Macroeconomic, financial and institutional determinants of banking crisis the money market pressure index approach

INTRODUCTION

Problem statement

The ongoing banking crisis in modern economies is a longstanding issue that has been extensively analyzed from various perspectives Research on banking crises has evolved through three key trends: the initial qualitative descriptions by Friedman and Schwartz (1963) regarding historical U.S crises, the subsequent use of econometric analysis with panel data, and the recent focus following the 2007 global financial turmoil These studies have identified crucial macroeconomic and banking sector indicators, such as reserves, current accounts, and real exchange rates (Kaminsky et al., 1998) While logistic regression has emerged as a valuable quantitative tool for predicting crisis signals and identifying significant indicators, challenges remain due to potential noise affecting its effectiveness This has prompted further research aimed at developing new methodologies and identifying critical variables.

As suggested, there have been many criteria to help researchers with banking crisis identification Amongst, money market pressure index from the work of Hagen and

Ho (2007) builds upon the work of Eichengreen, Rose, and Wyplosz (1995, 1996a, 1996b) regarding currency crises, offering a useful framework for understanding and gathering data on banking crises This index identifies periods when banking systems face liquidity issues by analyzing both high demand for central bank reserves and fluctuations in short-term real interest rates It serves as a criterion to determine the presence of a crisis within the examined scope.

Obtaining precise data on banks can be challenging due to its sensitive nature To address this, the research will utilize macroeconomic indicators, as highlighted by Quagliariello (2008), which suggest that analyzing these variables aids banking supervisors in evaluating banks' health Following the recommendations of Quagliariello (2008) and Hagen and Ho (2007), the study will examine various macroeconomic and financial variables, including inflation, monetary base growth, depreciation, real interest rates, private credit growth relative to GDP, deposit growth over GDP, and M2 growth over reserves Additionally, recent studies have incorporated institutional signals (Kaufmann et al., 2008) to predict vulnerabilities and crises, enhancing the model limitations identified by Kaminsky et al (1998) Inspired by Breuer et al (2006), this research will also integrate six updated world governance indicators (Kaufmann, 2013)—voice and accountability, government effectiveness, political stability, rule of law, regulatory quality, and control of corruption—to evaluate the impact of government health on banking system crises Furthermore, incorporating a 12-month lagged term of banking crises, as suggested by Falcetti and Tudela (2006), will provide valuable insights into the assessment.

While much research focuses on the causes of banking crises, there is a significant gap in understanding why some countries experience stability during certain periods Investigating the factors that contribute to a stable banking environment is crucial, as it can enhance our ability to forecast potential crises Future studies should emphasize the importance of analyzing non-crisis situations within the banking system, as these periods offer valuable insights that are often overlooked (Kauko, 2014).

Despite numerous studies on banking crises, there is a noticeable lack of research that simultaneously examines the health of government, macroeconomic factors, and financial conditions within a single model This gap highlights the need for a comprehensive approach to understanding the interconnectedness of these elements in the context of banking stability.

This thesis aims to utilize a combination of the MMP index approach and updated IMF-IFS data from 2001 to 2010 to analyze the overall phenomenon of banking crises, considering the influences of the macroeconomic environment, financial conditions, and institutional indicators This approach is expected to yield valuable insights for understanding banking crises and enhance awareness among authorities regarding the management of the banking sector and the broader economy.

Research objective

This thesis aims to provide updated research on benign periods within banking systems by analyzing banking crises It will focus on identifying macroeconomic, financial, and institutional factors that help explain the occurrence of these banking crises.

Research question

Which are the macroeconomic, financial and institutional indicators that provide awareness for the crisis time of banking system?

Structure of the thesis

After the finish of Chapter 1 about thesis introduction, the rest of this thesis will be categorized as following chapters:

Chapter 2 introduces banking crisis definition, relevant literature reviews of trends of banking crisis researches, money market pressure index which will be applied for banking crisis dependent variable identification.

Chapter 3 states the methodology, model choice and specification and data scope used This chapter also gives readers clear arguments on explanatory variables used, suggested statistical diagnostics of significance of model and variables. Simultaneously, data scope and sources together with model conceptual framework and analytical framework are also declared.

Chapter 4 interprets results and findings of thesis regression model.

Chapter 5 concludes with policy recommendation, thesis limitation and further research suggestion.

LITERATURE REVIEW

Defining banking crisis

According to the International Monetary Fund (IMF) definition from 1998, a banking crisis occurs when bank runs and widespread failures lead banks to halt the convertibility of their liabilities, necessitating significant government intervention in the banking system.

In another work of Demirgtic-Kunt and Detragiache (1998), the concept of banking crisis was defined as event method whose conditions are that one or the entire following phenomenon holds:

1) The existence of at least 10% of the ratio of non-performing assets over total assets in the banking system.

2) Cost of the rescue packages reached at least 2% of GDP.

3) Extensive nationalization of banks due to banking sector problems.

4) Governmental regulation of deposit guarantee, large-scale bank runs, long holidays of banks, deposit freeze.

The definition of a banking crisis has notable drawbacks, including the unclear costs of government rescue packages, which often delay the identification of crises until after they occur Nationalization of banks and prolonged bank holidays typically follow significant economic downturns, complicating assessments of government intervention Additionally, the timing of governmental actions can be inconsistent, leading to uncertainty in identifying the precise dates of crises The event method used to classify crises relies on severe market events, resulting in biased identification based on policy responses This bias ultimately restricts the ability to accurately analyze the determinants of banking crises.

With the attempt to contribute an alternative identification for banking crisis, the money market pressure index (MMP) was built up in the work of Hagen and Ho

In their analysis of currency crises, Hagen and Ho (2007) define a banking crisis as a period marked by excessive liquidity demand in the money market This concept stems from the traditional view that short-term interest rates negatively influence the banking sector's demand for central bank reserves The authors propose that a banking crisis is characterized by a significant increase in the aggregate demand for these reserves, which can be examined through three key reasons.

- Banks confront with increasing non-performing loans and/or significant decline in bank loans quality leading to illiquidity, hence, a rising in demand of reserves to retain liquidity.

- When sudden withdrawals occur, there will be a pressure for banks to deal with interbank market and central bank to be refinanced.

- Government bonds and other more guaranteed assets are favored by financial institutions rather than lending to those in troubled leading to “a drying up of inter-bank lending”.

In response to the rising demand for reserves, central banks, as the ultimate lenders, will implement two key policies: targeting bank reserves or short-term interest rates If the focus is on short-term interest rates, we can expect an increase in these rates Conversely, targeting bank reserves will necessitate injecting reserves into the banking system through open market operations (OMO) or discount window lending Consequently, either a significant rise in short-term interest rates or an increase in central bank reserves, or both, indicates that the money market is experiencing high pressure Therefore, the money market pressure index can effectively highlight vulnerabilities within the banking sector.

The index is calculated as a weighted average of the variations in the ratio of reserves to bank deposits and the fluctuations in the short-term real interest rate, with the weights determined by the sample standard deviations of these two components (Hagen and Ho, 2007).

The reserves to bank deposits ratio, denoted as "where," tends to rise during periods of high tension in the money market, particularly when the central bank injects reserves into the banking system or when depositors make withdrawals The variable "r" represents the short-term real interest rate, while the terms and their respective standard deviations reflect the variability of these two components.

The judgment for banking crisis (BC) will be shown below:

This chapter concludes its exploration of banking crises by outlining three key research trends that offer a comprehensive overview of existing empirical studies Additionally, it includes a thorough review of the methodologies and findings from Hagen's research, providing valuable insights into the dynamics of banking crises.

Ho (2007) as a conjunction for the Chapter 3.

Trends of banking crises researchtogether with crises mechanism

The history of banking system fragility reveals significant insights, starting with the qualitative analysis of the US crisis by Friedman and Schwartz in 1963, which laid the groundwork for understanding financial instability This analysis is complemented by the first comprehensive banking crisis database created by Caprio and Klingebiel in 1996, marking a pivotal moment in the study of banking crises.

