1. Trang chủ
  2. » Luận Văn - Báo Cáo

(Luận văn) market risk versus credit risk of the selected countries in the trans pacific partnership agreement

66 0 0

Đang tải... (xem toàn văn)

Tài liệu hạn chế xem trước, để xem đầy đủ mời bạn chọn Tải xuống

THÔNG TIN TÀI LIỆU

Nội dung

t to UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ng hi INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS ep w n lo VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS ad ju y th yi pl ua al n MARKET RISK VERSUS CREDIT RISK OF SELECTED COUNTRIES IN THE TRANS-PACIFIC PARTNERSHIP AGREEMENT n va ll fu oi m at nh z z k jm QUANG VAN TUAN ht vb BY om l.c gm an Lu MASTER OF ARTS IN DEVELOPMENT ECONOMICS n va ey t re th HO CHI MINH CITY December 2017 t to UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS ng hi ep w n lo VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS ad ju y th yi pl ua al n MARKET RISK VERSUS CREDIT RISK OF SELECTED COUNTRIES IN THE TRANS-PACIFIC PARTNERSHIP AGREEMENT n va ll fu oi m at nh A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS z z k jm ht vb BY QUANG VAN TUAN an Lu Dr VO HONG DUC om l.c gm Academic Supervisor n va ey t re th HO CHI MINH CITY December 201 t to ABSTRACT ng hi At the time this study is finalized, the future of the so-called Trans-Pacific ep Partnership Agreement (TPP) is still uncertain after the US Present Donald Trump walked w away from his predecessor Barack Obama’s commitment A different version of TPP, or n to be called the Comprehensive and Progressive Agreement for Trans-Pacific Partnership lo ad (CPTPP), may be formed without the US presence Among these member countries, ju y th Vietnam and Malaysia (in the ASEAN), together with Australia and New Zealand, in the Pacific Ocean, are generally considered closely competitive nations for various industries, yi pl in particular for Agriculture; Food and Beverage and Tourism ua al This study is conducted to measure and rank the market risk level of 10 n industries/sectors for selected courtiers in the Asia Pacific region: Vietnam, Malaysia, va Australia and New Zealand Two periods are considered in market risk, including: (i) the n ll fu GFC period (2007-2009); and (ii) the post-GFC period (2010-2016) The market risk level oi m is measured using the parametric approach and the historical approach for both Value at Risk (VaR), the potential losses in the future over the given time period (day or month) at nh at a given confidential level, and Conditional Value at Risk (CVaR), which is designed to z estimate the risk of extreme loss z ht vb Findings from this study confirm that Vietnamese sectors are relatively riskier than jm their counterparts in Malaysia, Australia and New Zealand In addition, market risk level k across sectors in all countries has substantially reduced in the post-GFC period Financials gm including Banks, Diversified Financials, and Insurance have been largely ignored from the l.c Vietnamese Government’s focus Interestingly, IT industry is considered very low risk in om Vietnam whereas this sector belongs to a group of high market risk in Malaysia, Australia, an Lu and New Zealand This study is then extended to measure and rank the credit risk level for all industries i th substantial improvement in the post-post GFC periods In addition, this study also ey performance in relation to their credit risk Utilities, Financials and IT have achieved a t re Industrials, Energy and Consumer Discretionary sectors have had the worst ranking n on a credit requirement from the default Findings from this empirical study indicate that va for Vietnam as the case study Credit risk is generally defined as the risk that is determined t to demonstrated an important link between market risk and credit risk, which can provide an ng important insight to develop for further issues integrating these aspects hi ep With the ambition to be a financial hub in the Asia Pacific region in the regional integration and a modern industrial economy, a shift of the attention to this particular and w n important sector in Vietnam is the near future is strongly recommended lo ad y th Key words: Market risk; Credit risk; Sectors; VaR; CVaR; DD; Vietnam; Malaysia, ju yi Australia, New Zealand pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm an Lu n va ey t re th ii t to DECLARATION ng hi ep I hereby declare that the thesis entitled “Market risk versus credit risk of selected w countries in the Trans-Pacific Partnership Agreement” written and submitted by me in n lo fulfillment of the requirements for the degree of Master of Art in Development Economics ad to the Vietnam – Netherlands Programme This is also my original work and conclusions y th ju drawn are based on the material collected by me yi I further declare that this work has not been submitted to any other university for the pl n ua al award of any other degree, diploma or equivalent course va n HCMC, December 2017 ll fu oi m Quang Van Tuan at nh z z k jm ht vb om l.c gm an Lu n va ey t re th ii t to ng hi ACKNOWLEDGEMENTS ep w First of all, I would like to express my gratitude to my supervisor Dr Vo Hong Duc, n lo for his knowledge, motivation, support and for providing me enormous, valuable ad opportunities His guidance helped me at all the time of research and writing of this thesis, y th ju without him, this thesis would have never been completed yi In addition, I would like to thank Prof Nguyen Trong Hoai, Dr Pham Khanh Nam, pl research n ua al Dr Truong Dang Thuy who have provided me the valuable knowledge in the first step of va Furthermore, I would also like to thank all lecturers, staff and Mr Pham Ngoc Thach n ll fu at the Vietnam Netherlands Programme m Finally, I wish to express my greatest gratitude to my parents, my aunt and my oi nh younger sister for their unconditional encouragement, support and love on the way I have at chosen z z jm ht vb Quang Van Tuan k Ho Chi Minh City, Vietnam om l.c gm an Lu n va ey t re th iii t to CONTENTS ng hi ep ACKNOWLEDGEMENTS iii w n CONTENTS iv lo ad LIST OF TABLES vi y th ju LIST OF FIGURES vi yi pl ABBREVIATIONS vii ua al n CHAPTER va INTRODUCTION n Problem statement 1.2 The research objectives 11 1.3 Research questions 11 1.4 A choice of the countries in the Asia Pacific Region in this study 12 ll fu 1.1 oi m at nh z z CHAPTER 13 Theoretical review 13 Basel II 13 k 2.1.1 jm 2.1 ht vb LITERATURE REVIEW 13 Value at Risk 16 l.c 2.1.2 Categories of risk 15 gm 2.1.1.1 Introduction 16 2.1.2.2 The Historical method 16 2.1.2.3 The Monte Carlo simulation 17 2.1.2.4 The Variance-Covariance method 18 2.1.2.5 Comparison of VaR Methodologies 20 2.1.2.6 Limitations of VaR 21 om 2.1.2.1 an Lu Distance to Default 25 2.1.5.1 KMV-Morton Model 25 iv th 2.1.5 ey Correlation 23 t re 2.1.4 n Conditional Value at Risk 22 va 2.1.3 t to 2.1.5.2 ng 2.2 Steps in the KMV-Merton model 27 Empirical literature 28 hi ep 2.2.1 Empirical evidences on the market risk 28 2.2.2 Empirical evidences on credit risk 29 w n CHAPTER 31 lo METHEDOLOGY AND DATA 31 ad 3.1 Methodology 31 y th Value at Risk 31 ju yi Conditional Value at Risk 31 pl Equity model 32 ua al Distance to Default 33 n Hypothesis Testing 34 n va Test selection 34 Data 36 ll 3.2 fu Spearman Rank Correlation Test 34 oi m nh CHAPTER 38 at EMPIRICAL RESULTS 38 Data descriptions 38 4.2 Market Risk by VaR and CVaR Results 41 z 4.1 z vb In the GFC period (2007 - 2009) 41 4.2.2 In the post-GFC (2010 – 2016) 44 4.2.3 Ranking Shifts in Vietnam 40 k jm ht 4.2.1 gm Credit Risk by Distance to Default Results for Vietnam 44 4.4 Market risk versus Credit risk outcomes 45 om l.c 4.3 an Lu CHAPTER 48 CONCLUDING REMARKS AND POLICY IMPLICATIONS 48 Policy implications 49 5.2.2 The implications for Vietnamese government 50 5.3 The limitations and further research 51 Reference 52 v th The implications for practitioners and investors 50 ey 5.2.1 t re 5.2 n Concluding remarks 48 va 5.1 t to LIST OF TABLES ng hi ep Table Comparison of VaR methods 20 Matrix Variance-Covariance Calculation for a Two-Asset Portfoli 33 w Table Spearman Rank Correlation Test 35 n Table lo Daily commodity market price movements in Vietnam and Malaysia (2007– 2016) 39 ju y th Table Sector Breakdown 37 ad Table Daily commodity market price movements in Australia and New Zealand (2007–2016) 40 Table The level of market risk proxied by VaR using Parametric and Historical approaches for Vietnam, Malaysia, Australia and New Zealand in the GFC period (2007-2009) 42 Table The level of market risk proxied by CVaR using Parametric