r e e n d o g e n o u s andtheyhavevalidandrelevantinstruments.

Intable4b,column1reportstheresultsfromaregressionofGDPgrowthratesont h e la ggedvaluesofthegrowthrates,bankingindustryvolatility andmarketexcessreturns.I n column8 r e p o r t s t h e r e s u l t s froma r e g r e s s i o n ofg r o w t h r a t e s ont h e l a g g e d valuesofthegrowthrates,bankingindustryvolatility,marketexc essreturns,and interactiont e r m V o l * i n f l a 1 B a n k v o l a t i l i t y a f f e c t n e g a t i v e l y oneconomicg r o w t h , thecoefficientsforvolisnegativeinbothmodelandsta tisticallysignificanta t the5%level,whichis-0.00373,-

0.0108,respectively.Thecoefficientsforothervar ia bl es are notstatisticallysignificant,exceptforlagged valuesofthe growthratev a r i a b l e forfullsampleof22economiesundersystemGMMmethods.

Column1 r e p o r t s t h e resultsf r o m t h e r e g r e s s i o n s ofg r o w t h r a t e s o n t h e l a g g e d valuesoft h e G D P g r o w t h r a t e , l a g g e d v a l u e s o f markete x c e s s r e t u r n ( Rm)a n d b a n k volatility(VOL).Bankvolatilityonlyhavestatisticsignificantinth e Africa,u n d e r GMM(DIF)andGMM(SYS)method.;thecoefficientofVolforthisgr oupsh a s anegativesign,whi ch is- 0 6 ,-

0 7 1 3 at 10%an d 1%s i g n i f i c an ce ,respectively.Inthe SouthAsiaandEastAsi a ,Bankvolatilityonlyhavestatistics i g n i f i c a n t a t 10%,thecoefficientis -

0.571 inGMM(SYS)method.Bank volatilityaffectnegativelyoneconomicgrowthundertwogroups.

Columns2–9ofthe tablespresentthe results fromsequentiallyaddingasinglec o u n t r y characteristic.Interactionvariabletoth especificationinthesemodels

Inc o l u m n 2 , t h e c o e f f i c i e n t oft h e i n t e r a c t i o n o f b a n k v o l a t i l i t y w i t h V o i c e a n d Accountabilitydonothavestatisticalsignificantinfivesubsamples.Th isresultissupportedbyrelatedliteraturereviewofMiyazawa,I.,&Zusman,E.

(2015),whop r o v e thatvoiceandaccountabilitydonothavethesignificanteffectsonc ountry’s performance.However,t h e coefficientforR m h av e s t at is t i cal significanceat 5 % a n d 1 % level, whichis-

0.0023forLowi n c o m e a n d l o w e r middleincomeeconomiesunderGMM(DIF )method,and-

Vol-2.566*rm-0.0113*Vol*political3.568*SouthAsiaandEastAsiaGMM(DIF)

Vol3 5 4 4 * * * Vol*political2.510***AfricaGMM(SYS)

Incolumn3,thecoefficientforvolhavestatisticalsignificanceat1%,5%and10%lev el , whichis-3 544for Africa under GMM(SYS)method, -

0 02 15 forUpper middleincomeeconomiesunderGMM(DIF)method,and- 2.566for SouthAsiaandEastAsiaunderGMM(DIF).

Bankv o l a t i l i t y affectn e g a t i v e l y o n e c o n o m i c g r o w t h Marketexcessr e t u r n s isn e g a t i v e l y associatedwithfutureeconomicgrowth,thecoefficientis- 0.0113,s i g n i f i c a n t at10%l ev el inS ou th AsiaandEastAsiaunder GMM(DIF )method.Particularly,PoliticalStabilityandAbsenceofViolenceweakenthen egativelinkbetweenmarketexcessreturnandeconomicgrowthinthisgroup.

The interaction between bank volatility and Political Stability, along with the Absence of Violence, shows statistical significance at the 10% level for South Asia and East Asia, and at the 1% level for Africa This positive interaction suggests that increased Political Stability and the Absence of Violence can mitigate the negative effects of bank volatility on economic growth Essentially, Political Stability may provide government guarantees that help alleviate the adverse impacts of banking industry shocks on future economic development Additionally, existing literature indicates that Political Stability and the Absence of Violence weaken the negative relationship between market excess returns and economic growth.

Vol*gov-0.196***UppermiddleincomeeconomiesGMM(DIF) vol-2.199**Vol*gov-

2.675**LowincomeandlowermiddleincomeeconomiesGMM(SYS) vol-1.074*AfricaGMM(DIF) rm0.0039*SouthAsiaandEastAsiaGMM(DIF) vol-1.764**AfricaGMM(SYS)

2 1 9 9 forLowincomea n d lowermiddlei n c o m e e c o n o m i e s un der GMM(SYS )method,- 1 0 7 4 and-

1.764forAfricaunder GMM(DIF)andGMM(SYS)method.

Bank volatility negatively impacts economic growth, while market excess returns are positively correlated with future economic growth, with a coefficient of 0.0039 significant at the 10% level in South Asia and East Asia using the GMM (DIF) method The interaction between bank volatility and government effectiveness (Vol * gov) shows statistical significance at the 1% and 5% levels in upper middle-income economies under GMM (DIF), as well as in South Asia and East Asia under GMM (SYS), and in low-income and lower middle-income economies under GMM (SYS), with coefficients of -0.196 and 2.289, respectively.

2 6 7 5 , respectively.T h i s i n t e r a c t i o n i s n e g a t i v e i n U p p e r middleincomeec onomiesandLowincomeandlowermiddleincomeeconomies,i n d i c a t i n g tha tthenegativeimpactofbankvolatilityoneconomicgrowthisexaggeratedbygreaterG overnmentEffectiveness.T h i s r e s u l t differfromr e l a t e d l i t e r a t u r e review Kaufmann(2013),Kong,T(2011),Miyazawa,I.,&Zusman,E.

( 2 0 1 5 ) w h o provethatgovernmenteffectivenessh a v e s u b s t a n t i v e l y a n d statistically s i g n i f i ca n t e f f e c t s o n p r o g r e s s c o u n t r i e s , b u t t h e sameresultofOmoteso,K , & I s h o l a Mobolaji,H

( 2 0 1 4 ) However,t h e i n t e r a c t i o n o f b a n k v o l a t i l i t y withGovernmen tEffectivenessispositivein SouthAsiaandEastAsia,i n d i c a t i n g thatthenegativ eimpactofbankvolatilityoneconomicgrowthiseasedb y greaterGovernmentEffecti veness.GovernmentEffectivenessmayr e l i e v e t h e negativei m p a c t o f b a n k i n g i n d u s t r y s h o c k s onfutureeconomicg r o w t h R e l a t e d l i t e r a t u r e researchhasdo cumentedthatGovernmentEffectivenessweakentheeffectofb an k in g i n d u s t r y vo l at i l it y oneconomicg r o w t h (Kaufmann,2013,K a u f m a n n , 2013,Kong,T,2011,M iyazawa,I.,&Zusman,E,2015).

Column5: vol0.304***Vol*regu_qua1.346***UppermiddleincomeeconomiesGMM( D I F

Vol1.362***Vol*regu_qua1.310***SouthAsiaandEastAsiaGMM(SYS)

304 forUppermiddleincomeeconomies underGMM(DIF)method,a n d 1.362forSouthAsiaandEastAsiaunderGMM(SYS)method.

Bankv o l a t i l i t y affectp o s i t i v e l y o n e c o n o m i c g r o w t h i n U p p e r m i d d l e i n c o m e economiesa n d S o u t h Asiaa n d E a s t A s i a Markete x c e s s r e t u r n s i s n e g a t i v e l y associatedwithfutureeconomicgrowth,thecoefficientis-

The GMM (SYS) method reveals a statistically significant interaction between bank volatility and Regulatory Quality, with coefficients of 1.346 in Upper middle-income economies and 1.310 in South Asia and East Asia This positive interaction indicates that improved Regulatory Quality mitigates the negative impact of bank volatility on economic growth in these regions Furthermore, Regulatory Quality may alleviate the adverse effects of banking industry shocks on economic performance Research by Kaufmann (2013) supports these findings, highlighting that Government Effectiveness also enhances the positive relationship between market excess returns and economic growth in South Asia and East Asia, as evidenced by the GMM (DIF) method.

Vol0.656**Vol*rule0.704**U p p e r middleincomeeconomiesGMM(DIF)

Vol1.61*rm0.0040*Vol*rule1.673***SouthAsiaandEastAsiaGMM(DIF)

Vol1.832*Vol*rule1.719***SouthAsiaandEastAsiaGMM(SYS)

Incolumn6,thecoefficientforvolhavestatisticalsignificanceat10%,5%,and1%le ve l, w h i c h i s , 0 6 5 6 forU p p e r middlei n c o m e e c o n o m i e s u n d e r GMM(DIF) method,-0.968forUppermiddleincomeeconomiesunderGMM(SYS)method, 1.61forSouthAsiaandEastAsiaunderGMM(DIF)method,-

0.874forAfricau n d e r GMM(SYS)method,and1.832forSouthAsiaandEastAsia

InUppermiddleincomeeconomies.Theresultsarenotrobusttodifferentestimationt echniques.H e r e , wechoosetheGMM(SYS)methodduetotheeffecto f bankingv olatilityoneconomicgrowthissuitabletotheliteraturereviews.Bankv o l a t i l i t y a f f e c t s n e g a t i v e l y o n economicg r o w t h , a n d Markete x c e s s r e t u r n s isn e g a t i v e l y associatedwithfutureeconomicgrowth.TheRuleofLawstrengthenthenegativel inkbetweenmarketexcessreturnandeconomicgrowth.Thecoefficiento f t h e i n t e r a c t i o n termbetweenbankvolatilitya n d Ruleof

Lawisnegative,i n d i c a t i n g t h a t R u l e ofL a w i n c r e a s e t h e n e g a t i v e i m p a c t ofb a n k v o l a t i l i t y o n economicg r o w t h i n T h i s n e g a t i v e i m p a c t iss t a t i s t i c a l l y s i g n i f i c a n t a t 5 % level.T h e coefficientis-

(2015),RuleofLaw mayr e l i e v e s t h e negativeimpactofb a n k i n g industrys h o c k s o n economicg r o w t h

Y S ) methodandunclearlyintheotheroneconomicgrowth,andMarketexcessretur nsi s negativelyassociatedwithfutureeconomicgrowthinGMM(DIF)method.

In South Asia and East Asia, the Generalized Method of Moments (GMM) analysis reveals consistent results across both methodologies, indicating that bank volatility positively influences economic growth Furthermore, market excess returns are positively correlated with future economic growth when using the GMM (DIF) method The Rule of Law enhances this positive relationship between market excess returns and economic growth The interaction term between bank volatility and the Rule of Law shows a positive coefficient, suggesting that the Rule of Law amplifies the beneficial effects of bank volatility on economic growth, with statistical significance at the 1% level The coefficients are reported as 1.673 and 1.719, aligning with findings from the literature, including Kaufmann (2013), Kong (2011), and Miyazawa & Zusman (2015), which suggest that the Rule of Law can have both substantively and statistically significant impacts on the progress of countries.

Vol-3.685*Vol*controlcur-3.601*LatinAmericaGMM(DIF)

Vol-1.330***Vol*controlcur-0.786**AfricaGMM(SYS)

Incolumn7 , t h e c o e f f i c i e n t forv o l h a v e s t a t i s t i c a l s i g n i f i c a n c e a t 1 0 % , a n d 1 % le ve l, whi ch is,-

3 6 8 5 forLatinAmericae c o n o m i e s u nd er GMM( DI F) method,3 8 6 2 forSout hAsiaandEastAsiaunder GMM(DIF)method,-

1.330forAfricau n d e r GMM(SYS)method,respectively.

BankvolatilityaffectpositivelyoneconomicgrowthinSouthAsiaandEastAsiai n G MM(DIF)method.However,thiseffectis negatively i n LatinAmericain GMM(

D I F ) method,a n d i n Africag r o u p Marketexcessr e t u r n s i s n e g a t i v e l y associ atedwithfutureeconomicgrowthinUppermiddleincomeeconomies,Africa,LatinA merica,exceptforLowincomeandl o w e r middleincomeeconomies,thisrelationi ssignificantat10%.Thecoefficientoftheinteractionofb a n k volatilitywithContr olofCorruption(Vol*controlcur)h a v e statisticals i g n i f i c a n t at5%,and10%in

(DIF)method,Africaun d er GMM(SYS)method,whichis-3.601,-

0.786,respectively.Thisinteractioni s negativeinthesegroup,indicatingthattheneg ativeimpactofbankvolatilityoneconomicgrowthisexaggeratedbygreaterControlofC orruption.

Thisresultdifferfromrelatedliteraturereview(Kaufmann,2013),anditissupportedby Miyazawa,I , & Zusman,E , 2 0 1 5 , w h o r e p o r t e d t h a t c o n t r o l o f c o r r u p t i o n d o n o t h a v e t h e significanteffectsoncountry’sperformance.

Vol-0.209***LowincomeandlowermiddleincomeeconomiesGMM(DIF) Rm0.0068*AfricaGMM(DIF)

Vol-1.237*r m 0.008*Vol*infla11.311*LatinAmericaGMM(DIF)Vol-

8.534**rm-0.0029*Vol*infla19.565**A f r i c a GMM(SYS)Rm-

Incolumn8,thecoefficientforvolhavestatisticalsignificanceat10%,5%,and1%lev el ,wh ichis,-1.237for LatinAmericaunderGMM(DIF)method,-

0.209forLowincomeandl o w e r middleincomee c o n o m i e s underGMM(DIF) method,respectively.