Banking crises, as highlighted by Demirguc-Kunt and Detragiache (1998) and Kaminsky and Reinhart (1999), are primarily driven by factors such as economic health, government policies, the inherent fragility of the banking system, and external contagion effects Despite these common underlying causes, each crisis period requires a unique assessment approach, influenced by the available data, analytical techniques, and statistical software support This article aims to explore existing trends and methodologies in banking crisis research, providing a comprehensive overview of the mechanisms involved Additionally, it discusses various approaches to crisis assessment, referencing Kauko's (2014) categorized suggestions.

Description of specific historic events is mainstream of the first trend of banking crisis analyses The below words introduce some authors of this trend.

Friedman and Schwartz (1963) in "Monetary History of the United States, 1867–1960" observed that bank runs were linked to rising short-term interest rates and a declining deposit-to-currency ratio As noted by Waldo (1985), banks often responded to withdrawal demands by prematurely selling long-term securities, which increased short-term asset yields This tradeoff between maintaining liquidity and selling securities led some banks to default on deposits, prompting depositors to withdraw cash to protect themselves against potential bank runs The banking crisis of October 1930 exemplified this issue, as bank failures led to a widespread rush to convert deposits into cash, ultimately resulting in the collapse of the US banking system by December 1930 The situation worsened from March to June 1931, marking a second, more severe wave of crisis due to the lingering instability from the previous turmoil.

Herrala (2011) contributes a description on Finnish crisis within the scope of 1865

The 1998 study examines the profitability of banks in Finland through case studies, revealing that the events leading to the banking crisis align with previous international research It defines a banking crisis as an incidental occurrence of negative profitability within the sector and analyzes statistical data to identify critical characteristics and cycles of banking crises that may undermine financial stability By comparing findings from international banking crises, the study seeks indicators that signal the advance phase of a banking crisis cycle, even when the financial conditions appear healthy Key factors influencing bank profitability, including total assets, real GDP growth, investment levels, inflation, export volume changes, stock money, exchange rates, interest rates, and the ratio of bank deposits to loans, are also considered.

In his 1988 study, Gorton examined the econometric factors contributing to banking panics in the United States during the National Banking Era (1863-1914), focusing on depositor behavior and the implications of moral hazard The research highlighted that shifts in depositor risk perceptions could trigger banking panics, with key indicators including deposit ratios and liabilities playing a significant role in this dynamic.

Such econometric based researches made a link between the first trend and the second trend which will be introduced below.

Econometric research utilizing panel data has been conducted to analyze banking crises, primarily focusing on macroeconomic and financial factors While studies often examine samples from numerous countries over extended periods, they tend to concentrate on developed nations Furthermore, these analyses typically categorize crises into a binary framework, reflecting a dichotomous nature that only considers the presence or absence of a crisis, as noted in various scholarly discussions.

The banking crises phenomenon has garnered significant attention, particularly through the foundational work of Caprio and Klingebiel (1996a, 1996b), who created the first comprehensive banking crisis database, detailing crisis dates, countries, and key economic variables, alongside policy measures Their research focused on bank insolvency and highlighted the need for more extensive data collection, including GDP, inflation, monetary growth, and other financial indicators, from 69 countries spanning 1970 to 1996 In-depth expert interviews were conducted to document crisis episodes; however, Caprio and Klingebiel emphasized the necessity of incorporating additional bank performance and development indicators to enhance the accuracy of crisis prediction for individual banks and the overall system Furthermore, political economy research was recommended as a valuable resource for governments addressing bank insolvency issues.

The twin crisis concept refers to the simultaneous occurrence of currency and banking crises, evaluated through a signal-to-noise approach that determines crisis situations based on threshold values for various indicators (Kaminsky and Reinhart, 1999) In this analysis, 16 indicators spanning the financial, external, real, and fiscal sectors were utilized to assess individual banking crises and twin crises collectively Despite some drawbacks related to false signals, early warnings can provide valuable insights for authorities The study examined 20 countries from 1970 to mid-1995, including both industrial and developing nations such as Denmark, Argentina, and Thailand, allowing for the analysis of 76 currency crises and 26 banking crises, as referenced in Caprio and Klingebiel (1996) Additionally, the research included out-of-sample testing of the 1997 twin crises in Asia.

Dermirguc-Kunt and Detragiache (1998) conducted a comprehensive analysis of systemic banking crises across developed and developing countries from 1980 to 1994 using a multivariate logistic model Their findings revealed that crises often arise in weak macroeconomic environments characterized by high inflation and low growth, with elevated real interest rates exacerbating issues within the banking sector Institutional factors, such as the presence of deposit insurance and ineffective law enforcement, were identified as risks to banking stability The study highlighted the critical role of low GDP growth in heightening banking sector vulnerability, noting that while banks typically engage in risk-taking, they may overlook domestic credit risks by lending internationally This behavior, while beneficial to some developing nations, pressures authorities to enhance regulatory frameworks to mitigate banking fragility stemming from cross-border activities The research also addressed the contentious impact of financial liberalization on banking stability, suggesting that controlled real interest rates during liberalization periods may correlate with increased banking crisis likelihood However, limitations in the estimation model and the interplay between macroeconomic, institutional, and financial factors were acknowledged, leading to recommendations for further investigation into banking structural indicators, including bank capitalization, market concentration, credit market competition, interbank liquidity, ownership structures, and regulatory quality.

Broad new and old samples of banking crisis over different countries have been combined in some researches However, once again, these analyses on focused on developed countries.

Bordo and Meissner (2012) conducted an analysis of 14 advanced countries from 1880 to 2008 to explore the relationship between credit booms, inequality, housing policy, and banking crises Their study utilized a logit model, revealing a positive correlation between credit booms and banking instability, particularly noting that a rise in real credit growth over two to five years prior significantly increased the likelihood of banking crises While the lag term of one year showed a low probability of crisis, the findings highlighted the importance of factors such as rising real income and declining interest rates in understanding credit booms However, the research did not find substantial evidence that increasing income inequality or housing redistributive policies significantly contributed to the risk of financial crises.

Schularick and Taylor (2012) explored the significant impact of previously overlooked credit expansion on the global economy, highlighting that the stable relationship between money and credit established post-Great Depression and World War II continues to influence current financial crises They noted that the complex macroeconomic environment and evolving financial policies, including the increase of fiat money and banks acting as

Jorda et al (2011) examined financial fragility in relation to external economic imbalances, such as current account deterioration, loan growth, interest rate volatility, inflation, and GDP growth, using a logistic country fixed effect model over 140 years in 14 developed countries Their analysis combined descriptive statistics with an econometric logit model, revealing that loan growth significantly accelerates crises on both national and global levels Deteriorating current accounts were found to be a critical precursor to crises, impacting individual nations and the global economy alike Notably, suppressed natural interest rates signaled impending crises, particularly during four major global downturns: 1890, 1907, 1930-1931, and 2007-2008, with real interest rates and inflation also serving as predictive indicators The study concluded that policymakers should focus on the build-up phase of crises by monitoring external macroeconomic imbalances, highlighting the significant interplay between credit growth and current accounts as predictors of financial instability based on historical and recent trends.

Since the 2007 global financial crisis and the subprime Lehman collapse, cross-country analyses have gained prominence, focusing on the effects of these events across various sectors Key areas of investigation include the financial sector's impact on the real economy and the use of diverse indicators to assess vulnerabilities within banking systems Each of these research dimensions will be examined individually.

Since Gorton’s (1988) analysis of banking crises, there has been an increasing interest among economists in understanding the macroeconomic and banking sector factors that contribute to banking system fragilities Kauko (2012) examined the link between current account deficits and banking vulnerabilities, focusing on the deterioration of credit quality as a direct crisis factor Non-performing loans, both at the individual bank level and across the banking system, serve as critical indicators of bank health, as they negatively impact profitability Excessive credit growth often precedes banking crises, and problematic credit growth can be indicated by high levels of foreign debt The study analyzed the relationship between the macroeconomic environment and credit quality during recent crises, using the relative amount of non-performing loans from 2009, as reported by the IMF, across 34 advanced countries The findings revealed that credit growth combined with current account deficits is a significant predictor of financial crises.

