Default Probabilities Of Banks Research Articles

The long-run effects of risk: an equilibrium approach

Advanced economies tend to have large financial sectors which can be vulnerable to crises. We employ a DSGE model with banks featuring limited liability to investigate how risk shocks in the financial sector affect long-run macroeconomic outcomes. With full deposit insurance, banks expand balance sheets when risk increases, leading to higher investment and output. With no deposit insurance, we observe substantial drops in long-run credit provision, investment, and output. Reducing moral hazard by lowering the fraction of reimbursed deposits in case of bank default increases the probability of bank default in equilibrium. The long-run probability of bank default under a regime with no deposit insurance is more than 50% higher than under a regime with full deposit insurance for high levels of risk. These differences provide a novel argument in favor of deposit insurance. Our welfare analysis finds that increased risk always reduces welfare, except when there is full deposit insurance and deadweight costs from default are small.

Is bank risk appetite relevant to bank default in times of Covid-19?

The paper aims to analyze the effect of bank risk appetite on banks' default probabilities during the year of COVID-19 in 12 countries while controlling for bank-specific and country-specific effects over time. A System Generalized Methods of Moments (GMM) model of default probabilities is estimated over the periods 2010–2021. This study confirms the ‘risk-mitigation view’, in which banks with higher ESG scores are more prudent in lending and have better relationship management, reducing the probability of bank default. Underperforming banks tend to have a higher portion of risky loans in their credit portfolio and therefore demonstrating a higher default propensity. Bank risk appetite, ESG, asset quality, economic growth, and currency depreciation appear to be material drivers for bank risk. We find that a lower risk appetite ratio (corresponding to higher risk appetite) is associated with higher estimated default probability during the COVID-19 outbreak, identified through interaction with a single time dummy for 2020 (the break-out year of the pandemic).

Bank competition, financial development and macroeconomic stability: Empirical evidence from emerging economies

In this study, we investigate the potential contribution of bank competition to macroeconomic stability, and the interactive role of financial development. We classify macroeconomic stability into economic and financial stability. Economic stability is represented by the volatility of actual and unexpected output growth, whereas financial stability is assessed by the aggregate Z-score and volatility of the private credit-to-gross domestic product ratio. We employ two structural and two non-structural measures of bank competition in our analysis. Applying a two-step dynamic panel system (GMM) to macroeconomic data from 48 developing nations from 1999 to 2018, we find a bell-shaped relationship between bank competition and macroeconomic stability. The findings imply that a higher level of bank competition promotes macroeconomic stability by reducing output growth volatility, fluctuations in private credit, and the probability of bank default. There is an optimal level of bank competition beyond which it may foster economic and financial instability. Moreover, financial development enhances bank competition’s positive impact on macroeconomic stability.

Regulation, banking competition and risk-taking behavior in the MENA region: policy implications for Islamic banks