and Historical approaches for Vietnam, Malaysia, Australia and New Zealand in the GFC period (2007-2009) 43 Table The level of market risk proxied by VaR using Parametric and Historical approaches for Vietnam, Malaysia, Australia and New Zealand in the GFC period (2010-2016) 38 yi Table pl n ua al n va ll fu oi m at nh z z k jm ht vb Table 10 The level of market risk proxied by CVaR using Parametric and Historical approaches for Vietnam, Malaysia, Australia and New Zealand in the GFC period (2010-20016) 39 gm Table 11 VaR Ranking Shifts in Vietnam 41 l.c Table 12 CVaR Ranking Shifts in Vietnam 43 Table 13 DD Ranking Shifts in Vietnam 44 om an Lu Table 14 Market Risk proxied by Parametric and Credit Risk proxied by DD Comparison in post-GFC (2010 – 2016) 46 n va Table 15 Market Risk proxied by Historical and Credit Risk proxied by DD Comparison in post-GFC (2010 – 2016) 47 ey t re th vi t to LIST OF FIGURES ng hi ep Distribution of daily returns of NASDAQ 100 – Ticker: QQQ 17 Figure Monte Carlo simulation 100 random trials 18 Figure w n lo Figure Distribution of daily returns of NASDAQ 100 – Ticker: QQQ 19 ad VaR, CVaR, Deviations 22 Figure VaR Values Changes in Vietnam 41 Figure CVaR Values Changes in Vietnam 43 ju y th Figure yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm an Lu n va ey t re th vi t to Table 11 VaR Ranking Shifts in Vietnam Change VaR Rank GFC VaR Rank post-GFC Diff in Rank Diff in Rank2 0.0503 0.0367 0.0137 -3 Real Estate 0.1083 0.0446 0.0637 10 0.0551 0.0411 0.0139 -3 0.0677 0.0344 0.0333 64 Industrials 0.0566 0.0470 0.0097 10 -7 49 Health Care 0.0595 0.0416 0.0178 1 0.0618 0.0366 0.0252 25 hi VaR post-GFC w ng VaR GFC ep Utilities Materials n lo IT ad ju y th Financials 0.0583 0.0447 0.0137 -2 Cons Stap 0.0579 0.0358 0.0220 Cons Disc 0.0571 0.0460 0.0111 -5 25 yi Energy pl ua al 204 10 r -0.2364 t -0.6880 degree of freedom critical value 90% 1.86 critical value 95% 2.306 critical value 99% 3.355 significance - n n n va ll fu oi m at nh z z VaR Values Changes in Vietnam k Figure jm ht vb Note: Rankings are from (lowest risk) to 10 (highest risk) l.c gm VaR - Vietnam 0.12 om 0.1 an Lu 0.08 0.06 va 0.04 n 0.02 t re ey th VaR GFC VaR post-GFC 41 t to Figure illustrate actual VaR as per Table 11 The bar indicates a completely pattern, ng illustrating the difference between the GFC and post-GFC period hi ep A Spearman Rank Correlation Test is applied to determine correlation between pre- GFC and GFC with VaR rankings The difference is not significant, and thus we reject the w n null Hypothesis (H1: There is association between GFC and post-GFC in VaR ranking) lo and conclude that there is no association in industry VaR Ranking between GFC and post- ad y th GFC in Vietnam ju The market risk level using VaR, the potential losses in the future over the given time yi pl period (day or month) at a given confidential level, presented in Table above indicate that ua al industries have enjoyed a sharp reduction in the post-GFC period (2010-2016) in comparison with the GFC period (2007-2009) Industrials industry is an interesting n n va industry to be considered While the market risk level has reduced in the post-GFC period, fu market risk level of this industry is relatively smaller than other industries As a ll consequence, while the industry is ranked third in the GFC period, it is now ranked 10th, m oi the riskiest industry among all 10 industries in Vietnam, in the post GFC period Another nh at extreme, IT is ranked 9th in the GFC period, the industry has jumped into the ladder of the z market, being the “safest” industry in Vietnam after the GFC These findings highlight the z vb reality that the market risk level may have reduced, there is no guarantee the ranking, jm ht representing how relative the marker risk level of the particular industry in comparison with other industries in the same market, to remain unchanged or improved k gm In addition, Table below presents the ranking shifts in term of the market risk of l.c various industries in Vietnam between the GFC period and the post-GFC period Using om CVaR, the extreme loss has been substantially reduced across industries between the GFC an Lu period and the post GFC period, the ranking among industries in Vietnam appear to