The study by Aizenman and Pasricha (2012) investigates the spillover effects of the 2007 financial crisis on 107 countries, analyzing financial crisis episodes from 2008-2009 using ordinary least squares regression Key variables identified include per capita real GDP, the international reserves-to-GDP ratio, a dummy variable for countries receiving swap lines from major central banks, trade-to-GDP ratio, commodity exporter status, and capital flow restrictions measured by the Chinn-Ito index The research distinguishes between internal and external financial stress, highlighting that countries with greater openness experienced larger shocks, while those with competitive banking systems were less vulnerable when well-capitalized or supervised Additionally, it found that countries with higher international reserves faced greater external stress, whereas commodity exporters experienced lower internal stress.

Money Market Pressure (MMP) Index (Hagen and Ho, 2007)

The MMP index is utilized to identify banking crises by applying specific criteria for each country A crisis is recognized when the index surpasses the 98.5 percentile of its sample distribution and its growth rate exceeds 5% This approach, as discussed by Hagen and Ho (2007), ensures that only significant crises are acknowledged while preventing the misclassification of non-crisis periods Relaxing the first criterion may result in an overestimation of crises, whereas tightening it could overlook genuine crises Additionally, altering the percentile value affects the regression model's explanatory power, with a lower percentile (95) diminishing accuracy and a higher percentile (99.5) showing negligible change The second criterion's strictness can also lead to the omission of actual crises Although some may argue for a universal definition across countries, the variability in MMP index fluctuations suggests that pooled data could obscure true crises in countries with lower volatility Ultimately, the percentile method is favored over multiple standard deviations due to the MMP index's non-normal distribution characteristics.

The definition of banking crises presents several drawbacks: firstly, while these crises are often viewed as asset-driven rather than liability-driven, the thesis does not address the increased demand for reserves resulting from declining bank assets Secondly, the MMP index may not be applicable in countries where central banks control interest rates; however, it remains advantageous as it operates independently of interest rate flexibility when central banks base their management on market measures Lastly, the MMP index can signal the onset of a banking crisis but cannot determine its conclusion, as identifying the end of such crises remains a challenging issue in empirical crisis literature, a topic that falls outside the scope of this thesis.

The analysis covered a sample of 47 countries from 1980 to 2001, selected based on data availability from the IMF, with Argentina and Brazil excluded due to their extreme inflation and interest rates The study yielded between 697 and 726 observations, identifying approximately 5% of these as crisis episodes Explanatory indicators were derived from existing literature and available data, employing a conditional fixed effects model to assess banking crises using the M

Chapter summary

After all related literatures are introduced this section gives brief summary in Table 2.1 according to the order of structure written above.

The banking crisis, as defined in this thesis, stems from fluctuations in the MMP index, which highlights vulnerabilities in the banking sector This index is calculated by assessing the weighted average of changes in the ratio of reserves to bank deposits alongside shifts in short-term real interest rates (Hagen and Ho, 2007).

Figure 2.1 illustrates the factors contributing to banking crises, highlighting the interplay between macroeconomic conditions, financial stability, and institutional efficiency A banking system operating within a weak macroeconomic environment, coupled with banks' fragile financial positions and ineffective institutions, significantly increases the risk of a banking crisis.

Table 2.1 Summary of literature reviewed

Author(s) Key indicators Methodology Data sample Findings

Bank run, short-term interest rate, bank deposit

The financial stability of banks is often assessed by their ability to withdraw funds, which can lead to the premature sale of long-term securities and an increase in short-term asset yields This situation may cause some banks to default on deposits, prompting depositors to quickly convert their funds into cash as a precaution against potential bank runs.

- The later wave of banking crisis occurred more severe because the banking system had been unhealthy during the former crisis.

The analysis of macroeconomic indicators, including real GDP growth, export volume changes, investment trends, inflation rates, money supply, exchange rates, interest rates, and the relationship between total assets and loans, was conducted using a descriptive statistics approach to identify the characteristics of banking crises.

- Banks’ profitability over total assets has been taken into account to analyze for the deterioration of banks’ financial condition which may lead to banking crisis cycles. Gorton

(1988) Banking panics and the depositors’ behaviors

Between 1863 and 1914, research highlighted that banking panics could stem from shifts in depositors' risk perceptions Key indicators, including deposit ratios and liabilities, were analyzed to understand these dynamics.

The Caprio and Klingebiel study on the insolvency of banks across 69 countries highlights the critical relationship between weak macroeconomic conditions and banking crises By analyzing data from 1970 to 1996, including GDP, inflation rates, and fiscal policies, the research underscores how deteriorating financial health and imbalances can precipitate banking crises The findings emphasize the importance of monitoring real credit-to-GDP ratios and deposit rates as indicators of banking stability, particularly during periods of economic downturn.

16 indicators from financial sector, external sector, real sector and fiscal sector signal-to- noise approach

20 countries for the period 1970-mid- 1995

- This study gives the opportunity to study

76 currency crises and 26 banking crises following the database in the work of Caprio and Klingebiel (1996) Out of sample testing was examined with the twin crises in Asia of 1997.

- Volatilities of such indicators from financial aspect, external and real sector seem to contribute to the probability of banking crisis occurrence.

Dermirguc- Growth rate of multivariate a large - The research found that weak

(1998) real GDP, change in term of trade, depreciation, real interest rate, inflation,

GDP, M2 over reserves, private credit over

GDP, bank cash over assets, growth rate of domestic credit, dummy for deposit insurance, GDP per capita, law and order

(ICRG) logistic model sample of 45-

65 countries in IFS database from both developed and developing countries over their scope from

1980 to 1994 macroeconomic background of the economy such as low growth rate of GDP tends to trigger banking crises due to the risk taking nature of banks.

Banking crises can emerge from issues related to maturity transformation in the banking sector, particularly during periods of high nominal interest rate fluctuations and elevated inflation While inflation control policies, such as restrictive monetary measures, aim to stabilize the banking sector, they can inadvertently increase the risk of crises through the channel of high real interest rates Therefore, weak banking systems must approach inflation control and monetary policies with caution Additionally, the institutional decision to implement a deposit insurance scheme may further heighten the risk of banking crises.

(2012) credit booms, inequality and housing policy to banking crises logit model with and without countries fixed effect

14 advanced countries over the scope of 1880-2008

- Banking system instability, which may lead to banking crisis, was evidently found to have a strong positive relationship with lagged term of two to five years of credit booms.

Inequality and housing policy are critical economic factors analyzed for their influence on the likelihood of financial crises However, the analysis of the current dataset revealed no significant evidence supporting this correlation.

Credit expansion plays a vital role in driving real economic growth, yet it also poses significant risks, as credit booms can lead to financial crises stemming from operational failures and regulatory shortcomings within the financial system.

- The role of such credit booms from the perspective of macroeconomics should be further studied as there have been the historic lessons of credit expansion and financial fragility.

(2011) the current account, growth of loans, volatility of interest rate, inflation, growth of GDP logistic country fixed effect model, descriptive statistics

The growth of loans is a crucial macroeconomic indicator that significantly contributes to the acceleration of banking crises, impacting both national and global economies.

Deteriorating current account balances have clearly played a significant role in the lead-up to crises, affecting both global economies and individual nations Additionally, the persistent suppression of natural interest rates serves as a warning sign of impending economic turmoil.

Real interest rate and inflation also gave similar predicting signal to this trend. Kauko

(2012) deficit current account to banking vulnerabilities with main concentration in banking sector, deterioration of credit quality,

Non- performing loans no banking crisis probability calculation model, OLS regression cross-section of 34 advanced countries according to the classification of IMF 2009

The outcome showed that credit growth in the combination with current account deficit act as an important predictor for financial crises.