Bank volatility negatively impacts economic growth in specific regions, particularly in Africa and South Asia when analyzed using the GMM (SYS) method Conversely, market excess returns show a positive correlation with future economic growth in South Asia, East Asia, and Latin America through the GMM (DIF) method Notably, the interaction between bank volatility and low inflation levels indicates that lower inflation mitigates the adverse effects of bank volatility on economic growth, with significant coefficients at the 10% and 5% levels This positive influence is primarily observed in Latin America and Africa, with coefficients of 1.311 and 9.565, respectively These findings align with the research of Bruno and Easterly, highlighting the complex relationship between bank volatility, inflation, and economic performance.

0.322***Rm0.00543***Vol*infla20.522***Lowincomeandlowermiddlei n c o m e economiesGMM(DIF)

Rm-0.0003*UppermiddleincomeeconomiesGMM(SYS)Vol*infla2-

0 3 2 2 forLowincomea n d lowermiddlei n c o m e e c o n o m i e s u n d e r GMM(D IF)method.

Bank volatility negatively impacts economic growth, while market excess returns are positively associated with future economic growth in certain regions However, in upper middle-income economies and Latin America, market excess returns show a negative correlation with future economic growth The interaction between bank volatility and high inflation levels indicates that high inflation mitigates the adverse effects of bank volatility on future economic growth, with this positive impact being statistically significant at the 1% level This finding, driven primarily by data from low-income and lower middle-income economies, reveals a coefficient of 0.522, contrasting with previous research by Bruno and Easterly (1998) and Andrés and Hernando.

Incontrast,thecoefficientofthisinteractiontermisnegative ,indicatingthathighinfla tionlevel increas e t h e negativeimpactofb ankvol at ility onfutureeconomicg r o w t h Thisnegativeimpactisstatisticallysignificantat5%,and1%level,andismainl yd r i v e n byt h e d a t a f r o m t h e S o u t h A s i a a n d E a s t A s i a i n GMM( D I F ) e stimations,LatinAmericaunderGMM(DIF)estimationsandAfricaunderGMM( S Y

S ) estimations.Thecoefficientis-1.145,-0.417,-46.07,respectively.Thisis animportantfindingcomplementarytoBruno,M.,&Easterly,W.(1998),andAndrés

Vol-1.254*r m -0.0078*Vol*credit0.0353***AfricaGMM(DIF)

Incolumn10,thecoefficient forvolhavestatisticalsignificance at10%,and5

1 4 5 6 forA f r i c a economiesu n d e r G M M ( D I F ) a n d GMM( S YS ) method,0 4 9

0 forU p p e r m i d d l e i n co me e c o n o m i e s u n d e r GM M (SYS)method.

InAfricagroupunderGMM(DIF)estimations,Bankvolatilityaffectsnegatively o n economicgrowth, andMarketexcessreturnsis negativelyassociatedwithfutureeconomicgrowthinthisgroup.Domesticcredittoprivat esectorweakenthenegativelinkbetweenmarketexcessreturnandeconomicgrowth Thecoefficiento f theinteractiontermbetweenbankvolatilityanddomesticcredittopriv atesector( V o l * c r e d i t ) i s p o s i t i v e ,i n d i ca t i n g t h at domesticcr ed it t o p r i v a t e sectordecreaset h e negativeimpactofbankvolatilityoneconomicgrowth.Thispos itiveimpactiss t a t i s t i c a l l y significantat1%level.Thecoefficientis0.0353.T hisisanimportantfindingdifferentfroml i t e r a t u r e r e v i e w o f M o s h i r i a n & Wu( 2 0

( 1 9 9 9 ) , whoindicatethattheeffectofthethreeindicatorsoffinancialdevelopmento n t helinkbetweenbankvolatilityandfutureeconomicgrowthisnotclear.But,theresulti s suppo rtedbyK i n g & L e v i n e , ( 1 9 9 3 ) ; L e v i n e e t a l , ( 2 0 0 0 ) ; M c K i n n o n ,

Incontrast,inUppermiddleincomeeconomies groupunderGMM(SYS)estimations , Bankv o l a t i l i t y affectsp o s i t i v e l y o n economicg r o w t h , a n d Marketexcessre turnsisnegativelyassociatedwithfutureeconomicgrowthinthisgroup.Domestic credit toprivatesectorstrengthenthen e g a t i v e l i n k betweenmarketexcessreturn andeconomicgrowth The coefficient oftheinteractiontermbetweenb a n k volatilityanddomesticcredittoprivatesector(Vol

*credit)isnegative,i n d i c a t i n g thatdomesticcredittoprivatesectorincreasethenegati veimpactofbankv o l a ti li t y oneconomicgrowth.Thisnegativeimpactisstatisticallysignif icantat5%level.Thecoefficientis-

&Wu(2012),andKing&Levine,(1993);Levineetal , (20 00) ; McKinnon,

Vol-6.329**Vol*liquid0.147**LatinAmericaGMM(DIF)

6.329for LatinAmericau n d e r GMM(DIF)method,and-

0 5 9 4 forLowincomeandlowermiddleincomeeconomies underGMM(SYS)met hod.

InLowincomeandlowermiddleincomegroup,andLatinAmericagroupunderGMM ( DIF) est imat ions , Bankv o l a t i l i t y affects n ega t iv el y o n economicgro wth.

Liabilitiesdecreaset h e negativei m p a c t ofb a n k v o l a t i l i t y o n e c o n o m i c g r o w t h T h i s p o s i t i v e impactiss t a t i s t i c a l l y significanta t 5 % level.T h e coeff icienti s 0 0 7 5 7 , a n d 0 1 4 7 , respectively.ThisisanimportantfindingdifferentfromliteraturereviewofMoshiri an& W u (2012) &Her nando,

(1999) ,w h o indi cateth at Th e e f f ec t ofthe threeindicators offinanciald ev elop ment onthe linkbetween b ank v o l a t i l i t y andfutureeconomicgrowthisnot clear.But,theresultissupportedbyKing&Levine,( 1 9 9 3 ) ; Levineetal.,

Incontrast,in UppermiddleincomeeconomiesgroupunderGMM(SYS)estim ations,B a n k v o l a t i l i t y a f f e c t s p o s i t i v e l y o n e c o n o m i c g r o w t h , a n d Marketexcessreturnsisnegativelyassociatedwithfutureeconomicgrowthinthis group.L i q u i d -

L i a b i l i t i e s strengthent h e negativel i n k betweenmarkete x c e s s r e t u r n a n d eco nomicgro wth T h e coefficiento f t h e i nt er act i o n t e r mb et we en b an k v o l a t i l i t y a n d L i q u i d -

&Wu(2012),andK i n g &Levine,(1993);Levineetal.,(2000);McKinnon,(1973);Shaw, (1973).

InSouthAsiaandEastAsiagroupunderGMM(DIF)method.Thecoefficientoftheinte ractiontermbetweenbankvolatilityandLiquid-

Liabilities(Vol*liquid)alsoi s negativeandsignificantat10%level.

Vol0 2 6 7 * * Rm-0.0009*Vol*stock_cap-

Vol0 4 9 3 * * r m -0.0074*Vol*stock_cap- 0 0 1 0 8 * * AfricaGMM(DIF)

Vol- 0 5 5 4 * * Vol*stock_cap0.01**AfricaGMM(SYS)

0 4 1 8 * * Rm0.00149***Vol*stock_cap0 0 1 3 5 * S o u t h AsiaandEastAsiaGMM (DIF)

Vol- 0 1 2 8 * * * Vol*stock_cap0.00564***LatinAmericaGMM(DIF)

Vol-0.146***Vol*stock_cap0 0 0 3 9 * * * LatinAmericaGMM(SYS)

% l e v e l , which i s , 0 2 6 7 f o r U p p e r middlein co me economiesund er GMM( S

In upper middle-income economies analyzed under GMM (SYS) estimations and the Latin America group under GMM (DIF) estimations, bank volatility has a positive effect on economic growth Conversely, market excess returns are negatively associated with future economic growth in this group The strength of stock market capitalizations exacerbates the negative relationship between market excess returns and economic growth The interaction term between bank volatility and stock market capitalization (Vol * stock_cap) shows a negative coefficient, suggesting that stock market capitalization diminishes the positive influence of bank volatility on economic growth, with this negative impact being statistically significant at the 5% level.

&Wu(2012),whoindicatethatTheeffectofthethreeindicatorsoffinancialdevelop mentonthelinkbetweenb a n k volatilityandfutureeconomicgrowthisnotclear, andPagano,(1993);Jun,( 2 0 1 2 ) ; Levine&Zervos,(1996);Ndlovu,

InAfricae c o n o m i e s u n d e r b o t h method.T h e resultsa r e n o t r o b u s t t o differe ntestimationtechniques.Here,wechosetheGMM(SYS)methodduetotheeffectofb a n k i n g v o l a t i l i t y o n economicg r o w t h i s s u i t a b l e t o t h e l i t e r a t u r e r e v i e w s T h e coefficientoftheinteractiontermbetweenbank volatilityands t o ck mar ketcapitalizationisp o s i t i v e ,i n d i c a t i n g t h a t s t o c k marketc a p i t a l i z a t i o n dec reaset h e negativeimpactofbank volatilityoneconomicgrowth.Thisisanimportantfindingcomplementfromliteraturerevi ewofMoshirian&Wu(2012)&Hernando,

(1999),w h o indicatethatTheeffectofthethreeindicatorsoffinancialdevelopmentont hel in k betweenbank volatilityandfutureeconomicgrowthisnotclear.But,theresult isdifferent fromP ag an o , (1993); Jun,( 2 0 1 2 ) ; Levine & Zervos,

In Latin America, bank volatility negatively impacts economic growth, while in South Asia and East Asia, market excess returns are positively associated with future economic growth Stock market capitalization enhances the positive relationship between market excess returns and economic growth The interaction term between bank volatility and stock market capitalization shows a positive coefficient, indicating that increased stock market capitalization mitigates the negative effects of bank volatility on economic growth This positive impact is statistically significant at the 10% and 1% levels, with coefficients of 0.0135 for South Asia and East Asia, and 0.00564 and 0.0039 for Latin America These findings contrast with the literature reviewed by Moshirian & Wu (2012) and are supported by Pagano (1993) and Jun.

Allmodelsareinterpretedabove,theyaretestforvalidandrelevantinstrumentsincaseof existingendogeneity.Arellano-BondtestSargan-

Bondtestisperformedforendogeneitytestofoneormoree n d o g e n o u s r e g r e s s o r s w i t h t h e p u r p o s e t o u s e t h e e n d o g o p t i o n I n t h i s situation,thenu ll- hypothesisstatingthatnoautocorrelationinthemodelorAR(1)i n firstdifferencesis existed.Itmeanthatthespecificedendogenousregressorscanb e h a n d l e d a s e x o g e n o u s I n t h e s e c o n d k i n d o f t e s t , then u l l - h y p o t h e s i s o f Sargan-

Hansent e s t s t a t i n g t h a t t h e equationi s u n d e r i d e n t i f i e d ortheinstrumentv a r i a b l e areinvalidandirrelevant,theyarealsocorrelatedwitherrorterm.Tobem oreclearly,i f t h e p - v a l u e oft h e Sargan-

Hansentesti s h i g h e r t h a n t h e conventionaltenpercent,wegetvalidandrelevantinstru mentvariables.Theresultsi n t e r p r e t e d isillustratedindetailinappendix1,2,3,4,5,6as well.

Policyimplicationandconclusion

Inthisresearch,wekeepinvestigatingtherelationshipbetweenbankingvolatilit ya n d economicgrowthindetailwaysafterexaminingcarefullythestudiesofMoshiria n&Wu, (2012);Lin& Huang,

(2012)b y investigatingwhetherthere isa n y relationshipbetweenbanking i ndustryvolatilityandeconomicgrowth.UsingGMMt e c h n i q u e s fordynamic paneldatat o analyzeo n e m a i n g r o u p a n d f i v e subsamplesind et ai l: a l l 22ma rkets,a n d 11up p er middlein co me g ro up , 11lo wi n c o m e andlowermiddleincom egroup,Sub-SaharanAfricagroup, SouthAsiaandEastAsiagroup,LatinAmericagroup.

The initial survey across six geographic regions reveals a slight correlation between GDP growth rates and banking volatility A detailed analysis indicates that bank volatility negatively impacts economic growth in all 22 markets, regardless of the presence of interaction terms In five subsamples that include interaction terms, the effect can vary between negative and positive, depending on the specific model and methods used Additionally, much of the predictive power of banking volatility is influenced by various country characteristics and financial development, particularly the levels of high or low inflation within each subsample.

Ast h e firstr e s e a r c h t o l i n k g r o w t h w i t h t h e i n t e r a c t i o n t e r m o f bankingv o l a t i l i t y andSixWorldwideGovernanceIndicators,andtwodummyv ariable.Wemakesomesignificantcontributionstotheliteratureoffinanceandgrowth.

Firstly,weprovideforthefirsttime,theinteractionofbankvolatilitywithVoice a n d Accountabilitydonothaveeffectoneconomicgrowthforoneall22markets,an dfivesubsamples.

Thirdly,t h e interactiono f b a n k v o l a t i l i t y w i t h GovernmentEffectivenessh a v e effectnegatively oneconomicgrowthinUppermiddleincomeeconomiesandlowi n c o m e andlowermid dleincomeeconomies.However,thisinteractionhaveeffectp o s i t i v e l y one c o n o m i c g r o w t h i n S o u t h A s i a a n d E a s t A s i a , i n d i c a t i n g t h a t t h e negativeimpa ct ofbankvolatility oneconomicgrowth is eased b y greaterG o v e r n m e n t Effe ctiveness.