be stable Figure illustrate actual CVaR as per Table 12 The bar indicates a completely n va pattern, illustrating the difference between the GFC and post-GFC period ey t re th 42 t to Table 12 CVaR Ranking Shifts in Vietnam Change VaR Rank GFC VaR Rank post-GFC Diff in Rank Diff in 0.0934 0.0501 0.0433 1 0 Real Estate 0.5449 0.1205 0.4244 10 1 0.1327 0.0783 0.0544 -4 16 0.1643 0.0663 0.0980 4 0 Industrials 0.1706 0.1072 0.0634 -3 Health Care 0.2839 0.1210 0.1628 10 -2 0.2128 0.0558 0.1570 16 0.3014 0.0649 0.2365 36 0.0726 0.1715 0.1061 0.0368 -4 16 hi CVaR post-GFC w ng CVaR GFC ep Utilities Materials n lo IT ad y th ju Financials yi Energy 0.1429 ua al 0.2441 Cons Disc pl Cons Stap Rank2 102 n n va ll fu oi m at nh 10 r 0.382 t 1.168 degree of freedom critical value 90% 1.860 critical value 95% 2.306 critical value 99% 3.355 significance - z z Note: Rankings are from (lowest risk) to 10 (highest risk) n ht vb CVaR Values Changes in Vietnam l.c gm 0.6 k CVaR - Vietnam jm Figure 0.5 om 0.4 an Lu 0.3 0.2 n va 0.1 ey t re CVaR post-GFC 43 th CVaR GFC t to A Spearman Rank Correlation Test is applied to determine correlation between pre- ng GFC and GFC with CVaR rankings The difference is not significant, and thus we reject hi ep the null Hypothesis (H2: There is association between GFC and post-GFC in CVaR ranking) and conclude that there is no association in industry CVaR Ranking between GFC w and post-GFC in Vietnam n lo ad 4.3 Credit Risk by Distance to Default Results for Vietnam y th The results below present DD and industry ranking changes in Vietnam The two sub ju periods are presented for post-GFC (2010 – 2012) and post-post GFC (2013 - 2016) in pl al DD Ranking Shifts in Vietnam Diff in Rank Utilities 6.41 9.98 10 16 Real Estate 8.68 10.11 1.43 1 Materials 10.06 10.58 0.52 1 IT 9.07 11.22 2.15 Industrials 8.08 8.77 0.69 Health Care 9.07 10.38 1.31 1 Financials 7.59 10.53 2.94 36 Energy 7.84 8.41 0.57 10 Cons Stap 9.51 9.94 -0.43 25 Cons Disc 8.99 9.46 0.47 Change n ll fu 3.57 oi m at nh k jm ht DD post-postGFC vb DD post-postGFC z DD Post-GFC va DD PostGFC z n ua Table 13 yi Table 13 Diff in Rank2 n 10 r 0.382 om l.c gm 101 1.168 degree of freedom an Lu critical value 95% 2.306 critical value 99% 3.355 significance - ey t re 1.860 n critical value 90% va Note: Rankings are from (lowest risk) to 10 (highest risk) t th 44 t to ng hi A Spearman Rank Correlation Test is applied to determine correlation between post- ep GFC and post-post-GFC in DD rankings The difference is not significant, and thus we reject the null Hypothesis (H3: There is association between post-GFC and post-post-GFC w n in DD ranking) and conclude that there is no association in industry DD Ranking between lo post-GFC and post-post-GFC in DD rankings in Vietnam ad y th The credit risk is proxied by the DD in Table 8, which is designed to estimate the ju Default occurring when the value of company's asset falls below the value of debt In the yi pl post-GFC, Materials, IT and Consumer Discretionary belongs to the safe group and ua al Utilities, Financials and Energy are in highest risk group in Vietnam However, in the postpost-GFC, estimations provide inversely result: Materials, IT and Financial are in the n ll fu highest risk group n va lowest risk group, and Industrial, Consumer Discretionary and Energy belongs to the oi m It is a surprise for Industrials, Energy and Consumer Discretionary had the worst nh ranking movement Utilities, Financials and IT achieve the substantial enhance after in the at post-post GFC periods z z 4.4 Market risk versus Credit risk outcomes vb jm ht This section considers is whether there is any association between market risk and k credit risk ranking in Vietnam To test for association, DD ranking are correlated with two gm approaches of the Market risk (Parametric and Historical) for the post-GFC period (2010 om market risk and credit risk ranking in Vietnam l.c – 2016) A Spearman Rank Correlation Test is applied to determine correlation between an Lu The difference in Table 14 is significant at 90%, and thus we accept the null Hypothesis (H4: There is association between market risk and credit risk ranking) and n ey t re risk proxied Distance to Default in Vietnam va conclude that there is association