GDP ratio, an interaction term between international reserves-to-

GDP ratio and a recipient of a swap line dummy variable, factors of external exposure, institutions, financial development, banking sector health and competition ordinary least square regression

107 countries together with their financial crises episodes from 2008- 2009

The research highlights the spillover effect of the crisis on countries worldwide, revealing that various crisis-related factors contribute to this phenomenon It also identifies common elements shared by the two types of stresses involved, underscoring the interconnectedness of global challenges.

Countries exhibiting greater de facto openness experienced more significant economic shocks, while those with competitive banking systems demonstrated reduced vulnerability, particularly when their banks were well-capitalized and effectively supervised Furthermore, nations with higher international reserves faced increased external stress, whereas commodity-exporting countries encountered diminished internal stress.

Berkmen et wide range of descriptive 43 countries The contagion effect of financial crisis in al (2012) variables of statistic from advanced countries to the rest of other

Trade linkages and the financial forecast reveal that countries experience varying degrees of crisis severity The World Economic Outlook (WEO) macroeconomic background, along with the financial regression database, highlights how institutional policies influence vulnerabilities in each country's financial system during crises This analysis underscores the importance of understanding these factors to better assess the resilience of financial structures in the face of economic shocks.

(2011) financial crisis in the linkage with labor market cross-country model 56 countries over the scope of 2007-2009 first quarter

Our analysis reveals that controlling for variables like trade and capital market integration, financial development, monetary and fiscal policy, institutional differences, and population growth, lower hiring costs significantly mitigate output loss, particularly in high-income countries Conversely, in low-income countries, the duration of economic crises tends to be prolonged when dismissal costs are low.

7 factors of cross-country crisis severity, i.e the GDP growth over times, growth of consumption,

METHODOLOGY, MODEL SPECIFICATION AND DATA

Model selection

The judgment outcome of banking crisis is in binary format, i.e it takes the value of

In analyzing banking crises, empirical studies frequently utilize binary indicators, assigning "1" for a crisis and "0" for no crisis Among the commonly employed methods, probit and logit regression models are prominent, with a noticeable preference for the logit model due to its binary nature and straightforward interpretation (Dermirguc-Kunt and Detragiache, 1998; Hagen and Ho, 2007; Jorda et al., 2011; Bordo and Meissner, 2012) This thesis will adopt the logistic regression technique, aligning with the prevailing trend in the literature The mathematical principles underlying logit regression are outlined in the following sections.

First, those may take a look at the logit model in its mathematic equation below:

Let = , the formula above can be rewrote as:

It is easy to recognize that while takes the values from -∞ to +∞, will receive values ranging between 0 to 1.

Supposed that is the probability of banking crisis occurrence, then the state of non-crisis will be described by the term (1- ) = 1- = (2)

= (3) After all, take natural log for both sides of equation (3) the logit model will be obtained as:

)= = (4) Where denotes the coefficients of each explanatory variables separately.

) Therefore, taking the antilog of the estimated logit, we get

The odds ratio is a key metric in statistical analysis To calculate the percent change in odds for a unit increase in the j-th regressor, take the antilog of the j-th slope coefficient, subtract 1, and then multiply the result by 100 This method provides a clear understanding of how changes in the regressor influence the odds.

The formula (4) under the conditions that time and entities are considered together can be rewritten as follow:

According to Gujarati (2004), the estimation relies on specific assumptions regarding the intercept, slopes of coefficients, and the error term There are five key assumptions: (1) the intercept and slopes remain constant across time and entities (such as countries), with the error term accounting for differences; (2) the slopes remain constant while the intercept varies among countries; (3) the slopes are unchanged, but the intercept varies over time and across countries; (4) both the intercept and slopes vary among countries; and (5) both the intercept and slopes vary across countries and time These assumptions reflect increasing complexity and may better align with real-world scenarios This analysis will focus on the first two assumptions.

The pooled regression model, often referred to in the context of assumption (1), offers a straightforward approach to regression analysis; however, its significant limitations can lead to a distorted understanding of the relationship between the dependent and independent variables.

As common sense, people are more interested in the specific nature of each country This turns to the second case described by the estimation rewritten from (5) as below:

The fixed effect model (FEM) allows for varying intercepts across countries while maintaining time invariance, highlighting unique characteristics that differentiate one country from another This approach is favored over pooled regression in the thesis, as it effectively captures the specific features inherent to each country.

In addition, a formal statistical test, i.e the restricted F-test, is suggested for the choice between restricted pooled model and FEM.

The Fixed Effects Model (FEM) is straightforward to implement, but it can limit degrees of freedom when dealing with numerous cross-sectional units (Gujarati, 2004) This raises the question of whether an alternative approach could better account for the unknown information in the error term, rather than focusing solely on the intercept in FEM As a result, the Random Effects Model (REM) was developed to address these concerns.

In the Random Effects Model (REM), the equation includes a random error term with a mean of zero and a specific variance, indicating that while individual countries share the same mean intercept, variations arise from the error term.

By substituting (7) into estimation (6), the REM estimation obtained below:

Hausman (1978) introduced a formal test to determine the appropriate choice between Fixed Effects Model (FEM) and Random Effects Model (REM) in analysis Despite this, FEM is often preferred due to its ability to provide unbiased estimations consistently.

Model specification

Hagen and Ho (2007) and Gujarati (2003) emphasize that selecting variables for a regression model should be grounded in both theoretical frameworks and empirical research Despite this, the risk of specification errors remains due to potential oversight of relevant variables or inclusion of irrelevant ones, as well as inaccuracies in the functional form This thesis supports the use of a logistic regression model for analyzing banking crises, while striving to incorporate as many relevant variables as possible, based on previous studies and data availability The analysis aims to identify key determinants influencing the probability of banking crises, utilizing a logit model as discussed in works by Dermirguc-Kunt and Detragiache (1998), Hagen and Ho (2007), Jorda et al (2011), and Bordo and Meissner (2012).

A comprehensive framework for predicting banking crises should incorporate all relevant variables, drawing upon established banking crisis theories and available data This model aims to enhance our understanding of the factors contributing to such financial disruptions.

The regression model incorporates various factors, including a constant item and an error term, while each independent variable's coefficient is specifically designated Key indicators include the banking crisis occurring 12 months prior for country i, inflation rates at time t, and the growth rate of the monetary base relative to GDP at the same time Additionally, the model considers the depreciation of the domestic currency, short-term real interest rates projected 36 months ahead, and the growth rates of domestic credit and deposits to GDP over the previous 12 and 6 months, respectively It also evaluates the growth rate of M2 over reserves at time t The model further assesses governance indicators such as voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption.

This thesis examines three groups of relevant variables: macroeconomic indicators, financial indicators, and past banking crises Key macroeconomic factors include inflation, monetary base growth, and currency depreciation, with prior banking crises considered significant due to their potential to exacerbate weak macroeconomic conditions (Berkmen et al., 2012) Additionally, financial indicators focus on metrics such as M2 growth relative to reserves, short-term real interest rates, and credit growth.

Gross Domestic Product (GDP) and the growth of bank deposits relative to GDP are key indicators of financial system health Additionally, institutional indicators such as Voice and Accountability, Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption provide valuable insights into governmental health This article discusses the impact of each explanatory variable and outlines their expected effects.

Inflation serves as a key indicator of government mismanagement within the economy, often coinciding with high interest rates, currency depreciation, and weakened bank balance sheets While the negative impacts of inflation are crucial for understanding banking crises, empirical studies indicate that its correlation with such crises is weaker in developed nations compared to emerging economies Despite this, inflation remains a prominent factor for analysis due to its comprehensive reflection of the macroeconomic landscape, and thus will be included in this thesis, even though its expected relationship with banking crises is uncertain.

• Past banking crisis (lagged 12 months)

Identifying the exact periods when a banking system is free from crisis is challenging due to delayed recognition and asymmetric information regarding recovery signals However, analyzing past banking crises can provide valuable insights for predicting current crises This concept is supported by Falcetti and Tudela (2006), who incorporated lagged terms into their model for the determinants of banking crises The underlying hypothesis suggests that previous banking crises may contribute to present crises, indicating a positive relationship between past and current banking instability.