Fourthly,t h e i n t e r a c t i o n o f b a n k v o l a t i l i t y w i t h R e g u l a t o r y Q u a l i t y i s posi tivei n U p p e r middleincomeeconomiesandLowincomeand SouthAsiaand East Asiaeconomies,i n d i c a t i n g t h a t t h e negativei m p a c t o f b a n k v o l a t i l i t y oneconomicg r o w t h isease dbygreaterRegulatoryQuality.

Fifthly,theinteractiont er mbetweenbankvolatilityand Ru l e ofLawh av e eff ectn e g a t i v e l y oneconomicgrowthinUppermiddleincomeeconomies,indicatingth atRuleofLawincreasethenegativeimpactofbankvolatilityoneconomicgrowth.

T h i s interactionispositive inSouthAsiaandEastAsia,indicatingthatRuleofLawincreasethepositiveimpact ofban kvolatilityoneconomicgrowthinSouthAsiaa n d EastAsia.

Sixthly,t h e interactiono f b a n k v o l a t i l i t y w i t h ControlofC o r r u p t i o n h a v e effe ctn e g a t i v e l y oneconomicgrowthnegativelyinLatinAmerica,andinAf rica,in dica ti ng thatthenegativeimpactofbankvolatilityoneconomicgrowthisexa ggeratedbygreaterControlofCorruption.

Seventhly,t h e i n t e r a c t i o n t e r m b e t w e e n b a n k v o l a t i l i t y a n d t h e d u m m y v a r i a b l e , low inflationlevelhaveeffectpositivelyoneconomicgrowth,indicatin gthatlowinflationleveldecreasethenegativeimpact ofbankvolatilityonfutu reeconomicg r o w t h inLatinAmericaandinAfrica.

Eighthly,theinteractiontermbetweenbankvolatilityandthedummyvariable,highinflation l e v e l h a v e effectp o s i t i v e l y o n economicg r o w t h , i n d i c a t i n g t h a t h i g h inflationleveldecreasethenegativeimpactofbankvolatilityonfutureeconomicinL o w incomeandlowermiddleincomeeconomies.Incontrast,theinteractiontermi s n egative,inSouthAsiaandEastAsia,LatinAmericaandAfrica

- Theinteractiontermbetweenbankvolatilityanddomesticcredittoprivatehaveef fectp o s i t i v e l y o n economicg r o w t h , i n d i c a t i n g t h a t domesticc r e d i t t o p r i v a t e sectordecreasethenegativeimpactofbankvolatilityoneconomicgrowthInAfrica g ro up Whereas,t h i s interactiont e r m i s negativei n U p p e r middlei n c o m e econo mies.

LiabilitiesdecreasethenegativeimpactofbankvolatilityoneconomicgrowthInLo wincomeandlower middleincomegroup.I ncontrast,inUpp er middleincomee conomies,th is interactiontermhaveeffectnegativelyoneconomicgrowth

- Theinteractiontermbetweenbankvolatilityandthestockmarketcapitalizationh a v e effectnegativelyoneconomicgrowth,indicatingthatstockmarketcapitalizationdecr easethepositiveimpactofbankvolatilityoneconomicgrowth inUppermiddleincomeeconomies.H o w e v e r , thisinteractiontermispositivein Africa,LatinAmerica,andinSouthAsiaandEastAsia

Inshort,t h e r e i s relationshipbetweenb a n k i n g industryv o l a t i l i t y a n d economi cg r o w t h i n samplesd i v i d e d i n t o incomec r i t e r i a a n d g e o g r a p h i c a l r e g i o n c r i t e r i a , eveninthepresenceofmarketexcessreturns,andtherelationshipbetweenbanki ngvo l a ti li ty andeconomicgrowthisaffectedbythecountrycharacteristicandfin ancialdevelopmentwhentheinteractiontermshavestatisticalsignificant.ExceptforV o i c e a n d A c c o u n t a b i l i t y h a v i n g n o e f f e c t Someresearchp a p e r s o f Fama(

1 9 8 1 , 1990)andSchwert(1990)haveprovedthattheeffectoftheuncertaintyof banking industryoneconomicgrowthisuncorrelatedwiththeeffectofthemarketst ockreturningeneraloneconomicgrowth.Hence,ourresultsismoreoneevidencefort h e r e l a t i o n s h i p b e t w e e n t h e s t o c k r e t u r n s ofb a n k a n d economicg r o w t h

Limitationoftheresearch

There are several limitations in our research that should be acknowledged Firstly, the issue of instrument variable selection poses a challenge; while many econometric analyses highlight the advantages of using internal instruments, they may not always be the most effective choice Additionally, there could be other variables influencing economic growth beyond internal instruments Another drawback of internal instrument variables is their ability to address endogeneity issues, but this capability is limited Consequently, some researchers may need to seek alternative instrument variables to bridge this gap.

Secondly,thefinancialshockineachgrouporeachcountrymaycauseveryhighinfl ationr a t e i n e c o n o m i e s surveyeda f t e r t h a t , a n d h i g h interestr a t e simultaneously ,t h i s effecto u r resultsw h e n i n s t r u m e n t v a r i a b l e s i s t a k i n g i n t h i s year.

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Bankingind ustry vola tilityandec onomicgro wth

- Laggedmarketex cessreturn,lagged bankvolatility,G overnmentowner shipofbanks,Insi detradinglaw,Ba nkingcrisis,Bank accountingdiscl osurestandards, Privatecredit,Liq uidliabilities,Co mmercial- centralbank

-18 develope dand18e merging market,c oversthe periodfro m1973 to 2006.

- Bankingindustryvolatilityreflectsonl o t s o f informationaboutt h e healtho f futureec onomic.Itrepresentf o r thes t a b i l i t y o f t h e performanceo f t h e bank.T h e country - specificandbankinginstitutionalcharacter isticsaffectt h e s e relationships.

- Thereis negativerelationshipbetweenbankvolatilitya ndfutureeconomicgrowth.Banking crisisandbankingaccountingstandards maketherelationshipbetweenbankexcessretu rnsandfutureeconomicgrowthto bepositiveandmaketherelationshipofbankv olatilityandfutureeconomicgrowthnegative moretremendously However,government ownershipreducetheserelation.Incontrast,the enforcementof theinsidertradinglawincreasethefirstrelation p o s i t i v e l y anddeclinethesecondrelationn egatively.Thethreeindicators offinancialdevelopmentstrengthenthefirstrel ationandimpactambiguouslyon thesecond.

Banking Industry, difference-in- Our -B a n k i n g i n d u s t r y volatility,measuredas industry Country difference benchmar thestandarddeviationofthegrowthof volatilityan dgrowth

ExternalFinanc e,BankDevelop ment,Vo l a t i l i t y ofBankDevel opment,S t o c k MarketDevelop ment, frameworkofRaj anandZingales(1 998)t o t h e cross- country, cross- industrydatadev elopedbyCiccon eand kresultfo rthe1980 –

- Develope d privatecredit,effectont h e growthofindustrie snegativelyt h a t arem o r e e x t e r n a l l y fin anciallydependent

- However,thedetrimentalgrowtheffectofbank ings e c t o r v o l a t i l i t y disappearswhent h e sampleisrestrictedtotherelativelyplacid

The volatility of economically integrated countries, as highlighted by Papaioannou (2009), is often accompanied by unpredictable development and financial crises that disrupt the banking sector Our findings suggest that in a more economically integrated world, the stability of financial development is crucial for long-term growth, as supported by Beck et al (2010).

Bankingsec tordevelop men t a nd interest ratevolatilit yinemergin geconomies

– Independentvaria ble:interestratevo latility,theratioof liquidliabilitiesof theﬁnancialsecto rasapercentageof GDPofcountry,re alincome,

- Error- correctionm o de li ng pro posedbyPesa ranetal., (2001)

- Theeffecto f interestr a t e u n c e r t a i n t y o n thehealtho f bankings e c t o r i s clearand significanti n l o n g r u n ands h o r t r u n , particularlythis isvery stronginthesh ortone.Theircointegrationrelationshipiscert ain.

-Un- wellfinancialenvironmentharmsthebanki ngs e c t o r d e v e l o p m e n t Impacto f in terestratevolatilityoncommercialbanksm a k e financialsectordeterioratedandeconomic growthstagnant in longrun.

BankingSe ctorDevelo pment and Econo micGrowth inIndia/Tri pathy,S.,

- Independentvari able:domestic credit,moneya ndquasi- money,totalAss etsandLiabilities three financialecono metrict o o l s : u n i t r o o t , cointegrat ionandcausality betweenIndianb ankingsectorde velopmentande conomicgrowth

Fuller(ADF)test asboth thecointegratio nandcausalityte chniques

- IntheIndi anecono my,betwe en1960a nd 2011,

- Bankingdatastream i s intergratedtoeco nomicgrowthdatastream,thereforet h e y are cointergratingandexist l on g runequilibriu m

- Economicgrowthhasbidirectionalrelation shipw i t h Domesticcredit,m o n e y andqua si- money.Ithasunidirectionalcausalitywithpriv atesectorcredit,totalassetsandliabilities.

- Domesticcredit,moneyandquasi- money,privatesectorcredit,totalassetsandli abilitiesimprovetheeconomy.

StockMark etandEcon omicGrowt hinGhana/ marketcapitaliza tion,bankcompet ition,Domestic bankcreditasper cento f GDP

Shortrunanalysi sis donewithin theframeworko fGrangerCausa lity whilethe longrun

Financialdevelopmentisveryimportanttob o o s t theeconomy.Bankingcompetitionands t o c k marketdevelopmentm a k e s m o o t h i n g operation,improvingservicesint h e banks.Asaresult,functionoffinancialsystemisenh anced.Thisaffectseconomicgrowthpositivel y.

M.(2011). analysisemploy edAutoregressiv eDistributedLag (ARDL)andDyn amic

Institutions, p o l i c y and bankingsect ordevelopm ent : a reassessm ent/Filippid is,I.,&Katr akilidis

- Independentvari able:Institutions (economic,polit icalandsocial),fi nancialopennes s,tradeopenness

Inflation,GDPgr owth,governmen tpolicy andaban kingcrisisdummy ,countryandtime period

- GMMestimatio nprocedurestac kstheequationin levelswiththos e infirst- differencesande stimatesthesyst emwithlaggeddi fferencesofth e t ime- varyingvariable sasadditionalins trumentsfort h e equationinlevel s

- Thedatas etconsists o f a panelof observati ons foragrou pof80 countriesf o rtheperi od1985 – 2007

- Thesamp lecountri esaredivi dedinto differenti ncome g roupsbas edonthe WorldBa nkclassif icat i o n

Theimportanceo fe co no mi c institutionasb ackgroundofbankingsectordevelopmenti s a mbiguousi n t h e w h o l e periodo f t i m e esp ecially intheemergingeconomies.Whereas, politicalinstitutionshavecloserelationshipw i t h bankingdevelopments i g n i f i c a n t l y i n developedcountrieso n l y , andsociali n s t i t u t i o n s i n l o w - a n d m i d d l e - i nc om e countries.Fortheeconomicinstit utions, t h e marginaleffecto f government dimensioni n richcountriesi s betterin poorcountries.

Tradeopennessandfinancialdevelopmenth avestrongandpositiverelationshipinthefirs tphaseofeconomic development,whilecapitalinflowshavethesa meimpactbutint h e laterp h a s e Capitalacc ounto p e n n e s s mightb e r i s k y f o r i n v e s t o r s i n emergingeconomies.

- Governmentpoliciesi n c l u d i n g p u b l i c organizationsandenterprises,p u b l i c inve stment,p u b l i c consumption,governments ubsidiesandtaxpoliciesist h e importantfacto rso f b a n k i n g developmentduringtheecon omicaldevelopingtime.

The banking industry's stock returns can be influenced by market excess returns, as demonstrated by a dynamic model that predicts future economic growth This relationship is independent of traditional growth metrics and highlights the interaction between market stock returns and economic performance Utilizing a fixed-effect and random-effect approach, the study employs a Generalized Method of Moments (GMM) to analyze these dynamics, emphasizing the significance of bank stock returns in forecasting economic trends.

R.A.,Moshi indicator covers capturedbyc o u n t r y - s p e c i f i c andb a n k i n g rian,F., variablesfor the institutionalcharacteristics,b a n k i n g c r i s i s

-Country-specific and institutionalcharacteristics affectgrowth through bankingindustrystock returns.

AnEmpiric alExplorati onofIdiosy ncratic Risk

- Themarketwid ereturn,anindu stry specificresi dual,andfirmspe cificresidual

- Weconstructti me seriesofvol atilitymeasure sof thethreecompon entsfora typicalf irm.

- Onecount ry,monthl y,overthe periodfro m1962 to 1997

- Therei s s t r o n g evidenceo f p o s i t i v e specifiedtrendi n t h e sectoro f firm- levelvolatility.T h e r e isn o similartrendi n i n d u s t r y ormarketvolatility.

- Thetrendincreaseinthesectorvolatilityrel ativetomarketvolatilitymeansthatthecor relationsamongindividualstockreturnshaver educedover thepastyears,R 2 for atypicalsto ckhas reduced.

- Firm- levelvolatilitybothaccountsforthegreatests h a r e o f totalf i r m volatility,andf o r thegre atestshareofthemovementsovert i m e i n tot alfirmvolatility.Marketv o l a t i l i t y s h o w s t h e greatestvariationa n d i t t e n d s t o leadt h e o t h e r c o m p o n e n t s ofvolatility.