between market risk proxied by Parametric and credit th 45 t to ng hi Table 14 ep Market Risk proxied by Parametric and Credit Risk proxied by DD Comparison in post-GFC (2010 – 2016), Vietnam Parametric VaR w n Utilities lo Real Estate ad Materials Ranking DD Diff in Rank Diff in Rank2 8.27 25 0.0446 9.56 1 0.0411 10.35 16 0.0344 9.84 0.0470 7.92 10 10 0 10.29 16 9.71 9.36 8 0 9.74 4 9.40 ju Industrials Ranking VaR 0.0367 y th IT DD Post-GFC yi Financials 0.0366 Energy 0.0447 Cons Stap 0.0358 Cons Disc 0.0460 n ua al n va 0.0416 pl Health Care 74 fu n 10 0.55 t 1.87 degree of freedom critical value 90% 1.860 critical value 95% 2.306 critical value 99% 3.355 significance * ll r oi m at nh z z k jm ht vb Note: Rankings are from (lowest risk) to 10 (highest risk) om l.c gm an Lu n va ey t re th 46 t to ng Table 15 Market Risk proxied by Historical and Credit Risk proxied by DD Comparison in post-GFC (2010 – 2016), Vietnam hi ep Historical VaR Ranking VaR Ranking DD Diff in Rank Diff in Rank2 0.0363 8.27 9 Real Estate 0.0347 9.56 Materials 0.0385 10.35 1 36 IT 0.0304 9.84 0.0427 7.92 10 0.0344 10.29 1 Financials 0.0356 9.71 5 Energy 0.0442 9.36 10 Cons Stap 0.0308 9.74 4 Cons Disc 0.0419 9.40 1 w Utilities DD n lo ad ju yi Health Care y th Industrials pl n ua al va n 64 n 10 0.61 t 2.19 degree of freedom critical value 90% 1.860 critical value 95% 2.306 critical value 99% 3.355 significance * ll fu r oi m at nh z z jm ht vb Note: Rankings are from (lowest risk) to 10 (highest risk) k The difference is significant at 90%, and thus we accept the null Hypothesis (H4: gm There is association between market risk and credit risk ranking) and conclude that there l.c is association between market risk proxied by Historical and credit risk proxied Distance om to Default in Vietnam an Lu The results are demonstrated in Table 14 and 15 reveling that correlation is found at n industries are risky from a market risk also risky from a credit risk in Vietnam va the 90% level It means there is a degree of similarity among those industries and those ey t re th 47 t to CHAPTER ng hi CONCLUDING REMARKS AND POLICY IMPLICATIONS ep In this chapter, we will summarize the key conclusions We will then accommodate w practitioners, investors, policy makers with considerable contributions and policy n lo implications from the empirical results Finally is limitations and recommendations for ad further research y th ju 5.1 Concluding remarks yi pl Vietnam has emerged as a new economic engine for the Southeast Asian region with ua al many important industries The three pillars contributing the most value to the Vietnamese n economy over the last decade or so are agriculture, manufacturing, and food & beverage va In order to maximize the potential benefits from the partnership with any country around n ll fu the world, it is time to recognize the important role of sectorial risk, in particular, for key oi m sectors (industries) relatively to similar sectors from Malaysia, Australia and New Zealand nh This study is conducted to measure the level of the market risk at the sectoral levels at which has attracted great attention from academia, investment bankers, and policymakers z z for 10 industries/sectors in Vietnam, Malaysia, Australia and New Zealand (selected vb countries in the Asia Pacific Region) Two periods are considered, including: (i) the GFC ht jm period (2007-2009); and (ii) the post-GFC period (2010-2016) The market risk level is k measured using the parametric approach and the historical approach for both Value at Risk gm (VaR), the potential losses in the future over the given time period at a given confidential l.c level, and Conditional Value at Risk (CVaR), which is designed to estimate the risk of om extreme loss This includes enhancing understanding of VaR, CVaR and industry risk, as an Lu well as providing its methodologies 48 th This study achieves some key findings can be summarized as below: ey by Distance to Default; and market risk, proxied by VaR and CVaR t re selected for this purpose The study is then to provide a link between credit risk, proxied n As a case study, Vietnam is the only country from the sample of nations to have been va In addition, the credit risk is also considered using the distance to