PurposeThis paper aims to investigate the impact of regulation and market competition on the risk-taking Behaviour of financial institutions in the Middle East and North Africa (MENA) region.Design/methodology/approachThe empirical framework is based on panel fixed effects/random effects specification. For robustness purpose, this study also uses the generalized method of moments estimation technique. This study tests the hypothesis that regulatory capital requirements have a significant effect on financial stability of Islamic and conventional banks (CBs) in the MENA region. This study also investigates the moderating effect of market power and concentration on the relationship between capital regulation and bank risk.FindingsThe estimation results support the view that capital adequacy ratio (CAR) has no significant impact on credit risk of Islamic banks (IBs), whereas market competition does play a significant role in shaping the risk behavior of these institutions. This study report opposite results for CBs – an increase in the minimum capital requirements is followed by an increase in a bank’s risk level, which has a negative impact on their financial stability. Furthermore, the results support the notion of a non-linear relationship between banking concentration and bank risk. The findings inform the regulatory authorities concerned with improving the financial stability of banking sector in the MENA region to set their policy differently depending on the level of concentration in the banking market.Research limitations/implicationsThis study contributes to the literature on the effectiveness of regulatory reforms (in this case, capital requirements) and market competition for bank performance and risk-taking. In regard to IBs, capital requirements are less effective in requiring IBs to adjust their risk level according to the Basel III methodology. This study finds that IBs’ risk behavior is strongly associated with market competition, and therefore, the interest rates. Moreover, banks operating in markets with high banking concentration (but not necessarily, low competition), will decrease their credit risk level in response to an increase in the minimum capital requirements. As a result, these banks will be more stable compared to their conventional peers. Thus, regulators and policymakers in the MENA region should restrict the risk-taking behavior of IBs through stringent capital requirements and more intense banking supervision.Practical implicationsThe practical implications of these findings are that the regulatory authorities concerned with improving banking sector stability in the MENA region should proceed differently, depending on the level of banking market concentration. The findings inform regulators and policymakers to set capital requirements at levels that would restrict banks from taking more risk to increase their returns. They are also important for bank managers who should avoid risky strategies in response to increased regulatory pressure (e.g. increase in the minimum required capital level of 8%), as they may lead to an increase in the level of non-performing loans, and therefore, a greater probability of bank default. A future extension of this study will focus on testing the effect of bank risk-taking and market competition on the capitalization levels of banks in the MENA countries. More specifically, this study will investigates if banks raise their capitalization levels during the COVID-19 pandemic.Originality/valueThe analysis of previous research indicates that there is no unambiguous answer to the question of whether IBs perform differently than CBs under different competitive conditions. To fill this gap, this study examines the influence of capital regulation and market competition (both individually and interactively) on bank risk-taking behavior using a large sample of banking institutions in 18 MENA countries over 14 years (2005–2018). For the first time in this line of research, this study shows that the level of market power is positively associated with the level of a bank’ insolvency risk. In others words, IBs operating in highly competitive markets are more inclined to take a higher risk than their conventional peers. Regarding the IBs credit risk behavior, this study finds that market power has a limited impact on the relationship between CAR and risk level. This means that IBs are still applying in their operations the theoretical models based on the prohibition of interest.

Home Bias in Sovereign Exposure and the Probability of Bank Default - Evidence from EU Stress Test Data

Since the European debt crisis economists and politicians discuss intensively the sovereign-bank nexus. The high activity in sovereign bond issuance required to mitigate the burden of the Covid19 crisis will rather intensify this debate than calm it down. Surprisingly, however, we still have only limited knowledge about the impact of a home bias in sovereign exposure on bank stability. This paper provides a new way to use European stress test data to study this relationship. In addition, we explore the effect on a bank’s probability of default if the existing capital requirement privilege for EU sovereign exposures were abolished. Our results support the conceptual idea behind the nexus theory. Interestingly, the effect of a home bias on bank stability is contingent on the home country’s solvency. If the home country is sufficiently solvent, investments in home sovereign bonds may improve bank stability. The findings clearly support the benefits of additional CET1 capital buffers. Regulation focusing on the home bias should account for heterogeneous effects depending on the home country’s solvency.

Predicting Bank Defaults in Ukraine: A Macro-Micro Perspective

This paper develops an early warning model (EWM) for a micro-macro analysis of individual and aggregated bank vulnerabilities in Ukraine. We applied a stepwise logit for predicting defaults at Ukrainian banks based on a panel bank and macro-level data from Q1 2009 to Q3 2019. Next, we aggregated individual bank default probabilities to provide policymakers with information about the general state of the financial system with a particular focus on generating a signal for countercyclical capital buffer (CCB) activation. Our key findings suggest that the probability of default exceeding 11% could signal about a vulnerable state in a bank and, in the aggregated model, in a financial system in general. The aggregated model successfully issues an out-of-sample signal of a systemic crisis four periods ahead of the start of the 2014-2015 turmoil.