Theimpact -Dependent -Descriptive -Thefull -Thestate-ownedbanksnormallyrunless ofstate variable:the statistics, sampleof profitably,heldlesscorecapital,and ownership ratioof Pooled 16 containedgreatercreditr i s k t h a n p r i v a t e on operatingpre-tax regressions countries, ownedbanks - performanc e difference s

- ownedvers usstate- ownedbank s: cashflowreturns ,corecapital/ assets,allowanc eforloan losses/ loans,nonperfor mingloans/ loans,personnel expenses/ loans,governme nt

Two- stageleastsquare s. including 5 countriest h at exper iencedext remecrisi s.

- Thereisthedeclineinthecashflowreturns, c o r e capital,a n d creditq u a l i t y o f state- ownedbankswassignificantlygreatert h a n t h a t o f p r i v a t e - o w n e d banksafterAsianfinancialcrisistime

L,Khaksari, securities/ assets,assetgrow thrate.

- Independentvaria ble:stateownershi pasd u m m y varia ble, withmini malgover nmentinv olvement in thebanki ngsystem ,

T h e countriess u f f e r i n g t h e m o s t serious Asiancrisiskeptcleardifferencest o t h e othe rs.Aftert h e c r i s i s t i m e , t h e s t a t e - ownedbanksshortenedthegapwithprivate - ownedonesinthesesectors.Furthermore,s t a t e -

H,H.,2010 foreignownershi pasd u m m y vari able,yeardummi es,countrydumm y,country dummiesandcou ntry- yeard u m m i e s , Size(assets/co untryGDP), andthe11 countries w it h hea vygovern mentinvo lvementi n thebanki ngsystem overthep eriodfro m1989 to 2004. morefinancetogovernment.

- Thestate- ownedb a n k s andt h e p r i v a t e - ownedonesalmosthavethesameperformance afterthecrisistimeasaresulto f increasingglo balizationofcompetition.

The study examines the impact of equity markets on economic growth in MENA countries, utilizing panel data and Generalized Method of Moments (GMM) estimation techniques It highlights the significance of stock market capitalization, turnover, and indices, alongside the role of bank credit to the private sector and liquidity in driving economic development The empirical evidence suggests that a robust equity market contributes positively to growth, underscoring the interconnectedness of financial markets and economic performance in the region.

- Financialdevelopmentkeepunimportantrolef oreconomicgrowth.Thisweakrelationshipis harmfulfor theeconomy. fromtheME

&Ghazouan i,S (2007) liabilities,bankde velopmenti n d e x , initialincome p ercapita,tradeope nness,FDI,black market

- Thereisnegativerelationshipbetweenbankd evelopmentandeconomicgrowthinconditio nofcontrollingforthestockmarketdevelopm ents i n c e u n w e l l - functioningfinancialsystemandpoorinstituti onalenvironment premium, inflationrate, government consumption,oil price,political turmoil, financialcrisis, legalsystem, timedummies,

Largercrise scostmore:I mpactofban kingsectori nstability on outputgro wth/

Valueofcreditatt imet;percentage changeinconsu mer;

Pricesbetweenp eriods,GDP Second,weuse

-Event- studyapproacha ndmulti- equationmodel s,andappliesme asuresofbankin gCrisesconstruc tedfrombanking

Observed in 14- yearwind owfroms evenyears before

- Aftercontrollingfortheimpactofrecessio nsonthesizeofc r i s e s , b a n k i n g crisescauseoutput growth to slow down

- Growthofcreditcauseareductioninaccum ulatedf o u r - y e a r GDPgrowthbyaround2 percentagep o i n t s Asigniﬁcantrelationshipbetweencredi tandm o n e y dynamics,andoutputgrowths uggeststhatt h e creditandm o n e t a r y trans

(2010) changesinaggreg atedepositsafterc ontrollingforthe impactofinterest paid onThesedeposits tomeasuretheex tent ofbankingcrises

;interestrate ;the differencebetwe ennewdepositin ﬂowsanddeposit

Thethirdmeasure ofbankingcrises sectoraggregates ,employinglarge datasetof over100banking crises

- Wedealwiththei dentiﬁcationpro blembydeﬁning assetoftheecono metricinstrumen ts. the Crisis toseveny earsafter

- Database ofsystemi candbord erline ban kingcrises ,whichinc ludes16 6 crisiseven tsfromove r100 countries. channelsareresponsiblefor transferringba nkingcrisesto therealeconomy

- Crisesare costlyfor economies,atleastintheshort-term

- Thesizeofthecrisisthatmattersforeconom icgrowth.Lowercreditandm o n e y growthdu ringcrisescauseGDPgrowthtodecline.

The study examines the impact of corporate spending on financial profitability, focusing on firms facing financial constraints Evidence suggests that constrained firms, as indicated by CFO surveys, are more likely to implement significant cuts in technology and capital spending During the fourth quarter of 2008, these firms experienced heightened cash burn and increased reliance on credit lines due to fears of restricted access from banks Additionally, they sold more assets to sustain operations, highlighting the challenges posed by difficult access to external borrowing The findings underscore the importance of understanding financial constraints in corporate decision-making.

Graham,J Changein assess them to fail to catch investment

R., & technology whether opportunities,w i t h 86%o f constrained Harvey,C expenditures,% their U.S,Ins h o r t , t h e deterioratedf u n c t i o n o f

R.(2010) Changein firmsare bankingsystemcausenegativeeffecton capital credit theflowofeconomicactivities.Ourresults expenditures,% constrain alsoholdinEuropeandAsia,andinmany

Changein edduring casesarestrongerinthoseeconomies.Our marketing theglobal analysisaddstotheportfolioofapproaches expenditures,% financial andknowledgeaboutt h e impacto f credit

Hansentestexcludinggroup: chi2(640) = 17.54 Prob> chi2 = 1.000 Difference(nullH =exogenous):chi2(1) = 3.13 Prob> chi2 = 0.077

Prob>chi2 = 0.000 max = 45 growth Coef.

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.473115 0941103 -5.03 0.000 -.6575679 -.2886622 vol -.0045819 0041092 -1.12 0.265 -.0126358 0034721 rm -.0000519 0005767 -0.09 0.928 -.0011822 0010784

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)L(1/46). (lagGvol)

Arellano-BondtestforAR(1)infirstdifferences:z= -2.04 Pr>z0.041Arellano- BondtestforAR(2)infirstdifferences:z= -1.12 Pr>z= 0.264

Sargantestofoverid.restrictions:chi2(641) d1.51 Prob>chi20.487(Notrobust,butnotweakenedbymanyinstruments.)

Hansentestofoverid.restrictions:chi2(641) = 20.67 Prob>chi21.000(Robust,butweakenedbymanyinstruments.)

Difference-in-Hansentestsofexogeneityofinstrumentsubsets:iv(L.rm)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4981206 1182796 -4.21 0.000 -.7299444 -.2662968 vol -.0036054 0101588 -0.35 0.723 -.0235163 0163055 rm -.0005644 0005939 -0.95 0.342 -.0017284 0005995 volvoice 0230656 0363366 0.63 0.526 -.0481528 094284

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)L(1/46). (lagGL2.vol)

Sargantestofoverid.restrictions:chi2(556) T1.09 Prob>chi20.667(Notrobust,butnotweakenedbymanyinstruments.)

Hansentestexcludinggroup: chi2(554) = 16.77 Prob>chi21.000Difference(nullH =exogenous):chi2(2)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.5283767 0940964 -5.62 0.000 -.7128022 -.3439512 vol -.0235214 0512679 -0.46 0.646 -.1240047 0769618 rm -.0003618 0005393 -0.67 0.502 -.0014188 0006952 volpolitical 0218434 062832 0.35 0.728 -.101305 1449918

Sargantestofoverid.restrictions:chi2(557) S9.02 Prob>chi20.700(Notrobust,butnotweakenedbymanyinstruments.)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.5301829 1306091 -4.06 0.000 -.7861721 -.2741938 vol -.0149998 0361412 -0.42 0.678 -.0858353 0558357 rm -.0003253 001076 -0.30 0.762 -.0024342 0017835 volgov -.0109634 0528735 -0.21 0.836 -.1145936 0926669

Hansentestexcludinggroup: chi2(546) = 19.07 Prob> chi2 = 1.000 Difference(nullH =exogenous):chi2(2) = -1.63 Prob> chi2 = 1.000

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.533259 0914682 -5.83 0.000 -.7125333 -.3539846 vol -.0152468 0190613 -0.80 0.424 -.0526063 0221126 rm 0000184 0012123 0.02 0.988 -.0023578 0023946 volregu_qua -.0213194 0563461 -0.38 0.705 -.1317557 0891169

Hansentestsofexogeneityofinstrumentsubsets:iv(L.rmL3.volregu_qua)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.528441 0895356 -5.90 0.000 -.7039276 -.3529544 vol -.0070589 041373 -0.17 0.865 -.0881484 0740306 rm -.0003569 0005384 -0.66 0.507 -.0014122 0006984 volrule -.0000687 0383554 -0.00 0.999 -.0752439 0751065

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.527489 0894624 -5.90 0.000 -.702832 -.352146 vol -.0070905 050072 -0.14 0.887 -.1052298 0910488 rm -.0003746 0005306 -0.71 0.480 -.0014146 0006653 volcontrol_cur -.0003028 0473598 -0.01 0.995 -.0931263 0925207

Hansentestexcludinggroup: chi2(559) = 16.52 Prob>chi21.000Difference(nullH=exogenous):chi2(2)0.79 Prob>chi2= 0.673

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4313396 1169738 -3.69 0.000 -.6606041 -.2020751 vol 0073424 0250735 0.29 0.770 -.0418008 0564855 rm -.0002953 0005772 -0.51 0.609 -.0014266 000836 volinfla1 -.0125814 0252851 -0.50 0.619 -.0621394 0369765

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.464043 1067542 -4.35 0.000 -.6732774 -.2548086 vol 0016453 0104383 0.16 0.875 -.0188133 0221039 rm 0003258 00064 0.51 0.611 -.0009286 0015803 volinfla2 -.0311639 0227613 -1.37 0.171 -.0757751 0134474

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.5216731 0887072 -5.88 0.000 -.6955359 -.3478103 vol 0111724 0712603 0.16 0.875 -.1284951 1508399 rm -.0004017 000569 -0.71 0.480 -.0015169 0007135 volcredit -.0006041 0026481 -0.23 0.820 -.0057943 0045861

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.5346381 0946407 -5.65 0.000 -.7201304 -.3491458 vol 0567265 2745306 0.21 0.836 -.4813436 5947967 rm -.0002032 0006349 -0.32 0.749 -.0014476 0010411 volliquid -.0014547 0062904 -0.23 0.817 -.0137837 0108743

Sargantestofoverid.restrictions:chi2(540) R4.90 Prob>chi20.671(Notrobust,butnotweakenedbymanyinstruments.)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4724308 0984148 -4.80 0.000 -.6653202 -.2795414 vol -.0221075 0276238 -0.80 0.424 -.0762491 0320341 rm -.0004448 0008524 -0.52 0.602 -.0021155 0012259 volstock_cap 0004569 0008043 0.57 0.570 -.0011196 0020334

Sargantestofoverid.restrictions:chi2(455) G5.21 Prob>chi20.248(Notrobust,butnotweakenedbymanyinstruments.)

Hansentestsofexogeneityofinstrumentsubsets:iv(L.rmL2.volstock_cap

Hansentestexcludinggroup: chi2(604) = 19.09 Prob > chi2 = 1.000 Difference(nullH=exogenous): chi2(59) = -2.41 Prob > chi2 = 1.000

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.3948275 1033881 -3.82 0.000 -.5974644 -.1921907 vol -.0037347 001619 -2.31 0.021 -.006908 -.0005615 rm -.0001126 0001389 -0.81 0.417 -.0003848 0001595 _cons 0083147 0012343 6.74 0.000 0058954 0107339

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)D.(lagGvol)

Sargantestofoverid.restrictions:chi2(663) f6.94 Prob>chi20.450(Notrobust,butnotweakenedbymanyinstruments.)

Hansentestexcludinggroup: chi2(662) = 16.33 Prob > chi2 = 1.000Difference(nullH=exogenous): chi2(1) = 0.34 Prob > chi2 = 0.557

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4437086 1098047 -4.04 0.000 -.6589218 -.2284954 vol -.0037938 0155746 -0.24 0.808 -.0343194 0267318 rm -.0001951 0001894 -1.03 0.303 -.0005663 000176 volvoice -.0303102 0628117 -0.48 0.629 -.1534189 0927985 _cons 0080475 0017671 4.55 0.000 0045842 0115109

Sargantestofoverid.restrictions:chi2(583) Y5.01 Prob>chi20.356(Notrobust,butnotweakenedbymanyinstruments.)