The monetary base is considered to reflect the monetary expansion (Hagen and Ho,

The growth of the monetary base is a crucial indicator for evaluating the overall health of the banking system, as it serves as a primary source of liquidity A strong monetary base is anticipated to mitigate the risk of banking crises, suggesting an inverse relationship between this indicator and potential financial instability.

Banks often borrow from foreign sources to lend domestically, which puts their profitability at risk during domestic currency depreciation (Demirgỹỗ-Kunt and Detragiache, 1998) In response, many countries have implemented regulations to restrict foreign borrowing by banks; however, these regulations are frequently circumvented Some banks issue domestic loans in foreign currencies, effectively transferring foreign exchange risk to borrowers Consequently, any depreciation in the exchange rate can lead to increased bad loans, negatively impacting bank profits Historical narratives support this analysis as highlighted by Demirgỹỗ-Kunt and Detragiache.

Research indicates that foreign currency loans contributed to banking system crises in several countries, including Chile in 1981, Mexico in 1995, the Nordic countries in the early 1990s, and Turkey in 1994 Therefore, a positive correlation is anticipated between this indicator and the occurrence of banking crises.

The monetary aggregate can indicate central bank reserves, with shortages potentially leading to crises Jorda et al (2011) found that crises were often preceded by a four-year peak in the money to nominal GDP ratio In contrast, Drehmann et al (2011) argued that this ratio is not a reliable predictor of crises To reconcile these differing findings, this thesis allows the data to speak for itself, maintaining an ambiguous stance on the expected impact.

• Real short-term interest rate

High interest rate often results in insolvencies for the borrowers; hence leading to banks may suffer non-performing loans Demirgỹỗ-Kunt and Detragiache (1998,

Research by Jorda et al (2000, 2002) indicates that banking crises often occur following periods of high real interest rates However, there is ongoing debate regarding the significance of high versus low real interest rates in influencing the likelihood of banking crises.

A study by 2011 found that the explanatory power of certain factors was insignificant, suggesting that their influence is only evident over time in individual countries Conversely, low real interest rates can facilitate credit expansion, which may increase banks' vulnerability to crisis risks (Bordo and Meissner, 2012) Similarly, Hagen and Ho (2007) supported the notion that high real interest rates negatively impact banking stability Given these conflicting views on the relationship, this thesis presents the impact as ambiguous.

• Growth of credit to GDP

Rapid credit growth can signal potential vulnerabilities within the banking system, as highlighted in numerous studies on banking and financial crises This phenomenon is closely linked to the performance and profitability of banks Historically, significant increases in credit levels have preceded banking crises, suggesting a positive correlation between high credit growth and the likelihood of a financial downturn.

• Growth of deposit to GDP

Deposit of all kinds may reflect the belief of the public in health of banking sector,

A higher ratio of deposits to assets contributes to a safer banking system, as noted by Kauko (2014) This view is widely accepted among researchers analyzing both first and second trends of banking crises, particularly in the absence of any deposit guarantee regulations Consequently, the growth of this ratio in relation to GDP will be incorporated into the thesis model, with the anticipated outcome being a negative relationship.

Estimation strategies and relevant model diagnostics

3.3.1 Calculation of MMP for banking crisis assessment

The MMP index is calculated using the formula from Chapter 2, where 'y' represents the reserves to bank deposits ratio, 'r' indicates the short-term real interest rate, and the terms denote the respective components The standard deviations of these two components are also factored into the calculation.

This thesis utilizes monthly data from the IMF International Financial Statistics CD-ROM, covering the period from 2001 to 2010 Total deposits are determined by aggregating demand deposits, time and savings deposits, and foreign liabilities of deposit money banks Following the methodology of Hagen and Ho (2007), borrowed reserves—defined as loans from monetary authorities to financial institutions—are utilized as the reserves aggregate instead of total reserves Nominal interest rates are derived from money market rates, with alternative sources such as treasury bill rates, government bond yields, deposit rates, lending rates, and discount rates employed in that order when money market rates are unavailable The inflation rate is calculated using the consumer price index to derive the real interest rate.

- Obtain the BC time based on the 2 criteria for MMP index shows in equation

(2) stated in Chapter 2 as below:

The percentile command in Excel calculates the 98.5 percentile of the MMP distribution once all index values are obtained from the previous step This calculation reflects the growth rate of the MMP index between the current period (t) and the previous period.

3.3.2 Model estimation steps and diagnostics

- Estimate the logit model using all possible explanatory variables from macroeconomic, financial and institutional aspects.

- Putting lagged terms of indicators into the estimation basing on relevant empirical studies to obtain better regression model with larger value of Likelihood Ratio between models.

The validity of logistic regression analysis hinges on several key assumptions, and any violation can lead to biased coefficient estimates and inflated standard errors, compromising statistical significance Therefore, it is crucial to conduct diagnostic assessments of the model before drawing any conclusions The essential assumptions include: (1) the dependent variable must reflect true conditional probabilities based on the independent variables; (2) there should be no omission of significant variables; (3) irrelevant variables must be excluded; (4) independent variable measurements must be accurate; (5) observations must be independent; and (6) multicollinearity among independent variables must be avoided.

- Test for model specification error

In building a logistic regression model, it is assumed that the logit of the outcome variable is a linear combination of independent variables, addressing both the link function and the inclusion of relevant predictors Specifically, the logit function is presumed to be the appropriate link function on the left side of the equation, while the right side assumes all relevant variables are included without any extraneous factors If the logit function is incorrectly chosen or if the relationship between the logit and independent variables is not linear, the model may face specification errors Although misspecification of the link function is generally less critical than using alternative functions like probit, the primary concern remains ensuring that all relevant predictors are included and that their linear combination adequately represents the outcome.

The Stata command linktest is essential for detecting specification errors following the logit or logistic command It operates on the principle that a well-specified model should not yield statistically significant additional predictors beyond chance After running the regression, linktest employs the linear predicted value (_hat) and its squared counterpart (_hatsq) to reconstruct the model The variable _hat is expected to be a significant predictor, as it derives from the model, unless there is complete misspecification Conversely, if the model is correctly specified, _hatsq should lack predictive power, indicating that a significant _hatsq suggests potential issues such as omitted relevant variables or an incorrectly specified link function.

Multicollinearity arises when two or more independent variables in a regression model are closely related through a linear combination of other variables The impact of multicollinearity can vary, with perfect multicollinearity making it impossible to derive unique estimates for regression coefficients when one variable is a perfect linear function of others In such cases, Stata addresses this issue by eliminating the variable that is a perfect linear combination, retaining only those variables that are not exact combinations of others to ensure unique coefficient estimates However, the decision on which variable to omit should be guided by theoretical considerations rather than assuming the dropped variable is the "correct" one to exclude.

Moderate multicollinearity is a common occurrence in regression analysis, as any correlation among independent variables signifies its presence Severe multicollinearity can lead to inflated standard errors for coefficients, making logistic regression estimates unreliable The command "collin" is utilized to detect multicollinearity, providing insights into the strength of interrelationships among variables Two key measures for assessing multicollinearity are tolerance, which indicates how much multicollinearity a regression can withstand, and the variance inflation factor (VIF), which reflects the extent of standard error inflation due to multicollinearity Tolerance is calculated as 1 minus the R² from regressing other variables on a specific variable, while VIF is the reciprocal of tolerance When variables are completely uncorrelated, both tolerance and VIF equal 1 However, if a variable is closely related to others, tolerance approaches 0, resulting in a significant increase in variance inflation.

Data scope and sources

Data availability plays a crucial role in selecting the countries included in the sample, which comprises 18 nations, primarily from ASEAN, along with China, Japan, Korea, and several Latin American countries This selection is based on the fact that these countries have experienced banking crises that significantly impacted their economic development Additionally, as emerging economies with relatively immature banking systems, they are particularly vulnerable to such financial crises.