Oligopoly Banking, Risky Investment, and Monetary Policy

Oligopolistic competition in the banking sector and risk in the real economy are important characteristics of developed economies, but have so far mostly been abstracted from in monetary economics. We build a dynamic general equilibrium model of monetary policy transmission that incorporates both of these features and document that including them leads to important insights in our understanding of the transmission mechanism. Various equilibrium cases can occur, and policies have differing effects in these cases. We calibrate the model to the U.S. economy in 2016-2019 in order to study how changes in the degree of banking competition or the policy rate would have affected equilibrium outcomes. We find that doubling banking competition would have increased welfare by 1.02\%, but at the cost of increasing the probability of bank default from 0.02\% to 0.44\%. We further find that the policy rate was set optimally to minimize the probability of bank default, but that a decrease in the policy rate by 1pp would have increased welfare by 0.40\%. We also show that bank profits are increasing in the policy rate, in particular when interest rates are low. Thus, a 1pp reduction in the policy rate would have reduced profits per bank by 35.5\% in our calibrated economy. Finally, we document that monetary policy pass-through is incomplete under imperfect competition in the banking sector, as a change in the policy rate by 1pp leads to a change of only 0.92pp in the loan rate, while pass-through to the deposit rate is nearly complete for rate increases, but almost zero for rate reductions due to the zero-lower bound.

Risk, Inside Money, and the Real Economy

In modern economies, most money takes the form of inside money; deposits created by commercial banks to fund credit extension. Because inside money is used as a payment instrument, doubts about the risks associated with credit extension can affect aggregate outcomes. This paper constructs and analyzes a model of risky credit extension, inside money creation, and monetary exchange. When credit extension is sufficiently risky, a positive probability of bank default arises and this affects the return characteristics of inside money. Depositors then demand a risk premium for holding inside money, which drives a wedge between bankers' funding costs and the social benefits of money creation. This wedge negatively affects credit extension, output, and welfare. A government can restore efficiency by swapping risky inside money for risk-free forms of government debt.

The Effects of Information on Credit Market Competition: Evidence from Credit Cards

We show empirically that public credit information increases competition in credit markets. We access data that cover all credit card borrowers in Chile and include details about relationship borrowers have with each lender. We exploit a natural experiment whereby a non-bank lender’s portfolio was sold to a bank. Because of this transaction, the lender’s borrowers, who were previously not identifiable unless in default, become observable by banks through the credit bureau but remain unobservable to other non-bank lenders. Using a difference-in-differences strategy, we find that after the transaction the lender’s borrowers receive higher credit limits from other banks relative to other non-bank borrowers. This result is mediated by individuals whose predicted probability of bank default drops as a result of the change to banks’ information set. After the transaction, the lender shifts originations to safer borrowers with higher initial limits, a result that is consistent with cross-sectional evidence that banks tend to lend to safer borrowers. Our results imply that by increasing competition, public credit information can reduce lenders’ incentive to “learn by lending”, potentially excluding riskier populations from access to credit.

Genetic algorithm based model for optimizing bank lending decisions

To avoid the complexity and time consumption of traditional statistical and mathematical programming, intelligent techniques have gained great attention in different financial research areas, especially in banking decisions’ optimization. However, choosing optimum bank lending decisions that maximize the bank profit in a credit crunch environment is still a big challenge. For that, this paper proposes an intelligent model based on the Genetic Algorithm (GA) to organize bank lending decisions in a highly competitive environment with a credit crunch constraint (GAMCC). GAMCC provides a framework to optimize bank objectives when constructing the loan portfolio, by maximizing the bank profit and minimizing the probability of bank default in a search for a dynamic lending decision. Compared to the state-of-the art methods, GAMCC is considered a better intelligent tool that enables banks to reduce the loan screening time by a range of 12%–50%. Moreover, it greatly increases the bank profit by a range of 3.9%–8.1%.

Endogenous Bank Networks and Contagion

I develop a model to study two related questions: how bank decisions to form connections depend on fundamentals; and how financial stability depends on bank network structure. In my model, banks are connected through two layers of networks: interbank debts and banks' common investments in non-financial firms. These layers of interconnections are incentivized by diversified investments when banks maximize their expected equity values according to mean-variance rules. I show that the topologies of the two network layers interact with each other. Comparative statics of a small number of banks indicates that, in equilibrium, as banks become less risk averse, they tend to issue more debts and form more links within the banking sector. Furthermore, I conduct numerical computations for bank default probabilities in a circle network and a more connected network. The results demonstrate that increased bank interconnectedness and common asset holdings significantly reduce systemic stability.