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL2.volvoice)

Hansentestexcludinggroup: chi2(581) = 17.84 Prob > chi2 = 1.000Difference(nullH=exogenous): chi2(2) = -1.18 Prob > chi2 = 1.000

Hansentestexcludinggroup: chi2(527) = 16.97 Prob > chi2 = 1.000 Difference(nullH=exogenous): chi2(51) = 1.86 Prob > chi2 = 1.000

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4417279 1115281 -3.96 0.000 -.660319 -.2231368 vol -.0151282 0217013 -0.70 0.486 -.057662 0274056 rm -.0000437 0002517 -0.17 0.862 -.0005369 0004496 volpolitical 0173543 0267686 0.65 0.517 -.0351111 0698198

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL2.volpolitical)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4648157 1418472 -3.28 0.001 -.7428312 -.1868003 vol -.0115031 0238586 -0.48 0.630 -.0582651 0352589 rm -.0001062 0001941 -0.55 0.584 -.0004867 0002743 volgov -.016378 0394973 -0.41 0.678 -.0937913 0610353 _cons 008983 0016568 5.42 0.000 0057357 0122303

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL2.volgov)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.5385725 0911875 -5.91 0.000 -.7172966 -.3598484 vol -.0034012 0137434 -0.25 0.805 -.0303378 0235353 rm -.0000271 0001784 -0.15 0.879 -.0003767 0003225 volregu_qua 0159765 0575031 0.28 0.781 -.0967274 1286804

Sargantestofoverid.restrictions:chi2(568) U3.61 Prob>chi20.659(Notrobust,butnotweakenedbymanyinstruments.)

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL3.volregu_qua)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4711955 1423802 -3.31 0.001 -.7502555 -.1921355 vol -.0166038 0278372 -0.60 0.551 -.0711638 0379562 rm -.000121 0001973 -0.61 0.540 -.0005078 0002657 volrule -.0153942 0276817 -0.56 0.578 -.0696494 0388609 _cons 0088826 0016453 5.40 0.000 0056579 0121073

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL2.volrule)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.4695867 1400387 -3.35 0.001 -.7440575 -.1951158 vol -.0183805 0295297 -0.62 0.534 -.0762577 0394968 rm -.0001171 0001926 -0.61 0.543 -.0004945 0002603 volcontrol_cur -.0174974 0307079 -0.57 0.569 -.0776837 0426889

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL2.volcontrol_cur)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.3986021 1025499 -3.89 0.000 -.5995962 -.1976079 vol -.0108194 0046121 -2.35 0.019 -.019859 -.0017798 rm -.0001222 0001536 -0.80 0.426 -.0004231 0001788 volinfla1 0095596 0063695 1.50 0.133 -.0029243 0220435 _cons 0081187 001625 5.00 0.000 0049338 0113036

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL.volinfla1)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4004116 1031412 -3.88 0.000 -.6025646 -.1982587 vol -.0010576 0024336 -0.43 0.664 -.0058274 0037121 rm -.0001429 0001396 -1.02 0.306 -.0004165 0001307 volinfla2 -.0365393 0237556 -1.54 0.124 -.0830995 0100209 _cons 007983 0012593 6.34 0.000 005515 0104511

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4339235 1350891 -3.21 0.001 -.6986932 -.1691537 vol 0193126 0366335 0.53 0.598 -.0524878 0911129 rm -.0001123 0001695 -0.66 0.508 -.0004446 00022 volcredit -.0008017 0013501 -0.59 0.553 -.0034479 0018445 _cons 0086109 0015452 5.57 0.000 0055823 0116395

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL2.volcredit)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4516292 1303233 -3.47 0.001 -.7070582 -.1962002 vol 0422625 0602481 0.70 0.483 -.0758217 1603466 rm -.0001144 0001825 -0.63 0.531 -.000472 0002433 volliquid -.0010531 0013479 -0.78 0.435 -.003695 0015888 _cons 008971 0018097 4.96 0.000 0054241 012518

Sargantestofoverid.restrictions:chi2(561) W8.67 Prob>chi20.294(Notrobust,butnotweakenedbymanyinstruments.)

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL2.volliquid)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4697914 1044283 -4.50 0.000 -.6744671 -.2651157 vol -.024437 0430138 -0.57 0.570 -.1087425 0598684 rm -.0000986 0001826 -0.54 0.589 -.0004565 0002592 volstock_cap 0003165 0007991 0.40 0.692 -.0012496 0018827

Sargantestofoverid.restrictions:chi2(478) I0.25 Prob>chi20.339(Notrobust,butnotweakenedbymanyinstruments.)

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL2.volstock_cap)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4330329 129359 -3.35 0.001 -.6865718 -.179494 vol 0006914 0070918 0.10 0.922 -.0132082 0145911 rm 000043 0002984 0.14 0.885 -.0005418 0006278

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)L(1/46). (growthvol)

Sargantestofoverid.restrictions:chi2(372) 73.15 Prob>chi20.473(Notrobust,butnotweakenedbymanyinstruments.)

Difference-in-Hansentestsofexogeneityofinstrumentsubsets:iv(rm)

Hansentestexcludinggroup: chi2(371) = 8.86 Prob>chi21.000Difference(nullH=exogenous):chi2(1)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.5406856 1069102 -5.06 0.000 -.7502256 -.3311455 vol -.0596785 0611617 -0.98 0.329 -.1795532 0601962 rm 0007496 0005823 1.29 0.198 -.0003916 0018908 volvoice -.3050534 314893 -0.97 0.333 -.9222324 3121256

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4849344 1139187 -4.26 0.000 -.7082109 -.2616579 vol -.0214846 0093397 -2.30 0.021 -.0397901 -.0031792 rm -.0014265 0019919 -0.72 0.474 -.0053305 0024775 volpolitical 0096286 0181258 0.53 0.595 -.0258974 0451545

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)L(1/4). (lagGL.vol)

Hansentestsofexogeneityofinstrumentsubsets:iv(L3.rmL3.volpolitic al)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.5304326 0427667 -12.40 0.000 -.6142539 -.4466114 vol -.0436325 0485063 -0.90 0.368 -.1387031 0514381 rm 000888 0017829 0.50 0.618 -.0026064 0043824 volgov -.1957305 0274485 -7.13 0.000 -.2495287 -.1419324

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)L(1/5). (L.lagGvol)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -2.714352 0815695 -33.28 0.000 -2.874225 -2.554479 vol 3044598 0673719 4.52 0.000 1724134 4365062 rm -.0088608 volregu_qua 1.346138 4601733 2.93 0.003 4442146 2.248061

Arellano-BondtestforAR(1)infirstdifferences:z= -2.28 Pr>z0.022Arellano- BondtestforAR(2)infirstdifferences:z= 0.45 Pr>z= 0.655

Hansentestsofexogeneityofinstrumentsubsets:iv(L.rmL3.volregu_qua

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.5664836 0857278 -6.61 0.000 -.734507 -.3984602 vol 6559129 3125484 2.10 0.036 0433294 1.268496 rm -.0042539 003568 -1.19 0.233 -.011247 0027392 volrule 7041102 3568934 1.97 0.049 004612 1.403608

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.5427758 0877001 -6.19 0.000 -.7146649 -.3708868 vol 0685946 0746815 0.92 0.358 -.0777784 2149676 rm -.0013392 00115 -1.16 0.244 -.0035931 0009147 volcontrol_cur 0847232 0847592 1.00 0.318 -.0814019 2508482

Hansentestsofexogeneityofinstrumentsubsets:iv(L3.rmL4.volcontro l_cur)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4709543 0930444 -5.06 0.000 -.6533179 -.2885906 vol 1300391 1042307 1.25 0.212 -.0742492 3343274 rm -.0011595 0018338 -0.63 0.527 -.0047537 0024348 volinfla1 -.1421823 1119055 -1.27 0.204 -.361513 0771483

Hansentestexcludinggroup: chi2(343) = 6.66 Prob> chi2 = 1.000 Difference(nullH=exogenous):chi2(2) = 0.43 Prob> chi2 = 0.806

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.2922326 2046489 -1.43 0.153 -.693337 1088719 vol -.0071059 0142769 -0.50 0.619 -.035088 0208762 rm 0005771 0013133 0.44 0.660 -.001997 0031511 volinfla2 -.0643059 1218412 -0.53 0.598 -.3031102 1744984

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.6548839 0290961 -22.51 0.000 -.7119112 -.5978566 vol 319862 2061489 1.55 0.121 -.0841825 7239065 rm 0032329 0018291 1.77 0.077 -.000352 0068179 volcredit -.0115304 0072396 -1.59 0.111 -.0257197 002659

Sargantestofoverid.restrictions:chi2(287) (4.06 Prob>chi20.538(Notrobust,butnotweakenedbymanyinstruments.)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.486976 1294071 -3.76 0.000 -.7406092 -.2333428 vol -.0171063 0349 -0.49 0.624 -.085509 0512964 rm -.0019939 004954 -0.40 0.687 -.0117036 0077158 volliquid -.0000153 0005301 -0.03 0.977 -.0010542 0010237

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.5299003 1713013 -3.09 0.002 -.8656448 -.1941558 vol -.3486871 736524 -0.47 0.636 -1.792248 1.094873 rm -.0022101 0048465 -0.46 0.648 -.0117091 0072888 volstock_cap 0088193 0187126 0.47 0.637 -.0278568 0454953

Sargantestofoverid.restrictions:chi2(278) (1.71 Prob>chi20.427(Notrobust,butnotweakenedbymanyinstruments.)

Hansentestsofexogeneityofinstrumentsubsets:iv(L2.rmL2.volstock_c ap)

Hansentestexcludinggroup: chi2(350) = 6.20 Prob > chi2 = 1.000 Difference(nullH =exogenous): chi2(31) = 0.00 Prob > chi2 = 1.000

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.3260166 1774518 -1.84 0.066 -.6738158 0217825 vol 0058735 0104194 0.56 0.573 -.0145483 0262952 rm -.0003472 0002411 -1.44 0.150 -.0008197 0001253 _cons 0058389 002375 2.46 0.014 0011841 0104938

Hansentestexcludinggroup: chi2(380) = 7.38 Prob > chi2 = 1.000Difference(nullH =exogenous): chi2(1) = -1.17 Prob > chi2 = 1.000

Hansentestexcludinggroup: chi2(298) = 2.57 Prob > chi2 = 1.000 Difference(nullH =exogenous): chi2(31) = -0.34 Prob > chi2 = 1.000

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -1.569141 4093254 -3.83 0.000 -2.371404 -.7668779 vol 00091 0221461 0.04 0.967 -.0424956 0443155 rm -.0006299 0001493 -4.22 0.000 -.0009225 -.0003374 volvoice -.1889419 1175883 -1.61 0.108 -.4194107 0415269 _cons 0116296 001889 6.16 0.000 0079272 0153319

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)DL.

Arellano-BondtestforAR(1)infirstdifferences:z= Pr>z.Arellano- BondtestforAR(2)infirstdifferences:z= -1.49 Pr>z= 0.137

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L2.rmL2.volvoice)

Hansentestexcludinggroup: chi2(327) = 1.21 Prob > chi2 = 1.000Difference(nullH =exogenous): chi2(2) = 1.02 Prob > chi2 = 0.602

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -1.092982 6815195 -1.60 0.109 -2.428735 2427721 vol 193175 207118 0.93 0.351 -.2127689 5991189 rm -.0001522 0004771 -0.32 0.750 -.0010873 0007829 volpolitical -.1747592 1880915 -0.93 0.353 -.5434118 1938935

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L3.rmL3.volpolitical)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4224875 2229112 -1.90 0.058 -.8593855 0144105 vol -.0549843 5412768 -0.10 0.919 -1.115867 1.005899 rm -.0002847 000867 -0.33 0.743 -.001984 0014146 volgov -.0858553 9253753 -0.09 0.926 -1.899558 1.727847 _cons 0082685 0058091 1.42 0.155 -.0031171 0196542

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL3.volgov)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -1.426871 5031111 -2.84 0.005 -2.412951 -.4407913 vol -.13057 3281577 -0.40 0.691 -.7737473 5126072 rm -.000169 0000364 -4.64 0.000 -.0002404 -.0000977 volregu_qua -.5981128 1.125224 -0.53 0.595 -2.803512 1.607286

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -1.418236 4265023 -3.33 0.001 -2.254165 -.5823064 vol -.9681069 4454801 -2.17 0.030 -1.841232 -.094982 rm -.0017917 0007127 -2.51 0.012 -.0031884 -.0003949 volrule -.9760302 4427406 -2.20 0.027 -1.843786 -.1082746 _cons 0171303 0040198 4.26 0.000 0092516 025009

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL3.volrule)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.405588 4913498 -2.86 0.004 -2.368616 -.4425598 vol -.9245356 6054046 -1.53 0.127 -2.111107 2620356 rm -.0019315 0011528 -1.68 0.094 -.004191 0003279 volcontrol_cur -.9893193 6180569 -1.60 0.109 -2.200688 22205

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL3.volcontrol_cur)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.1887212 2175332 -0.87 0.386 -.6150785 237636 vol -.0057107 0142701 -0.40 0.689 -.0336795 0222582 rm -.0001613 0005568 -0.29 0.772 -.0012526 00093 volinfla1 -.0122577 0826865 -0.15 0.882 -.1743202 1498048 _cons 006673 0058248 1.15 0.252 -.0047433 0180894

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL2.volinfla1)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.1706173 2860928 -0.60 0.551 -.7313489 3901142 vol -.0043019 0159534 -0.27 0.787 -.03557 0269662 rm -.0003301 0002774 -1.19 0.234 -.0008737 0002135 volinfla2 -.0816813 1022084 -0.80 0.424 -.2820061 1186435 _cons 0058659 0034808 1.69 0.092 -.0009562 0126881

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -1.58819 2823842 -5.62 0.000 -2.141652 -1.034727 vol 4904368 2066105 2.37 0.018 0854876 8953859 rm -.0014691 0002964 -4.96 0.000 -.0020499 -.0008882 volcredit -.0175314 007565 -2.32 0.020 -.0323585 -.0027042 _cons 008794 0016345 5.38 0.000 0055903 0119976

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L3.rmL2.volcredit)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -1.205573 4370229 -2.76 0.006 -2.062122 -.3490237 vol 3.922933 1.850157 2.12 0.034 2966925 7.549173 rm -.0077554 0027545 -2.82 0.005 -.0131541 -.0023566 volliquid -.0917319 043174 -2.12 0.034 -.1763515 -.0071123 _cons -.0253981 0231768 -1.10 0.273 -.0708238 0200276

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L3.rmL3.volliquid)

Hansentestexcludinggroup: chi2(261) = 3.30 Prob > chi2 = 1.000 Difference(nullH =exogenous): chi2(27) = -0.00 Prob > chi2 = 1.000

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -1.557149 3683955 -4.23 0.000 -2.279192 -.8351075 vol 2670375 1174233 2.27 0.023 0368921 4971829 rm -.0008987 0004813 -1.87 0.062 -.0018421 0000448 volstock_cap -.0049709 0023313 -2.13 0.033 -.0095402 -.0004016

Sargantestofoverid.restrictions:chi2(288) )1.96 Prob>chi20.424(Notrobust,butnotweakenedbymanyinstruments.)