default approach t to  First, the market risk level using VaR for Vietnam’s industries has exhibited a ng sharp reduction in the post-GFC period in comparison with the GFC period These hi ep findings highlight the reality that the market risk level may have reduced, there is no guarantee the ranking, representing how relative the marker risk level of the w particular industry in comparison with other industries in the same market, to n lo remain unchanged or improved However, CVaR has been substantially reduced ad across industries between the GFC period and the post GFC period, the ranking y th among industries appear to be stable ju yi  Second, findings from this study confirm that Vietnam’s sectors are relatively pl riskier than their counterparts in Malaysia, Australia and New Zealand and that al n ua the market risk level across sectors in these countries has substantially reduced in va the post-GFC period Financials including Banks, Diversified Financials, and n Insurance have been largely ignored from the Vietnamese Government’s focus fu ll Moreover, IT industry is considered very low risk in Vietnam whereas this sector m oi belongs to a group of high market risk in Malaysia, Australia, and New Zealand nh at  Third, the credit risk is measured using Distance to Default for various industries z in Vietnam The empirical results from this study indicate that Industrials, Energy z vb and Consumer Discretionary have had the worst ranking movement in relation to jm ht their distance to default in comparison with other sectors of the Vietnamese economy Utilities, Financials and IT have achieved the substantial improvement k l.c gm in the post-post GFC periods  Fourth, this study also demonstrated an important link between credit risk, om proxied by distance to default, and mảket risk, proxied by VaR, at least in the an Lu context of Vietnam 49 th investors as well as for the Vietnamese Government ey risk and credit risk According to these findings, some policy implications are provided for t re The previous section had provided various important findings in relation to market n va 5.2 Policy implications t to 5.2.1 The implications for practitioners and investors ng hi  This research may provide some substantial benefits to practitioners This ep also contains enhancing knowledge and understanding of VaR, CVaR, DD as well as its methodologies w n  Besides, based on the empirical results of this study, investors are able to lo ad consider their investment strategy in developing countries as Vietnam, ju y th Malaysia as well as developed countries Australia and New Zealand yi 5.2.2 The implications for Vietnamese government pl  First, this particular industry is considered relatively risky in Vietnam al n ua whereas it is ranked as a very safe sector in Malaysia in the GFC period With va the ambition to be a financial hub in the Asia Pacific region in the regional n integration and a modern industrial economy, a shift of the attention to this fu ll particular and important sector in Vietnam in the near future is strongly oi nh Second, the empirical results indicate that IT which indicate that it is a safe at  m recommended z industry in Vietnam whereas this sector belongs to a group of high market z vb risk in others and highest risk in Industrial in the post-GFC Therefore, jm ht Government should consider, recognize weaknesses and strengths of k Vietnam, after that adopt strategies to adapt the revolution and modify the l.c  gm economy Third, the Government should develop a new approach such as creating om motivation, reasonable conditions, and legal frameworks for enterprises to an Lu highly make our national economy adaptable to The Fourth Industrial Revolution with the strength in IT industry demonstrated in the empirical va receives the potential impact So that, the Government should raise consideration when determine policies to manage the risk in market 50 th Therefore, any decision influencing the market risk, the credit risk also ey Fourth, because of the correlation between market risk and credit risk t re  n results t to 5.3 The limitations and further research ng hi Although this research has deliberated about market risk proxied VaR, CVaR and ep credit risk proxied DD This section will present