Evaluating systemic risk using bank default probabilities in financial networks

In this paper, we propose a novel methodology to measure systemic risk in networks composed of financial institutions. Our procedure combines the impact effects obtained from stress measures that rely on feedback centrality properties with the default probabilities of institutions. We also present new heuristics for designing feasible and relevant stress-testing scenarios that can subside regulators in financial system surveillance tasks. We develop a methodology to extract banking communities and show that these communities have a relevant effect on systemic risk. We find that these communities are mostly composed of non-large banks, suggesting that regulators should also broaden their surveillance efforts to these banking communities other than to the usual SIFIs and large banks. Finally, our results provide insights and guidelines for policymakers.

Credit Rating and Default Probabilities of Vietnamese Commercial Banks

As a main function of bank is accepting or borrowing from their customers or credit institutions to make loans to others, default risk is the oldest, the most important and primary risk in banking sector. Especially in these years after the recent financial crisis, a huge amount of banks’ loan unfortunately became bad debt, default probability of banks consequently increases, and depositors, regulators also gradually lost their belief on banks. As a measure of bank health, bank credit rating is introduced for the assessment. This paper represents a sample of 16 Vietnamese commercial banks from 2008 to 2012 aims to provide knowledge about credit trustworthiness and determine estimated bank default probability afterward.

Banks’ Risk Endogenous to Strategic Management Choices

Use of variability of profits and other accounting‐based ratios in order to estimate a firm's risk of insolvency is a well‐established concept in management and economics. We argue that these measures fail to approximate the true level of risk accurately because managers consider other strategic choices and goals when making risky decisions. Instead, we propose an econometric model that incorporates current and past strategic choices to estimate risk from the profit function. Specifically, we extend the well‐established multiplicative error model to allow for the endogeneity of the uncertainty component. We demonstrate the power of the model using a large sample of US banks and show that our estimates predict the accelerated bank risk that led to the subprime crisis in 2007. Our measure of risk also predicts the probability of bank default both in the period of the default but also well in advance of this default and before conventional measures of bank risk.

Modeling Banks’ Probability of Default

The unprecedented financial crisis of 2008-2009 has called attention to limitations of existing methods for estimating the default risk of financial intuitions. Over the past decade, we have had considerable success at predicting default and credit relative value using Merton-type structural models and Hybrid Probability of Default models. However, generating accurate model-based estimates of default probabilities (PDs) for financial firms has proven difficult. To address this need, I built and tested a time-adaptive statistical model that predicts the default probabilities of banks. The model is a logistic regression whose input variables are selected based on their past effectiveness at predicting bank failures and whose inclusion in the model and weights are to be updated quarterly. Model performance at discriminating between defaults and non-defaults was evaluated for horizons of one to five years using a sequence of annual walk-forward out-of-sample tests from 1992 to 2012. I tested the ability of the model to predict absolute default rates out to five years and, except for underestimating the high bank default rates during the credit crisis, the models perform well at estimating the annual bank default rates. Because most default models provide little benefit over agency ratings for low-rated credits, I examined the performance of the model to Kroll agency ratings only for those banks rated above single-B-minus or above single-C-minus. Although default predictions from agency ratings fall off rapidly for banks rated at or above single-B and single-C, the time-adaptive statistical model predictions deteriorate far less. Accuracy at predicting bank defaults using agency ratings decreases to near chance at a prediction horizon of five years, but the time-adaptive statistical model continues to perform well above chance at all horizons. I also present a detailed analysis of the contributions of financial variables to model outputs by year (2000-2012) and tenor (1-5 years) and evaluate the consistency of variable contributions over time. The model performs favorably at predicting defaults, even relative to the best non-financial corporate default models, with a 97% accuracy ratio (AR) at one year prior to default, and decreasing, but still above-chance predictive power out to five years. I find that banks’ quality of assets and return on equity are most important for predicting near term defaults, giving way at longer horizons to operating income and the yield on earning assets.