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L2.rmL2.volstock_cap)

Prob>c hi 2 = 0.082 max = 42 growth Coef.

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.68391 1003601 -6.81 0.000 -.8806121 -.4872079 vol 4237948 3630058 1.17 0.243 -.2876835 1.135273 rm -.0022591 0009682 -2.33 0.020 -.0041568 -.0003614 volvoice 5081633 4126492 1.23 0.218 -.3006144 1.316941

Sargantestofoverid.restrictions:chi2(75) = 56.65 Prob>chi20.944(Notrobust,butnotweakenedbymanyinstruments.)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4510218 2221118 -2.03 0.042 -.886353 -.0156906 vol -.5740001 7740982 -0.74 0.458 -2.091205 9432045 rm 0042666 0026587 1.60 0.109 -.0009443 0094776 volpolitical 3290191 5322003 0.62 0.536 -.7140742 1.372112

Hansentestsofexogeneityofinstrumentsubsets:iv(L2.rmL2.volpoliti cal)

Prob> ch i2 = 0.000 max = 39 growth Coef.

CorrectedS t d E r r z P>|z| [95%Conf.Interval] lagG -.5712973 1146518 -4.98 0.000 -.7960107 -.3465839 vol -.986457 1.284458 -0.77 0.442 -3.503948 1.531034 rm -.0011035 0014593 -0.76 0.450 -.0039637 0017568 volgov -1.237772 1.614648 -0.77 0.443 -4.402424 1.926879

GMM-type(missing=0,separateinstrumentsf o r e a c h periodunlesscollapsed)L(2/3). (growthv o l )

Arellano-Bondt e s t f o r AR(1)i n firstdifferences:z = -2.03 Pr>z0.042Arellano- Bondt e s t f o r AR(2)i n firstdifferences:z = -1.23 Pr>z= 0.218

Sargant e s t o f overid.restrictions:chi2(139) 3.92 Prob>chi20.370( N o t robust,b u t n o t weakenedb y m a n y instruments.)

Hansent e s t o f overid.restrictions:chi2(139) = 5.34 Prob>chi21.000(Robust,b u t weakenedb y m a n y instruments.)

Hansent e s t s o f exogeneityo f instrumentsubsets:iv(L.rmL3.volgov)

Hansent e s t excludinggroup: chi2(137) = 6.40 Prob>chi21.000Difference( n u l l H = exogenous):chi2(2)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.5234002 1816733 -2.88 0.004 -.8794733 -.1673272 vol -.2575928 4827911 -0.53 0.594 -1.203846 6886603 rm -.0006752 0010039 -0.67 0.501 -.0026429 0012925 volregu_qua -.3558681 5917852 -0.60 0.548 -1.515746 8040096

Hansentestsofexogeneityofinstrumentsubsets:iv(rmL3.volregu_qua)

CorrectedS t d E r r z P>|z| [95%C o n f I n t e r v a l ] lagG -.7699642 2155853 -3.57 0.000 -1.192504 -.3474247 vol 0928459 3238195 0.29 0.774 -.5418287 7275204 rm 0042931 005048 0.85 0.395 -.0056009 014187 volrule 214254 3284456 0.65 0.514 -.4294875 8579954

Timev a r i a b l e : y e a r Numbero f g r o u p s = 11 Numbero f i n s t r u m e n t s = 8 0 Obsp e r group:m i n = 7

Prob> ch i2 = 0.000 max = 39 growth Coef.

] lagG -.5600147 2763763 -2.03 0.043 -1.101702 -.018327 vol -.005401 2033737 -0.03 0.979 -.4040061 3932041 rm 0053786 0029186 1.84 0.065 -.0003418 0110991 volcontrol_cur -.0297531 2050324 -0.15 0.885 -.4316092 3721029

GMM-type(missing=0,separateinstrumentsf o r e a c h periodu n l e s s collapsed)L 2

Sargant e s t o f overid.restrictions:c h i 2 ( 7 6 ) = 76.19 Prob>chi20.472( N o t r o b u s t , b u t n o t weakenedb y m a n y instruments.)

Hansent e s t o f overid.restrictions:c h i 2 ( 7 6 ) = 5.84 Prob>chi21.000( R o b u s t , b u t weakenedb y m a n y instruments.)

Hansent e s t excludingg r o u p : chi2(74) = 6.80 Prob>chi21.000D i f f e r e n c e ( n u l l H = exogenous):c h i 2 ( 2 ) = -0.96

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4904993 1728512 -2.84 0.005 -.8292813 -.1517172 vol -.2093416 0784579 -2.67 0.008 -.3631161 -.055567 rm 00183 0035451 0.52 0.606 -.0051183 0087784 volinfla1 2307798 4327344 0.53 0.594 -.617364 1.078924

Corrected Std.Err z P>|z| [95%Conf.Interval] lagG -1.15297 2741885 -4.21 0.000 -1.69037 -.6155707 vol -.3224474 0186679 -17.27 0.000 -.3590359 -.285859 rm 0054314 0008985 6.04 0.000 0036703 0071926 volinfla2 5224074 0722212 7.23 0.000 3808564 6639584

Sargantestofoverid.restrictions:chi2(239) "2.72 Prob>chi20.768(Notrobust,butnotweakenedbymanyinstruments.)

CorrectedS t d E r r z P>|z| [95%C o n f I n t e r v a l ] lagG -.744902 2297683 -3.24 0.001 -1.195239 -.2945645 vol 068782 3367448 0.20 0.838 -.5912257 7287897 rm 0027394 001183 2.32 0.021 0004207 0050581 volcredit -.0079689 0166964 -0.48 0.633 -.0406932 0247554

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -1.843099 5166311 -3.57 0.000 -2.855677 -.8305204 vol -2.749713 1.202392 -2.29 0.022 -5.106358 -.3930693 rm 0108678 0201256 0.54 0.589 -.0285776 0503133 volliquid 0757253 0219335 3.45 0.001 0327365 118714

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.5626157 3261755 -1.72 0.085 -1.201908 0766764 vol -.0141643 0604577 -0.23 0.815 -.1326592 1043307 rm 0015573 0012789 1.22 0.223 -.0009492 0040638 volstock_cap 002119 0012053 1.76 0.079 -.0002433 0044814

Hansentestsofexogeneityofinstrumentsubsets:iv(L.rmL.volstock_cap)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -1.02281 3926263 -2.61 0.009 -1.792343 -.2532763 vol -.2947587 1831697 -1.61 0.108 -.6537647 0642473 rm -.0002884 0003084 -0.94 0.350 -.000893 0003161 _cons 0150205 0041814 3.59 0.000 0068251 023216

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.3613336 2420497 -1.49 0.135 -.8357422 1130751 vol -.3851838 380113 -1.01 0.311 -1.130192 359824 rm -.0001497 0001325 -1.13 0.258 -.0004094 0001099 volvoice -.3992089 4055218 -0.98 0.325 -1.194017 3955992 _cons 0092087 0027319 3.37 0.001 0038543 014563

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.354393 1872152 -1.89 0.058 -.7213281 012542 vol 7885062 1.013797 0.78 0.437 -1.198499 2.775511 rm 0000435 0003271 0.13 0.894 -.0005977 0006846 volpolitical -.6138148 7725814 -0.79 0.427 -2.128047 9004169

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.2037586 2986075 -0.68 0.495 -.7890186 3815014 vol -2.198846 1.120194 -1.96 0.050 -4.394386 -.0033049 rm -.0002408 0002776 -0.87 0.386 -.0007849 0003033 volgov -2.675469 1.336419 -2.00 0.045 -5.294803 -.0561349 _cons 0188752 0069693 2.71 0.007 0052156 0325349

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)DL2.

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4111309 183272 -2.24 0.025 -.7703375 -.0519244 vol -.3189669 2482809 -1.28 0.199 -.8055884 1676546 rm -.0001265 0001128 -1.12 0.262 -.0003476 0000947 volregu_qua -.3854111 3052628 -1.26 0.207 -.9837151 2128929 _cons 0097728 0022749 4.30 0.000 005314 0142316

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(rmvolregu_qua)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.3582172 2683644 -1.33 0.182 -.8842017 1677673 vol 0127835 0818859 0.16 0.876 -.14771 173277 rm -.000164 0001304 -1.26 0.208 -.0004197 0000916 volrule 0236787 0862674 0.27 0.784 -.1454023 1927598 _cons 0075552 0022029 3.43 0.001 0032376 0118729

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.4234777 3089304 -1.37 0.170 -1.02897 1820149 vol -1.156611 3.641642 -0.32 0.751 -8.294099 5.980877 rm -.000132 0001416 -0.93 0.351 -.0004094 0001455 volcontrol_cur -1.102949 3.534164 -0.31 0.755 -8.029783 5.823885

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(rmL2.volcontrol_cur)

Corrected Std.Err z P>|z| [95%Conf.Interval] lagG -.405041 1743352 -2.32 0.020 -.7467317 -.0633502 vol -.0305414 3941138 -0.08 0.938 -.8029903 7419074 rm -.0003125 0005593 -0.56 0.576 -.0014087 0007837 volinfla1 085897 7490333 0.11 0.909 -1.382181 1.553975 _cons 0065012 0028375 2.29 0.022 0009399 0120625

Sargantestofoverid.restrictions:chi2(228) !2.29 Prob>chi20.765(Notrobust,butnotweakenedbymanyinstruments.)

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(rmL.volinfla1)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.3861381 1993699 -1.94 0.053 -.776896 0046198 vol 3923574 1.105494 0.35 0.723 -1.774371 2.559085 rm -.0001575 0001969 -0.80 0.424 -.0005435 0002285 volinfla2 -.4572601 1.299431 -0.35 0.725 -3.004098 2.089577 _cons 0059345 0047869 1.24 0.215 -.0034477 0153167

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(rmL2.volinfla2)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.2429081 3461843 -0.70 0.483 -.9214169 4356008 vol 075087 2202321 0.34 0.733 -.35656 5067339 rm 0004233 0006785 0.62 0.533 -.0009065 001753 volcredit 006202 0109421 0.57 0.571 -.0152441 0276481 _cons 0066165 0048358 1.37 0.171 -.0028614 0160944

Corrected Std.Err z P>|z| [95%Conf.Interval] lagG -1.958632 5954276 -3.29 0.001 -3.125649 -.7916154 vol -.5944987 1862307 -3.19 0.001 -.9595042 -.2294932 rm 0009202 0008284 1.11 0.267 -.0007035 0025439 volliquid 025203 0167756 1.50 0.133 -.0076766 0580826 _cons 017826 0034644 5.15 0.000 0110359 0246161

Corrected Std.Err z P>|z| [95%Conf.Interval] lagG -.0401756 3746772 -0.11 0.915 -.7745294 6941783 vol -.0622971 0855835 -0.73 0.467 -.2300376 1054434 rm -.0003556 00026 -1.37 0.171 -.0008651 0001539 volstock_cap 0013177 0033563 0.39 0.695 -.0052606 007896

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)D. (lagGvol)

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevel s iv(rmvolstock_cap)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.3102051 1253424 -2.47 0.013 -.5558716 -.0645385 vol -.6002441 3352305 -1.79 0.073 -1.257284 0567956 rm -.0006399 0012399 -0.52 0.606 -.0030701 0017903

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG 9567728 7886595 1.21 0.225 -.5889713 2.502517 vol -1.424259 1.125195 -1.27 0.206 -3.6296 7810825 rm 0001927 0030455 0.06 0.950 -.0057764 0061618 volvoice -1.294698 1.082481 -1.20 0.232 -3.416321 8269258

Arellano-BondtestforAR(1)infirstdifferences:z= -1.43 Pr>z0.152Arellano- BondtestforAR(2)infirst differences:z= 0.94 Pr>z= 0.348

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG 1131574 4851281 0.23 0.816 -.8376763 1.063991 vol -1.389136 1.034333 -1.34 0.179 -3.416392 6381202 rm -.0018629 0018276 -1.02 0.308 -.0054449 0017191 volpolitical 9941466 7190601 1.38 0.167 -.4151853 2.403478

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG 1876939 5393435 0.35 0.728 -.8693999 1.244788 vol -1.073854 6015593 -1.79 0.074 -2.252888 1051811 rm -.004594 volgov -.7676713 345489 -2.22 0.026 -1.444817 -.0905253

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG 1.020356 8652644 1.18 0.238 -.6755312 2.716243 vol -1.089189 8368296 -1.30 0.193 -2.729345 5509665 rm -.0002955 003262 -0.09 0.928 -.0066889 0060979 volregu_qua -1.058021 8718197 -1.21 0.225 -2.766757 6507139

Hansentestsofexogeneityofinstrumentsubsets:iv(L.rmL.volregu_qua)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG 1841188 5606435 0.33 0.743 -.9147222 1.28296 vol -1.070149 695128 -1.54 0.124 -2.432575 2922768 rm -.0034459 0016948 -2.03 0.042 -.0067676 -.0001241 volrule -.7381967 4542269 -1.63 0.104 -1.628465 1520716

Prob> chi2 = 0.003 max = 41 growth Coef.