some limitations and further studies are also discussed w n First, data employed to provide the estimation for market risk in four selected lo ad countries and one country for credit risk In some circumstances, it cannot provide critical ju y th analysis for the long-term decision Although the assessing data is very complicated and difficult to obtain, this research is produced with the hope that the investors and yi pl Government are aware of the important role of risk al ua Second, the limitation belongs to the methodology VaR is not coherent risk n measurement, but this problem is not apparent in CVaR The parametric approach basing va n on a normal distribution, this reason may underestimate the risk of portfolio In additional, fu ll the assumption on time horizon (T=1) in DD model oi m at nh z z k jm ht vb om l.c gm an Lu n va ey t re th 51 t to Reference ng hi Acerbi, C., & Tasche, D (2002) On the coherence of expected shortfall Journal of ep Banking & Finance, 26(7), 1487-1503 Agrawal, K., & Maheshwari, Y (2016) Predicting financial distress: revisiting the option w n based model South Asian Journal of Global Business Research, 5(2), 268 – 284 lo ad Albanese, C (1997) Credit exposure, diversification risk and coherent VaR Preprint, y th Department of Mathematics, University of Toronto ju Alexander, S., T.F Coleman, and Y Li (2006) "Minimizing CVaR and VaR for a Portfolio yi of Derivatives." Journal of Banking & Finance, 30 583-605 pl al Allen, D E., & Powell, R (2012) The fluctuating default risk of Australian banks n ua Australian Journal of Management, 37(2), 297 va Allen, D.E., and R.J Powell (2011) "Measuring and Optimising Extreme Sectoral Risk n in Australia." Asia Pacific Journal of Economics and Business, 15, 1-14 fu ll Artner, P., Delbaen, F., Eber J M., & Health, D (1999) Coherent Measures of Risk oi m Mathematical Finance, 9(3), 203 – 228 nh Basel Committee on Banking Supervision (BCBS) (2016) Basel II: International at z convergence of capital measurement and capital standards: a revised framework - z comprehensive version Retrieved from http://www.bis.org/publ/bcbs128.htm vb jm ht Bharath, S.T., & Shumway, T (2008) Forecasting default with the Merton distance to default Model The Review of Financial Studies 21(3), 1339-1369 k gm Black, F., & Scholes, M (1973) The pricing of options and corporate liabilities Journal l.c of political economy, 81(3), 637-654 Journal of Alternative Investments, 8(4), 39-47 om Bystrom, H.N (2006) Merton unraveled: a flexible way of modeling default risk The an Lu Choudhry, M (2004) Fixed Income Markets Instruments, Applications, Mathematics t re ey Trading and Hedging John Wiley & Sons http://www.moodyskmv.com/research/files/wp/ModelingDefaultRisk.pdf 52 th Crosbie, P., & Bohn, J (2003) Modeling default risk Crosbie, P., & Bohn, J (2003) Modelling Default Risk Retrieved n Choudhry, M., Moskovic, D., & Wong, M (2014) Fixed Income Markets: Management, va Singapore: John Wiley & Sons (Asia) Pte Ltd from t to Deal, J A., & Harper, W L (2004) Marbled murrelet nesting habitat conservation plan ng for the Nimpkish Valley, north central Vancouver Island In Proceedings of the hi ep species at risk 2004 pathways to recovery conference Duffie, D., & Pan, J (1997) An overview of Value at Risk Journal of Derivatives, 4(3), w – 49 n lo Fretheima, T, and G Kristiansena (2015) "Commodity Market Risk from 1995 to 2013: ad An Extreme Value Theory Approach." Applied Economics, 47, 2768-2782 y th Gaivoronski, A A., & Pflug, G C (2000) Properties and computation of value at risk ju yi efficient portfolios based on historical data Department of Industrial Economics and pl Technology Management, NTNU, Working Paper, 5(00) al ua Glasserman, P., Heidelberger, P., & Shahabuddin, P (2000) Efficient Monte Carlo n methods for value-at-risk IBM Thomas J Watson Research Division va n Gourieroux, C., Laurent, J P., & Scaillet, O (2000) Sensitivity analysis of values at fu risk Journal of empirical finance, 7(3), 225-245 ll oi m Hogan, W., Avram, K., Brown, C., Degabrielle, R., Ralston, D., Skully, M., et al nh (2004) Management of Financial Institutions Milton: John Wiley & Sons at Huang, F., & He, Y (2010) Enactment of default point in KMV model on CMBC, SPDB, z z CMB Huaxia Bank and