This thesis utilizes data from the International Financial Statistics (IFS) by the IMF, the World Bank (WB), and the World Governance Indicators (WGI) to calculate both dependent and independent variables The IFS dataset, which provides monthly data, includes key indicators such as inflation, monetary base growth, depreciation, and real interest rates In contrast, the WB database offers yearly data for indicators like M2 growth relative to reserves, credit growth in relation to GDP, bank deposit growth compared to GDP, and various institutional variables from the WGI.

The analysis utilizes panel data covering a time range from January 2001 to December 2010, based on the availability of relevant data This dataset combines cross-country and time series information, with detailed sources provided in Tables 3.1 and 3.2.

Table 3.1 Data for MMP index calculation

Demand deposits IFS line 24 monthly Time and saving deposits IFS line 25 monthly

Foreign liabilities of deposit money of banks

Total reserves Borrowed reserves used instead IFS line 26G monthly

Nominal interest rate Money market rates IFS line 60B monthly

Inflation rate Obtained from consumer price index IFS line 64 monthly

In countries lacking accessible money market rates, alternative financial indicators such as Treasury bill rates, government bond yields, deposit rates, lending rates, and discount rates will be utilized in that order as substitutes.

Table 3.2 Data and sources of explanatory variables

Variable name Definition Expected sign Sources

Inflation (%) Inflation rates Ambiguous IFS line 64 monthly MB_gr Growth of monetary base Negative IFS line 14 monthly

Past BC BC in last period Positive 12-month lagged term of

Depre (%) Depreciation: changes of nominal exchange rates Positive IFS line RF monthly

M2RES_GR Growth of M2 over reserves Ambiguous World Bank

Nominal interest rates are from IFS line 60b; Inflation rates are from IFS line 64 monthly

CrdGDP_GR Growth of private credit to

DEPOGDP_GR Growth of deposit to GDP Negative World Bank

VA Voice and Accountability Negative WGI Annually

PS Political Stability Negative WGI Annually

GE Government Effectiveness Negative WGI Annually

RQ Regulation Quality Negative WGI Annually

RL Rules of Law Negative WGI Annually

CC Control of Corruption Negative WGI Annually

Macroeconomic Variables Growth of monetary base (-)

Hagen and Ho, 2007 Facetti and Tudela, 2006 Growth of M2/reserves (Ambiguous)

Growth of banking deposits over GDP (-)

Banking Crisis (Money market pressure index approach) Financial Variables

Growth of credit over GDP (+)

Political Stability and Absence of Violence (-)

Kaufmann et al., 2008 Institutional Variables Regulatory Quality (-)

Conceptual framework

A banking crisis refers to a situation where banks experience a sudden loss of confidence, leading to a liquidity shortage and potential insolvency Over time, three key trends have emerged in the analysis of banking crises: the evolution of risk assessment methodologies, the impact of regulatory changes, and the role of macroeconomic indicators One significant tool for assessing banking crises is the Money Market Pressure Index, developed by Hagen and Ho in 2007, which evaluates the pressures within the money market that can signal distress in the banking sector When analyzing banking crises, it is essential to select explanatory variables that encompass macroeconomic, financial, and institutional factors to gain a comprehensive understanding of their causes and effects.

Apply available explanatory indicators from macroeconomic, financial and institutional aspects and arguments on effects of those variables on banking crisis

Between 2001 and 2010, relevant data from the International Financial Statistics (IFS), International Monetary Fund (IMF), and World Bank (WB) was analyzed for countries in Asia and Latin America Utilizing logit regression with conditional country fixed effects in Stata software, the study conducted model specification tests, goodness of fit tests, and multicollinearity tests to ensure robust results.

Thesis results, findings, policy recommendation, limitations and future research

Research Process

Thesis content: determinants of banking crisis

Methodology applied in the thesis

RESUTLS AND FINDINGS

Descriptive statistics of explanatory indicators

To provide a comprehensive understanding of the relevant results, it is essential to first introduce the statistical characteristics of the dataset utilized for regression analysis Table 4.2 presents the mean, standard deviation, minimum, and maximum values for each indicator, organized into three distinct categories: macroeconomic, financial, and institutional groups.

The analysis reveals significant fluctuations in macroeconomic and financial indicators, with M2RES_GR showing a minimum value of -67.88314 and a maximum of 216.7543 Similarly, R_i, CrdGDP_GR, and DEPOGDP_GR exhibit considerable spreads between their maximum and minimum values, indicating high variability when assessed with mean and standard deviation These substantial gaps suggest potential vulnerabilities within the economy, highlighting that a weak economic background may elevate the risk of a banking crisis, as evidenced by the pronounced fluctuations in relevant indicators.

The institutional indicators, ranging from -2.5 to +2.5, exhibit significant fluctuations, suggesting potential issues within government health A poorly functioning government could jeopardize the stability of the banking system, leading to a crisis.

Table 4.2 Summary statistics of variables used in the regression

Variable Observations Mean Std Dev Min Max

The analysis of correlation between dependent and independent variables, as presented in Tables 4.3a and 4.3b, reveals significant insights into banking crises Specifically, the findings indicate that the past banking crisis (BC_MMP(-12)), growth of the monetary base (MB_gr), depreciation (Depre), real interest rate (R_i), and growth of credit relative to GDP are positively correlated with the occurrence of banking crises (BC_MMP) Conversely, inflation (INF), growth of deposits over GDP, and six institutional variables—VA, PS, GE, RQ, RL, and CC—exhibit a negative relationship with banking crises Notably, a strong correlation is observed among the institutional variables, particularly between RQ, RL, CC, and GE.

The RL value has reached a significant high of 0.9, indicating a multicollinearity issue where the regression model struggles to accommodate all highly correlated variables Consequently, specific solutions will be discussed in the following section of this chapter.

Table 4.3a The correlation on the sample observations

BC_MMP INF BC_MMP (-

Table 4.3b The correlation on the sample observations

M2RES_GR VA PS GE RQ RL CC

Statistical tests for model

In analyzing banking crises, a simple logit regression model is employed to assess the impact of various explanatory variables on the occurrence of these crises This model focuses on the direct relationships between the regressors and the regressand, intentionally excluding lagged terms and fixed or random effects that are typically beneficial for panel data analysis The primary aim of this initial step is to conduct a model specification diagnostic using the linktest command, rather than to delve deeply into the effects of the included variables Consequently, statistical indices such as the likelihood ratio, probability, Chi-square, or R-squared can be temporarily overlooked, despite their statistical significance.

The findings in Table 4.4 indicate that the logistic regression model is suitable for the included indicators, as evidenced by the statistically insignificant item _hatsq (p-value of 0.276) and the significant item _hat at the 5% level.

Upon completing this test, the issue of specification error is likely resolved, both in terms of statistical significance and through the robust application of foundational economic theories.

The goodness of fit is a crucial criterion for assessing the effectiveness of a model, as illustrated in Table 4.5 Using Stata, the lfit command allows for this evaluation, with a higher Prob > Chi-squared value indicating a better fit The results reveal a high index value of 0.7524, demonstrating strong goodness of fit for the model.

Multicollinearity is a significant concern in panel data analysis, as it can lead to biased regression results and diminish the reliability of indicator explanations To assess multicollinearity, the Stata command "collin" proves useful, as outlined in Chapter 3 of the methodology section The Variance Inflation Factor (VIF) and tolerance metrics help identify potential multicollinearity issues, with a common threshold indicating that a problematic indicator typically has a VIF greater than 10 or a tolerance below 0.1 Testing results reveal three variables—GE (VIF 34), RL (VIF 13), and CC (VIF 68)—exhibiting multicollinearity Remedies for addressing collinearity include removing redundant variables or utilizing interaction terms among significantly correlated variables Subsequent testing results in Tables 4.7, 4.8, and 4.9 demonstrate the VIF and tolerance values after implementing these corrective measures.

The mean Variance Inflation Factors (VIFs) from the tests suggest that a suitable variable combination for regression should have a minimum VIF value Consequently, the removal of GE, RL, and CC, as illustrated in Table 4.8, is justified, with the lowest recorded VIF being 1.44.