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG 1912586 5637926 0.34 0.734 -.9137545 1.296272 vol -1.037649 6921188 -1.50 0.134 -2.394177 3188792 rm -.0040296 00223 -1.81 0.071 -.0084002 0003411 volcontrol_cur -.5404154 3869823 -1.40 0.163 -1.298887 218056

Arellano-BondtestforAR(1)infirstdifferences:z = -1.51 Pr>z0.131Arellano- BondtestforAR(2)infirstdifferences:z = 0.60 Pr>z= 0.548

Sargantestofoverid.restrictions:chi2(143) 0.94 Prob> chi20.309(Notrobust,butnotweakenedbymanyinstruments.)

Hansentestofoverid.restrictions:chi2(143) = 3.06 Prob> chi21.000(Robust,butweakenedbymanyinstruments.)

Hansentestexcludinggroup: chi2(141) = 3.11 Prob> chi21.000Difference(nullH = exogenous):chi2(2)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.3418095 1612002 -2.12 0.034 -.657756 -.025863 vol -.4749547 3810779 -1.25 0.213 -1.221854 2719443 rm 0067766 0038867 1.74 0.081 -.0008412 0143945 volinfla1 -.0201523 3657856 -0.06 0.956 -.7370789 6967743

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.4565304 2265373 -2.02 0.044 -.9005354 -.0125255 vol -.0136869 0229798 -0.60 0.551 -.0587265 0313528 rm 0048403 0029462 1.64 0.100 -.0009341 0106148 volinfla2 3788015 3253026 1.16 0.244 -.25878 1.016383

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG 3407087 6222754 0.55 0.584 -.8789285 1.560346 vol -1.253818 6554119 -1.91 0.056 -2.538402 0307658 rm -.0077937 0040423 -1.93 0.054 -.0157165 0001291 volcredit 0352995 0130755 2.70 0.007 009672 060927

Hansentestexcludinggroup: chi2(57) = 4.40 Prob> chi2 = 1.000 Difference(nullH=exogenous):chi2(2) = -1.53 Prob> chi2 = 1.000

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.330149 13708 -2.41 0.016 -.5988209 -.0614771 vol 5580429 4483611 1.24 0.213 -.3207288 1.436815 rm 0024721 0016419 1.51 0.132 -.0007461 0056902 volliquid -.0150812 0115157 -1.31 0.190 -.0376516 0074891

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.2231124 0935544 -2.38 0.017 -.4064755 -.0397492 vol 493024 2435664 2.02 0.043 0156426 9704054 rm -.0073802 0043937 -1.68 0.093 -.0159918 0012313 volstock_cap -.0107626 0043166 -2.49 0.013 -.019223 -.0023021

Hansentestsofexogeneityofinstrumentsubsets:iv(L2.rmL.volstock_cap)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.2438813 1646338 -1.48 0.139 -.5665576 0787949 vol -.7134593 2061166 -3.46 0.001 -1.11744 -.3094781 rm -.0009878 0010221 -0.97 0.334 -.0029912 0010155

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)DL4. (lagGvol)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG 1944263 6298785 0.31 0.758 -1.040113 1.428966 vol -2.336584 1.675407 -1.39 0.163 -5.620321 947154 rm -.0046524 0053902 -0.86 0.388 -.015217 0059122 volvoice -2.23221 1.441191 -1.55 0.121 -5.056893 5924732 _cons -.0091485 0238843 -0.38 0.702 -.0559608 0376638

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.3492856 7465237 -0.47 0.640 -1.812445 1.113874 vol -3.543796 9244164 -3.83 0.000 -5.355619 -1.731973 rm -.0005135 volpolitical 2.510326 2288378 10.97 0.000 2.061812 2.95884

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.2059176 1.293998 -0.16 0.874 -2.742106 2.330271 vol -1.763961 8922022 -1.98 0.048 -3.512645 -.0152767 rm -.0032797 0024666 -1.33 0.184 -.0081141 0015546 volgov -1.194141

Corrected Std.Err z P>|z| [95%Conf.Interval] lagG -2.307354 8481782 -2.72 0.007 -3.969753 -.6449551 vol 1879231 6834194 0.27 0.783 -1.151554 1.527401 rm 0017769 0028609 0.62 0.535 -.0038304 0073841 volregu_qua -.1370641 1.509259 -0.09 0.928 -3.095158 2.82103

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L2.rmL2.volregu_qua)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG 0850486 1.049979 0.08 0.935 -1.972872 2.142969 vol -.874189 3026747 -2.89 0.004 -1.46742 -.2809576 rm -.0014122 0015193 -0.93 0.353 -.0043899 0015656 volrule -1.348226 1.16361 -1.16 0.247 -3.628861 9324082 _cons 0005947 0147117 0.04 0.968 -.0282398 0294291

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L3.rmL2.volrule)

Hansentestexcludinggroup: chi2(111) = 3.11 Prob > chi2 = 1.000 Difference(nullH = exogenous): chi2(28) = -0.55 Prob > chi2 = 1.000

Prob> chi2 = 0.085 max = 40 growth Coef.

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.1532625 8205611 -0.19 0.852 -1.761533 1.455008 vol -1.329878 5137549 -2.59 0.010 -2.336819 -.3229364 rm -.0029353 0021799 -1.35 0.178 -.0072079 0013372 volcontrol_cur -.7855744 3095474 -2.54 0.011 -1.392276 -.1788725

Arellano-BondtestforAR(1)infirstdifferences:z = -4.03 Pr>z0.000Arellano- BondtestforAR(2)infirstdifferences:z = -0.35 Pr>z= 0.727

Sargantestofoverid.restrictions:chi2(139) 7.59 Prob> chi20.746(Notrobust,butnotweakenedbymanyinstruments.)

Hansentestofoverid.restrictions:chi2(139) = 2.56 Prob> chi21.000(Robust,butweakenedbymanyinstruments.)

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L3.rmL.volcontrol_cur)

Hansentestexcludinggroup: chi2(137) = 2.61 Prob > chi2 = 1.000Difference(nullH = exogenous): chi2(2) = -0.04 Prob > chi2 = 1.000

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -4.820163 532316 -9.06 0.000 -5.863483 -3.776843 vol -8.53402 3.781866 -2.26 0.024 -15.94634 -1.121698 rm -.0029337 0015065 -1.95 0.051 -.0058865 000019 volinfla1 9.565055 4.461051 2.14 0.032 8215558 18.30855 _cons 0775283 0147398 5.26 0.000 0486388 1064177

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L4.rmL3.volinfla1)

Hansentestexcludinggroup: chi2(144) = 3.94 Prob > chi2 = 1.000 Difference(nullH= exogenous): chi2(21) = -1.48 Prob > chi2 = 1.000

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.121795 1.187937 -0.10 0.918 -2.450109 2.206519 vol -.5888159 6368801 -0.92 0.355 -1.837078 6594461 rm 0061935 0072106 0.86 0.390 -.0079389 020326 volinfla2 -46.0702 12.71263 -3.62 0.000 -70.9865 -21.15389 _cons 4532265 2830376 1.60 0.109 -.101517 1.00797

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.4010255 1683535 -2.38 0.017 -.7309923 -.0710588 vol -1.456256 8745538 -1.67 0.096 -3.17035 2578381 rm -.0007678 0018244 -0.42 0.674 -.0043435 002808 volcredit 0557015 0361658 1.54 0.124 -.0151822 1265852 _cons -.0041629 0210659 -0.20 0.843 -.0454512 0371255

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L2.rmL.volcredit)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -2.645386 1.140325 -2.32 0.020 -4.880382 -.4103899 vol 1.29132 2.031407 0.64 0.525 -2.690164 5.272804 rm 0010896 0009812 1.11 0.267 -.0008334 0030126 volliquid -.0252443 0390429 -0.65 0.518 -.1017671 0512784 _cons 0307577 0114598 2.68 0.007 0082969 0532185

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.040099 4909427 -0.08 0.935 -1.002329 922131 vol -.554056 2413121 -2.30 0.022 -1.027019 -.081093 rm -.0006339 0010172 -0.62 0.533 -.0026277 0013599 volstock_cap 0100452 0041018 2.45 0.014 0020059 0180845

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L2.rmvolstock_cap)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.376991 6905987 -1.99 0.046 -2.730539 -.0234422 vol 5334191 7572756 0.70 0.481 -.9508138 2.017652 rm -.0001366 0012921 -0.11 0.916 -.0026691 0023958 _cons 0087166 0019288 4.52 0.000 0049362 0124969

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.9920712 vol 6986261 2769282 2.52 0.012 1558569 1.241395 rm -.0059604 volvoice 1897871 7970964 0.24 0.812 -1.372493 1.752067

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)L(1/4). (growthL.vol)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.710562 498566 -3.43 0.001 -2.687734 -.7333911 vol -2.565504 1.3981 -1.83 0.067 -5.305729 1747215 rm -.0112645 0062517 -1.80 0.072 -.0235176 0009886 volpolitical 3.568471 2.151149 1.66 0.097 -.6477034 7.784645

Hansentestsofexogeneityofinstrumentsubsets:iv(L2.rmL2.volpoliti cal)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.1322 2720824 -4.16 0.000 -1.665471 -.5989278 vol 1.027085 7389315 1.39 0.165 -.4211942 2.475364 rm 0039074 002242 1.74 0.081 -.0004869 0083017 volgov 1.958296 7615354 2.57 0.010 4657137 3.450878

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.157856 2862291 -4.05 0.000 -1.718855 -.5968577 vol 9150061 6996357 1.31 0.191 -.4562545 2.286267 rm 0027765 0020332 1.37 0.172 -.0012085 0067615 volregu_qua 1.073308 5357688 2.00 0.045 0232205 2.123396

Hansentestsofexogeneityofinstrumentsubsets:iv(L2.rmL2.volregu_q ua)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.178911 2858368 -4.12 0.000 -1.739141 -.6186814 vol 1.609676 8984514 1.79 0.073 -.1512563 3.370609 rm 0039736 0020513 1.94 0.053 -.0000468 007994 volrule 1.673217 4605452 3.63 0.000 7705652 2.575869

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -2.111709 8805634 -2.40 0.016 -3.837582 -.3858367 vol 3.861555 9302349 4.15 0.000 2.038328 5.684782 rm 0086013 volcontrol_cur 4.990398

Hansentestsofexogeneityofinstrumentsubsets:iv(L2.rmL3.volcontrol_ cur)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.304237 1.009889 -1.29 0.197 -3.283583 6751097 vol -.2349182 0916208 -2.56 0.010 -.4144917 -.0553447 rm 0454093 0254574 1.78 0.074 -.0044863 0953049 volinfla1 1.240817 1.706414 0.73 0.467 -2.103692 4.585326

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.992553 vol 7719491 6363214 1.21 0.225 -.475218 2.019116 rm -.0030159 volinfla2 -1.144599 325923 -3.51 0.000 -1.783396 -.5058013

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.11346 4768562 -2.34 0.020 -2.048081 -.1788393 vol -.3587375 rm 0141556 0050155 2.82 0.005 0043253 0239859 volcredit 0046021

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.152988 4509634 -2.56 0.011 -2.03686 -.2691162 vol 1.944583 1.433281 1.36 0.175 -.8645958 4.753761 rm -.0033828 0108366 -0.31 0.755 -.0246221 0178565 volliquid -.0264172 0149984 -1.76 0.078 -.0558136 0029792

Hansentestexcludinggroup: chi2(61) = 2.11 Prob> chi2 = 1.000 Difference(nullH =exogenous):chi2(3) = -1.51 Prob> chi2 = 1.000

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.269135 184628 -6.87 0.000 -1.630999 -.9072709 vol -.4183769 1657467 -2.52 0.012 -.7432344 -.0935193 rm 0014914 000574 2.60 0.009 0003663 0026166 volstock_cap 0134803 0069636 1.94 0.053 -.0001681 0271286

GMM- type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)L(1/2).grow th

Hansentestsofexogeneityofinstrumentsubsets:iv(L4.rmL5.volstock_capv ol)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -2.008831 3879687 -5.18 0.000 -2.769236 -1.248426 vol 5706062 344414 1.66 0.098 -.1044328 1.245645 rm -.0009188 0006744 -1.36 0.173 -.0022405 000403 _cons 007532 0043265 1.74 0.082 -.0009478 0160117

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)DL3. (growthvol)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.2039908 3085898 -0.66 0.509 -.8088157 4008342 vol 4562257 6538622 0.70 0.485 -.8253206 1.737772 rm 0027891 001977 1.41 0.158 -.0010857 006664 volvoice -.51875 5360021 -0.97 0.333 -1.569295 5317949 _cons 0197094 0071766 2.75 0.006 0056435 0337752

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L2.rmL2.volvoice)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -1.205816 0926972 -13.01 0.000 -1.387499 -1.024133 vol -.9122012 5.485153 -0.17 0.868 -11.6629 9.838501 rm -.0022036 volpolitical 9.420992 11.32804 0.83 0.406 -12.78156 31.62354

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL.volpolitical)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.277184 5166037 -2.47 0.013 -2.289709 -.2646595 vol 1.111059 8809986 1.26 0.207 -.6156668 2.837784 rm -.0006721 volgov 2.289017 7515302 3.05 0.002 8160446 3.761989