SDB International Journal of Financial Research, 1(1), 30- vb 36 ht jm J.P Morgan, & Reuters (1996) RiskMetrics Technical Document k Jorion, P (1996) Risk2: Measuring the risk in value at risk Financial Analysts gm Journal, 52(6), 47-56 l.c Jorion, P (2007), “Value at risk: The new bench mark for controlling market risk, Third om Edition”, McGraw-Hill an Lu Koutsomanoli-Filippaki, A., & Mamatzakis, E (2009) Performance and merton-type default risk of listed banks in the EU: a panel VAR approach Journal of Banking & va n Finance, 33(11), 2050-2061 ey Spillovers Among Major Agricultural Commodities." The Journal of Applied t re Lahiani, A., D Nguyen, and D Vo (2013) "Understanding Return and Volatility th Business Research 29, 1781-1790 Mausser, H., & Rosen, D (1999) Beyond VaR: From measuring risk to managing risk 53 t to In Computational Intelligence for Financial Engineering, 1999.(CIFEr) Proceedings ng of the IEEE/IAFE 1999 Conference on (pp 163-178) IEEE hi ep McKay, R., & Keefer, T E (1996) VaR is a dangerous technique Corporate Finance Searching for Systems Integration Supplement Sep, 30 w Merton, R C (1974) On the pricing of corporate debt: The risk structure of interest rates n lo The Journal of finance, 29(2), 449-470 ad Morgan, J P (1996) Reuters (1996) RiskMetrics Technical Document Retrieved from y th the World Wide Web: www jpmorgan com ju yi Palmquist, J., Uryasev, S., & Krokhmal, P (1999) Portfolio optimization with conditional pl value-at-risk objective and constraints Department of Industrial & Systems al ua Engineering, University of Florida n Patel, K., & Vlamis, P (2006) An empirical estimation of default risk of the UK real estate va n companies The Journal of Real Estate Finance and Economics, 32(1), 21-40 fu Pflug, G (2000), "Some Remarks on Value-at-Risk and Conditional-Value-at-Risk." in ll oi m Probabilistic Constrained Optimisation: Methodology and Applications, edited by R nh Uryasev Dordrecht, Boston: Kluwer Academic Publishers at Pham, T (2015) The capital asset pricing models: Beta and what else Master’s thesis, z R (2007) Industry value at risk in vb Powell, z Vietnam – The Netherlands Programme Australia Retrieved from ht jm http://ro.ecu.edu.au/theses/297 k Powell, R., Vo, D., & Pham, T (2016a) The Great Agricultural Commodities l.c gm Triathlon Working Paper, Edith Cowan University Powell, R., Vo, D., & Pham, T (2016b) The long and short of commodity tails and om their relationship to Asian equity markets Working Paper, Edith Cowan University an Lu Powell, R., Vo, D., & Pham, T (2016c) Economic Cycles and Downside Commodities Risk Working Paper, Edith Cowan University 54 th Rockafellar, R T., & Uryasev, S (2000) Optimization of conditional value-at-risk Journal ey Distributions." Journal of Banking and Finance, 26, 1443-1471 t re Rockafellar, R T, and S Uryasev (2002) "Conditional Value-at-Risk for General Loss n computational time Journal of Financial Services Research, 12(2-3), 201-242 va Pritsker, M (1997) Evaluating value at risk methodologies: accuracy versus t to of risk, 2, 21-42 ng Saggu, A, and W Anukoonwattaka (2015) "Global Commodity Price Falls: A Transitory hi ep Boost to Economic Growth in Asia Pacific Countries with Special Needs." United Nations’ Economic and Social Commission for Asia and the Pacific ESCAP, Trade w Insights, March, 1-9 n lo Sarykalin, S., Serraino, G., & Uryasev, S (2008) Value-at-risk vs conditional value-at- ad risk in risk management and optimization Tutorials in Operations Research y th INFORMS, Hanover, MD, 270-294 ju yi Siegel, S., & Castellan Jr, N J (1988) The Kruskal-Wallis one-way analysis of variance pl by ranks Nonparametric Statistics for the Behavioural Sciences, 206-214 al ua Singh, A K., Allen, D E., & Powell, R J (2014) Modelling and Forecasting Intraday n Market Risk with Application to Stock Indices Available at SSRN 2395610 va n Uryasev, S, and R T Rockafellar (2000) "Optimisation of Conditional Value-at-Risk." ll fu Journal of Risk, 2, 21-41 oi m Yamai, Y., & Yoshiba, T (2002) Comparative analyses of expected shortfall and value- nh at-risk (2): expected utility maximization and tail risk In MONETARY AND at ECONOMIC STUDIES/APRIL 2002 z z k jm ht vb om l.c gm an Lu n va ey t re th 55

Ngày đăng: 15/08/2023, 14:54

w