The regression results of the full model and the restricted model, which excludes highly correlated variables, are presented in Tables 4.10 and 4.11 These tables illustrate the differences in outcomes between the two models, highlighting the impact of eliminating variables with high collinearity.

The findings indicate that by removing highly correlated variables from the comprehensive model, the thesis achieves an improved model characterized by more accurate expected signs of coefficients and enhanced statistical significance.

The results of the model diagnostics are summarized in the tables presented in the Appendix, specifically in Tables 4.12, 4.13, 4.14, and 4.15, which facilitate a comparison between the logit, fixed effect, and random effect models.

Coefficients interpretation

Based on the results from normal logit regression, fixed effect model, and random effect model, all explanatory variables maintain consistent signs, with minimal fluctuations in coefficient values across models As discussed earlier in this chapter, the fixed effect model will be utilized for its ability to deliver unbiased estimation results Therefore, the fixed effect model is presented as follows:

After applying all necessary diagnostics and remedies along with an appropriate estimation model, the reliability of the coefficient significance is established The indicators will be categorized into their respective groups and analyzed in relation to previous research The discussion will commence with macroeconomic indicators, followed by financial counterparts, and conclude with an examination of institutional factors.

As having been classified, indicators of this group include inflation, past 12-month banking crisis, growth of monetary base and depreciation.

Inflation plays a crucial role in influencing the probability of banking crises, exhibiting a significant negative impact at a 1% level This finding contrasts with the conclusions of Demirguc-Kunt and Detragiache (1998), who posited that high inflation increases the likelihood of banking crises Typically, inflation peaks either before or during a financial crisis, highlighting the complexity of its effects on banking stability.

High inflation rates may not significantly contribute to banking crises; however, their prolonged accumulation can signal potential crises, particularly in emerging and developing countries where inflation tends to rise during economic growth, potentially reducing the likelihood of banking system failures This suggests that economic growth can lead to inflation due to resource scarcity and inelastic short-term supply Additionally, the analysis indicates that prior banking crises, particularly with a 12-month lag, are strong indicators of current crisis likelihood, aligning with findings from Falcetti and Tudela (2006) The regression results show a significant lagged term for crises at a 0.001 significance level, underscoring the importance of this variable for banking authorities to implement proactive measures against future crises.

The growth of the monetary base has reached a statistically significant level, albeit with an unexpected sign, which echoes the findings of Hagen and Ho (2007) that indicate the sign of this variable fluctuates across different model specifications This variability leads to their conclusion that the monetary base has a negligible impact on predicting banking crisis probabilities Furthermore, differing datasets from various groups of countries may yield contrasting results Despite the incorrect expected sign of monetary base growth in this study, its significance at the 5% level suggests a need for further observation and analysis to determine its value and impact—whether positive or negative—on crisis probabilities in specific country groups.

Davis and Karim (2008) highlight that depreciation plays a crucial role in predicting banking crises, particularly during periods of rapid credit growth and moderately high interest rates Their regression analysis reveals significant lags in the relationship between real interest rates and credit growth relative to GDP Specifically, the findings indicate that current depreciation, coupled with a 12-month growth in credit and a 36-month increase in real interest rates, serves as a strong indicator of an impending banking crisis The p-values for depreciation, real interest rate, and credit growth to GDP are notably significant, measuring 0.009, 0.007, and 0.000, respectively, all meeting the stringent 1% significance level.

This section presents the financial results and findings related to key explanatory variables, including the M2 to reserves ratio, the real interest rate, and the growth rates of credit and deposits relative to GDP.

The M2 to reserves ratio, as noted by Falcetti and Tudela (2006), reflects the liquidity capacity of the financial sector and serves as an indicator of the vulnerability of financial institutions during sudden depositor withdrawals A higher value of this ratio correlates with an increased likelihood of banking crises, supporting the findings of Falcetti and Tudela However, this ratio did not meet the significance criteria, as its p-value was 95.6%, significantly exceeding the 10% significance level.

High real short term interest rate has found by Demirgtic-Kunt and Detragiache

Research indicates that high real interest rates on bank deposits can significantly increase the likelihood of banking crises, as supported by Falcetti and Tudela (2006) and earlier studies by Demirgüç-Kunt and Detragiache (1998, 1999, 2000) Notably, a lagged effect of three years for real interest rates was found to be statistically significant at the 10% level (p-value = 0.011) This suggests that elevated real interest rates may contribute to banking crises within a timeframe of up to four years Conversely, Hagen and Ho (2007) reported an unexpected finding that low short-term interest rates can also elevate the probability of banking crises within the same year, attributing this to contractionary monetary policies implemented to address inflation during periods of low interest rates.

The regression analysis indicates that the 12-month lag of credit growth over GDP is significantly associated with the likelihood of a banking crisis, evidenced by a coefficient of 0.037 and a p-value of 0.003, confirming its predictive power This finding aligns with previous research suggesting that lagged credit growth can forecast banking crises (Dermirguc-Kunt and Detragiache, 2000; Jorda et al., 2011; Barrel et al., 2011; Schularick and Taylor, 2012; Kauko, 2014) Notably, this thesis supports Barrel et al (2011) in their use of a 1-year lag, while contrasting with Hagen and Ho (2007), who found no significant link between credit growth and banking crisis probability.

The growth of banking deposits relative to GDP is a crucial financial indicator for assessing banking crisis prevention, particularly when considering its 6-month lagged term, which demonstrates a significant impact at a 5% significance level This finding aligns with the research of Falcetti and Tudela (2006), indicating that a sharp decline in the growth of bank deposits over GDP heightens the probability of a banking crisis Additionally, these results support the conclusions drawn by Barrel et al., reinforcing the importance of monitoring deposit growth as a preventive measure against potential banking crises.

(2010) which concludes that growth of bank deposit over GDP in lagged format is a good indicator for banking crisis.

In summary, the analysis of macroeconomic and financial indicators indicates the overall health of the non-banking macro-financial environment, aligning with the theories and empirical studies discussed earlier in this thesis This interconnectedness of a weak economic backdrop and unhealthy financial conditions can increase the vulnerability and instability of banking systems, potentially leading to a banking crisis.

In contrast to the previously discussed indicators, the three institutional variables derived from the Worldwide Governance Indicators (WGI) show that only two out of the three variables exhibit the expected signs, although they remain statistically insignificant at levels exceeding 10%.

The integration of institutional variables into the model has proven unsuccessful; however, incorporating government-related indicators is crucial for assessing their influence on the likelihood of banking crises Common sense suggests that government intervention can either assist the banking system in overcoming potential crises or mitigate the effects of ongoing ones.

Effective policies are crucial in managing crises, as poor management can exacerbate them Breuer et al (2006) demonstrated that factors like weak law and order, unstable government, and corruption contribute to currency crises The author suggests that additional insights could be gained by analyzing institutional factors from the International Country Risk Guide (ICRG) dataset to explore banking crises, though access to this database is beyond their capability.

CONCLUSION, POLICY RECOMMENDATION AND LIMITATION

Conclusion

Since the early 1980s, the global banking system has faced numerous crises, with significant events like the Lehman Brothers collapse triggering widespread economic turmoil This has heightened the urgency to identify the factors contributing to banking crises, especially as economies become increasingly interconnected In the 21st century, banking crises appear to be more frequent and complex, necessitating a thorough understanding of their causes to mitigate future risks.

Disasters in major countries can lead to significant economic losses that negatively impact long-term global growth Numerous studies on banking crises reveal shared factors among countries that help explain the underlying logic of these crises.

The thesis effectively utilizes the MMP index from Hagen and Ho (2007) to assess banking crises through macroeconomic and financial lenses However, the integration of WGI institutional indicators yields less favorable results than anticipated Recognizing that government health significantly influences the overall economy, the thesis advocates for the use of more suitable institutional health databases, such as the ICRG data employed by Breuer et al (2006) in their analysis of currency crises Consequently, it underscores the need for a broader application of combined macroeconomic, financial, and appropriate institutional indicators for a more comprehensive understanding of economic health.

Policy recommendation

The findings of the regression model align with previous research, indicating that a weak macroeconomic and financial environment can increase the banking system's exposure to risks and vulnerabilities, potentially leading to banking crises.