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L2.rmL2.volgov)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -2.225898 7767852 -2.87 0.004 -3.748369 -.7034275 vol 1.361678 5190033 2.62 0.009 3444502 2.378906 rm -.0013266 0009059 -1.46 0.143 -.0031023 000449 volregu_qua 1.309547 2285382 5.73 0.000 8616203 1.757474

Arellano-BondtestforAR(1)infirstdifferences:z= -1.72 Pr>z0.086Arellano-

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(rmvolregu_qua)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -2.296934 9232861 -2.49 0.013 -4.106541 -.4873264 vol 1.832478 1.013043 1.81 0.070 -.153049 3.818005 rm -.000456 0002347 -1.94 0.052 -.000916 4.06e-06 volrule 1.718782 6458248 2.66 0.008 4529885 2.984575 _cons 0092564 0052807 1.75 0.080 -.0010935 0196064

Corrected Std.Err z P>|z| [95%Conf.Interval] lagG -1.300186 4179633 -3.11 0.002 -2.119379 -.4809931 vol -.2385382 1.894907 -0.13 0.900 -3.952487 3.475411 rm -.0001968 0003004 -0.66 0.512 -.0007856 0003919 volcontrol_cur -1.512562 2.718211 -0.56 0.578 -6.840157 3.815033

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L4.rmL2.volcontrol_cur)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.197281 4078939 -2.94 0.003 -1.996738 -.3978233 vol 5735518 4266875 1.34 0.179 -.2627404 1.409844 rm -.0009264 000367 -2.52 0.012 -.0016457 -.0002071 volinfla1 -.6138186 4831352 -1.27 0.204 -1.560746 3331091

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.5320208 547164 -0.97 0.331 -1.604443 5404009 vol -9.464989 7.343408 -1.29 0.197 -23.8578 4.927825 rm -.0038748 0019676 -1.97 0.049 -.0077312 -.0000185 volinfla2 11.23969 8.250054 1.36 0.173 -4.930118 27.4095 _cons 0004598 0018309 0.25 0.802 -.0031288 0040483

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.435953 4584256 -3.13 0.002 -2.33445 -.537455 vol 9.762479 9.962482 0.98 0.327 -9.763627 29.28858 rm -.0061214 0041569 -1.47 0.141 -.0142688 002026 volcredit -.1056352 1094512 -0.97 0.334 -.3201557 1088852 _cons -.076813 0779526 -0.99 0.324 -.2295973 0759714

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.406123 3932733 -3.58 0.000 -2.176924 -.6353211 vol 12.44596 10.68568 1.16 0.244 -8.497585 33.38951 rm -.0068748 0041634 -1.65 0.099 -.015035 0012853 volliquid -.1309095 1143719 -1.14 0.252 -.3550743 0932553 _cons -.0844762 0702003 -1.20 0.229 -.2220663 0531139

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.111268 1498193 -7.42 0.000 -1.404908 -.8176274 vol -5.73761 3.572901 -1.61 0.108 -12.74037 1.265147 rm -.0045821 0027904 -1.64 0.101 -.0100512 000887 volstock_cap 0950231 0607093 1.57 0.118 -.0239649 2140111

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)L(2/4). (growthvol)collapsed

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL.volstock_cap)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.4818 4417009 -3.35 0.001 -2.347518 -.6160817 vol -.0597068 1083306 -0.55 0.582 -.2720309 1526172 rm -.0023904 0025593 -0.93 0.350 -.0074065 0026257

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.320305 2795641 -4.72 0.000 -1.86824 -.7723693 vol -1.809031 2.534116 -0.71 0.475 -6.775807 3.157745 rm -.0326491 0441921 -0.74 0.460 -.1192641 0539659 volvoice -6.772129 9.606186 -0.70 0.481 -25.59991 12.05565

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.9900212 4312317 -2.30 0.022 -1.83522 -.1448225 vol 2754727 306031 0.90 0.368 -.3243371 8752825 rm -.0091414 0111049 -0.82 0.410 -.0309067 0126239 volpolitical -.3142906 2123218 -1.48 0.139 -.7304337 1018526

Arellano-BondtestforAR(1)infirstdifferences:z=-10.17 Pr>z= 0.000Arellano- BondtestforAR(2)infirstdifferences:z= -1.47 Pr>z= 0.142

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.298186 6796328 -1.91 0.056 -2.630241 0338702 vol -3.657858 3.195631 -1.14 0.252 -9.92118 2.605464 rm 0022962 0053414 0.43 0.667 -.0081728 0127652 volgov -6.507093 5.723509 -1.14 0.256 -17.72496 4.710779

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)L(3/46). (growthL.vol)

Difference-in-Hansentestsofexogeneityofinstrumentsubsets:iv(rmvolgov)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.624232 604484 -2.69 0.007 -2.808999 -.4394652 vol 3974524 3192566 1.24 0.213 -.228279 1.023184 rm 0023806 0197282 0.12 0.904 -.0362859 0410471 volregu_qua 3795437 4443623 0.85 0.393 -.4913903 1.250478

Hansentestsofexogeneityofinstrumentsubsets:iv(L5.rmL5.volregu_ qua)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.454147 3845428 -3.78 0.000 -2.207837 -.7004575 vol 0 (omitted) rm -.0050508 0065897 -0.77 0.443 -.0179664 0078647 volrule 0113951 010818 1.05 0.292 -.0098077 0325979

Corrected Std.Err z P>|z| [95%Conf.Interval] lagG -1.121625 431721 -2.60 0.009 -1.967783 -.2754679 vol -3.685349 1.910099 -1.93 0.054 -7.429075 0583773 rm -.0031466 0179219 -0.18 0.861 -.0382728 0319796 volcontrol_cur -3.60137 1.86943 -1.93 0.054 -7.265385 0626456

Arellano-BondtestforAR(1)infirstdifferences:z= -1.50 Pr> z 0.134Arellano- BondtestforAR(2)infirstdifferences:z= -1.16 Pr> z = 0.246

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.550831 0408674 -13.48 0.000 -.6309296 -.4707325 vol -1.237319 7052198 -1.75 0.079 -2.619525 144886 rm 0079884 0044855 1.78 0.075 -.0008031 0167799 volinfla1 1.311303 7469853 1.76 0.079 -.152761 2.775368

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.338115 3946402 -3.39 0.001 -2.111596 -.5646348 vol 0386595 0483156 0.80 0.424 -.0560374 1333564 rm 000886 0055355 0.16 0.873 -.0099633 0117353 volinfla2 -.4170708 1987205 -2.10 0.036 -.8065559 -.0275858

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.359689 428259 -3.17 0.001 -2.199061 -.5203165 vol 0 (omitted) rm -.0023564 0015606 -1.51 0.131 -.0054151 0007023 volcredit -.0006591 0002073 -3.18 0.001 -.0010653 -.0002529

Hansentestexcludinggroup: chi2(0) = 0.00 Prob> chi2 = Difference(nullH=exogenous):chi2(2) = 1.91 Prob> chi2 = 0.384

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.8402831 2640528 -3.18 0.001 -1.357817 -.3227492 vol -6.328746 2.97252 -2.13 0.033 -12.15478 -.5027138 rm -.0017417 0058331 -0.30 0.765 -.0131744 009691 volliquid 1465289 0695534 2.11 0.035 0102068 282851

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)L(4/5). (lagGvol)collapsed

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.435178 4971309 -2.89 0.004 -2.409537 -.4608198 vol -.1277735 0368113 -3.47 0.001 -.1999224 -.0556246 rm 0037044 0040729 0.91 0.363 -.0042782 0116871 volstock_cap 0056441 0019117 2.95 0.003 0018972 0093909

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -.3873748 2340133 -1.66 0.098 -.8460324 0712828 vol 0034644 0080612 0.43 0.667 -.0123352 019264 rm 0000894 0004 0.22 0.823 -.0006946 0008734 _cons 0096433 0025611 3.77 0.000 0046236 0146631

Sargantestofoverid.restrictions:chi2(221) #9.82 Prob>chi20.183(Notrobust,butnotweakenedbymanyinstruments.)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.576921 8304 -1.90 0.058 -3.204475 0506332 vol 8118113 502829 1.61 0.106 -.1737155 1.797338 rm -.0002593 0011907 -0.22 0.828 -.0025932 0020745 volvoice 3.035575 1.948918 1.56 0.119 -.7842335 6.855384 _cons 0013634 0194103 0.07 0.944 -.0366802 039407

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL.volvoice)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.340772 7359782 -1.82 0.068 -2.783263 101719 vol -1.456354 1.034049 -1.41 0.159 -3.483054 5703453 rm 0020833 0013583 1.53 0.125 -.0005789 0047454 volpolitical 1.462954 1.038483 1.41 0.159 -.5724348 3.498342

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.330156 6410581 -2.07 0.038 -2.586607 -.0737054 vol 7806483 5002361 1.56 0.119 -.1997965 1.761093 rm -.0006488 0007146 -0.91 0.364 -.0020494 0007519 volgov 1.315814 817025 1.61 0.107 -.2855259 2.917153 _cons 0009007 0135713 0.07 0.947 -.0256987 0275

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)DL2. (L.lagGvol)

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L2.rmL.volgov)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -34.296 23.18841 -1.48 0.139 -79.74444 11.15244 vol -13.69583 9.853779 -1.39 0.165 -33.00888 5.617222 rm 0623045 0446911 1.39 0.163 -.0252884 1498974 volregu_qua -47.55735 34.21657 -1.39 0.165 -114.6206 19.50591

Arellano-BondtestforAR(1)infirstdifferences:z= Pr>z.Arellano- BondtestforAR(2)infirstdifferences:z= 0.14 Pr>z= 0.886

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG 0430994 3687875 0.12 0.907 -.6797108 7659097 vol 3.9973 2.475744 1.61 0.106 -.8550695 8.84967 rm -.0065998 0037623 -1.75 0.079 -.0139738 0007742 volrule 3.521012 2.19059 1.61 0.108 -.772465 7.814488 _cons -.1110067 0722396 -1.54 0.124 -.2525937 0305803

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL.volrule)

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -.0186764 3349939 -0.06 0.956 -.6752524 6378997 vol 2.7196 1.68812 1.61 0.107 -.5890547 6.028254 rm -.0070109 0040151 -1.75 0.081 -.0148803 0008585 volcontrol_cur 2.174541 1.360442 1.60 0.110 -.4918758 4.840958

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L.rmL.volcontrol_cur)

Hansentestexcludinggroup: chi2(0) = 0.00 Prob > chi2 = Difference(nullH=exogenous): chi2(2) = -0.00 Prob > chi2 = 1.000

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.599841 3079734 -5.19 0.000 -2.203457 -.996224 vol -.3952668 2139445 -1.85 0.065 -.8145903 0240567 rm 0009861 0009725 1.01 0.311 -.0009199 0028922 volinfla1 2837677 1950628 1.45 0.146 -.0985484 6660839 _cons 0389512 0114238 3.41 0.001 0165611 0613414

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)DL5. (lagGvol)collapsed

Arellano-BondtestforAR(1)infirstdifferences:z=-11.33 Pr>z= 0.000Arellano- BondtestforAR(2)infirstdifferences:z= -1.45 Pr>z= 0.146

Hansentestexcludinggroup: chi2(0) = 0.00 Prob > chi2 = Difference(nullH=exogenous): chi2(2) = 0.00 Prob > chi2 = 1.000

CorrectedS td.Err z P>|z| [95%Conf.Interval] lagG -1.106027 0711819 -15.54 0.000 -1.245541 -.9665126 vol -.1138877 0738762 -1.54 0.123 -.2586824 0309069 rm 001892 0016956 1.12 0.265 -.0014313 0052153 volinfla2 -.7747245 7567829 -1.02 0.306 -2.257992 7085428 _cons 0448608 0262345 1.71 0.087 -.0065578 0962794

Hansentestexcludinggroup: chi2(0) = 0.00 Prob > chi2 = Difference(nullH=exogenous): chi2(2) = -0.00 Prob > chi2 = 1.000

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -7.76368 6.136382 -1.27 0.206 -19.79077 4.263407 vol -8.237733 5.879644 -1.40 0.161 -19.76162 3.286158 rm 0011356 0012334 0.92 0.357 -.0012818 003553 volcredit 3171886 2264675 1.40 0.161 -.1266795 7610568 _cons 0693814 0454252 1.53 0.127 -.0196503 1584131

GMM-type(missing=0,separateinstrumentsforeachperiodunlesscollapsed)L(1/46). (L.lagGL2.vol)

CorrectedSt d.Err z P>|z| [95%Conf.Interval] lagG -1.340769 1949017 -6.88 0.000 -1.722769 -.9587684 vol -1.078032 8013895 -1.35 0.179 -2.648727 4926622 rm 0013918 0009289 1.50 0.134 -.0004288 0032123 volliquid 0255041 0186938 1.36 0.172 -.011135 0621432 _cons 0291109 009924 2.93 0.003 0096601 0485616

Hansentestsofexogeneityofinstrumentsubsets:GMMinstrumentsforlevels iv(L3.rmL3.volliquid)

Tiêu đề	Banking Industry Volatility and Economic Growth
Tác giả	Trần Quốc Thanh
Người hướng dẫn	Assoc. Prof. Dr. Võ Xuân Vinh
Trường học	University of Economics
Chuyên ngành	Development Economics
Thể loại	thesis
Năm xuất bản	2015
Thành phố	Hồ Chí Minh City

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Số trang	220
Dung lượng	817,75 KB