Federal Home Loan Bank advances and bank risk

The Federal Home Loan Bank system (FHLB) has evolved into a major source of liquidity for the banking system with the demonstrated ability to borrow over a trillion dollars in world financial markets based on an implied U. S. Treasury guarantee. The FHLB loans the borrowed funds to commercial banks at reduced rates that are not adjusted for the risk of an individual bank. Moral hazard could cause member banks using FHLB loans to increase financial leverage and exposure to high risk assets. Conversely, the FHLB offers banks additional liquidity and specialized debt instruments that help them manage interest rate risk. We use dynamic panel generalized method of moments estimation to test the relation between FHLB advances and bank risk. We find that if banks have relatively normal default probabilities, advances are not associated with increased bank risk but, instead, advances are related to decreased interest rate risk. However, when bank default probabilities are high, our evidence suggests advances and higher bank risk are related.

Does Size of Banks Really Matter? Evidence from Credit Default Swaps Market Data

This study aims to discover whether markets take into account the phenomenon known as Too Big to Fail. Using Credit Default Swaps market data, which reflects the risk, markets attribute to banks, we calculate the default probabilities of banks over one, two, and three year periods. These results are then regressed with financial values such as total assets, total shareholders’ equity and net income. Subsequently, the study is extended and the regression analysis repeated using Return on Assets as dependent variable. We find that markets place emphasis on profitability rather than size when pricing the riskiness of a bank. We conclude that the well-known concept of ‘Too Big to Fail’ cannot be considered as a concept with much validity, but the phrase ‘Too Profitable to Fail’ may provide a more accurate assessment of the situation.

Comparison of Bank Financial Stability Factors in CIS Countries

We develop the model for estimating the default probabilities of banks for Russia, Belarus, Kazakhstan and Ukraine using banking statistics from 2005 to 2013. We find that a binary logit regression works best. In addition, macroeconomic and institutional factors significantly improve model accuracy. The results indicate that there are significantly different sources of risk in banking in the considered banking systems. These results are useful for agents operating with CIS banks.

The fluctuating default risk of Australian banks

Australian banks are widely considered to have fared far better during the Global Financial Crisis than their global counterparts, continuing to display solid earnings, good capitalization and strong credit ratings. Nonetheless, Australian banks experienced significant deterioration in the market values of assets. We use the KMV/Merton structural methodology, which incorporates market asset values, to examine default probabilities of Australian banks, making extensive international comparisons. We also modify the model to incorporate conditional probability of default, which measures extreme credit risk. We find that, during the Global Financial Crisis, based on extreme asset value fluctuations, Australian bank default probabilities fare only slightly better than their global counterparts. JEL Classification: G01, G21, G28

A network model of systemic risk: stress testing the banking system1

AbstractAlthough there are many definitions of systemic risk, most agree that it manifests itself by an initial shock that results in the failure of one or more banks and then spreads out to the entire system by a contagion mechanism which can result in the failure of more banks in the system. Assuming that bank failures in the initial shock are randomly dependent on the failure probabilities of the individual banks and that the ensuing contagion process is deterministic, depending on interbank exposures, in this paper we propose a network model to analyse systemic risk in the banking system that, in contrast to other proposed models, seeks to obtain the probability distribution of losses for the financial system resulting from the shock/contagion process.Thus, calculating the probabilities of joint failures by simulation and assuming that the matrix of bilateral interbank exposures is known, we represent systemic risk in the financial system by means of a graph and use discrete modelling techniques to characterize the dynamics of contagion and corresponding losses within the network. The probability distribution of losses, risk profile for the Mexican banking system, is obtained through an efficient, complete enumeration procedure of all possible bank default events in the system. This, in turn, allows the use of the wide variety of well‐established risk measures to describe the fragility of the financial system. Additionally, the model allows us to perform stress tests along both the bank default probabilities and the interbank exposures and is used to assess the risk of the Mexican banking system. Copyright © 2009 John Wiley & Sons, Ltd.