Recent regression analysis suggests that a banking crisis occurring within the last 12 months may signal the potential for ongoing or new banking crises Consequently, it is crucial for authorities to closely monitor current banking conditions and implement effective strategies for crisis management By focusing on either alleviating or eliminating the crisis, they can mitigate the negative impacts on economic output and prevent further deterioration of the banking sector.

High real interest rates, credit growth relative to GDP, and deposit growth relative to GDP, along with their lagged effects and currency depreciation, are significant indicators for predicting banking crisis probabilities These financial metrics reflect the overall health of the banking system, making it crucial for authorities to monitor sudden fluctuations and implement timely interventions Although the effects of these indicators may not be immediately apparent, their accumulation over time can increase the likelihood of a banking crisis Interestingly, high inflation during this analysis period appears to correlate with economic growth in emerging countries, suggesting a complex relationship that warrants careful observation Additionally, inflation stability programs may inadvertently introduce volatility in real interest rates, impacting bank profitability and increasing vulnerability to crises Therefore, it is essential to design inflation control programs with caution, balancing their effectiveness against the potential costs of a banking crisis (Dermirguc-Kunt and Detragiache, 1998).

Limitation of the research

Despite efforts to identify determinants of banking crises, the results remain imperfect, as traditional analyses often overlook non-banking crisis factors that could explain varying risk exposure among countries It is essential to establish criteria for non-crisis periods to enhance understanding and management of banking systems and economies This thesis employs the banking crisis definition based on the money market pressure index and utilizes a fixed effect model regression, acknowledging potential data collection biases due to variable availability and access to updated data Additionally, the significance of certain indicators may only emerge with time lags, necessitating further data to avoid sample size issues Testing for appropriate lags is challenging due to the number of variables, leading to reliance on previous studies' suggestions with minor adjustments Unfortunately, institutional variables in the estimation model yielded insignificant results, possibly due to an inadequate dataset, highlighting the need for more suitable databases in future research Ultimately, this analysis reveals a lack of evidence for the conclusion of banking crises, focusing instead on their onset and related timelines.

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Table 4.1 Banking crisis dates retrieved from MMP index

Table 4.4Linktest for specification error of logit model logit BC_MMP INF l12 BC_MMP MB_grDepreR_iCrdGDP_GR DEPOGDP_GR M2RES_GR VA PS

Logistic regression Number of obs = 1943

- BC_MMP | Coef Std Err z P>|z| [95% Conf Interval] -+ -

INF | -.3266457 1266713 -2.58 0.010 -.5749169 -.0783745 BC_MMP(12) | 1.237036 2891568 4.28 0.000 6702991 1.803773 MB_gr | 0254394 0167837 1.52 0.130 -.0074561 0583348 Depre| 0687579 0277797 2.48 0.013 0143107 123205 R_i| 0090064 0155471 0.58 0.562 -.0214654 0394781 CrdGDP_GR | 0226107 0113294 2.00 0.046 0004054 0448159 DEPOGDP_GR | -.0241729 0133022 -1.82 0.069 -.0502448 001899 M2RES_GR | -.0053557 0051703 -1.04 0.300 -.0154893 0047778

- BC_MMP | Coef Std Err z P>|z| [95% Conf Interval] -+ -

Table 4.5 Goodness of fit test of model lfit, group(10) table

Logistic model for BC_MMP, GOODNESS-OF-FIT TEST

(Table collapsed on quantiles of estimated probabilities)

| Group | Prob | Obs_1 | Exp_1 | Obs_0 | Exp_0 | Total |

+ -+ number of observations = 1943 number of groups = 10

Tabel 4.6 Full model multicollinearity test result

Variable VIF SQRT VIF Tolerance R-Squared

Table 4.7 Dropping significantly high correlated variables GE, RL:

Variable VIF SQRT VIF Tolerance R-Squared

Table 4.8 Dropping high correlated variables GE, RL and CC

Variable VIF SQRT VIF Tolerance R-Squared

Table 4.9 Using interactive term of GE and RL

Variable VIF SQRT VIF Tolerance R-Squared

Table 4.10 Full model logit BC_MMP INF l12 BC_MMP MB_grDepreR_iCrdGDP_GR DEPOGDP_GR M2RES_GR VA PS

- BC_MMP | Coef Std Err z P>|z| [95% Conf Interval] -+ -

Table 4.11 Restricted model without GE, RL, CC

logit BC_MMP INF BC_MMP12 MB_grDepreR_iCrdGDP_GR DEPOGDP_GR M2RES_GR VA PS RQ

- BC_MMP | Coef Std Err z P>|z| [95% Conf Interval] -+ -

Table 4.12 Fixed effect model with lags xtlogit BC_MMP INF l12.BC_MMP MB_grDepre l36.R_i l12.CrdGDP_GR l6.DEPOGDP_GR M2RES_GR VA PS RQ, fe

Conditional fixed-effects logistic regression Number of obs = 1511

Group variable: country1 Number of groups = 18

Obs per group: min = 83 avg = 83.9 max = 84

- BC_MMP | Coef Std Err z P>|z| [95% Conf Interval] -+ -

INF | -.4326759 1718959 -2.52 0.012 -.7695856 -.0957662 BC_MMP12 | 1.144859 3351207 3.42 0.001 4880346 1.801683 MB_gr | 0448394 0189506 2.37 0.018 007697 0819818 Depre | 0983399 0376113 2.61 0.009 0246231 1720567 R_i(36) | 0279047 0110093 2.53 0.011 0063268 0494825 CrdGDP_G(12) | 0370456 012508 2.96 0.003 0125303 061561 DEPOGDP_GR(L6)| -.0354308 0153012 -2.32 0.021 -.0654207 -.005441

Table 4.13 Random effect model with lags xtlogit BC_MMP INF l12.BC_MMP MB_grDepre l36.R_i l12.CrdGDP_GR l6.DEPOGDP_GR M2RES_GR VA PS RQ, re

Random-effects logistic regression Number of obs = 1511

Group variable: country1 Number of groups = 18

Random effects u_i ~ Gaussian Obs per group: min = 83 avg = 83.9 max = 84

Wald chi2(11) = 48.62 Log likelihood =-313.92006Prob> chi2 = 0.0000

- BC_MMP | Coef Std Err z P>|z| [95% Conf Interval] -+ -

- Likelihood-ratio test of rho=0: chibar2(01) = 0.00 Prob>= chibar2 = 1.000

INF | -.413111 1467951 -2.81 0.005 -.7008241 -.125398 BC_MMP12| 1.159605 3339441 3.47 0.001 5050869 1.814124 MB_gr | 0441357 0189147 2.33 0.020 0070636 0812078 Depre | 0970619 0374037 2.59 0.009 0237521 1703717 R_i(36)| 0262877 0097476 2.70 0.007 0071827 0453927 CrdGDP_GR(12)| 0395713 0103906 3.81 0.000 0192061 0599365 DEPOGDP_GR(6)| -.0328716 014082 -2.33 0.020 -.0604719 -.0052713

Table 4.14 Simple logit model with lags logit BC_MMP INF l12.BC_MMP MB_grDepre l36.R_i l12.CrdGDP_GR l6.DEPOGDP_GR M2RES_GR VA PS RQ

Logistic regression Number of obs = 1511

- BC_MMP | Coef Std Err z P>|z| [95% Conf Interval] -+ -

INF | -.413118 1468117 -2.81 0.005 -.7008636 -.1253723 BC_MMP12 | 1.1596 333979 3.47 0.001 505013 1.814187 MB_gr | 0441349 0189171 2.33 0.020 007058 0812118 Depre | 0970666 0374072 2.59 0.009 02375 1703833 R_i(36)| 0262878 0097487 2.70 0.007 0071807 0453949 CrdGDP_GR(12)| 0395718 0103918 3.81 0.000 0192042 0599394 DEPOGDP_GR(6)| -.0328699 0140837 -2.33 0.020 -.0604735 -.0052662