The market’s view on the probability of banking sector failure: cross-country comparisons

Hall and Miles (1990) suggest an approach of estimating default probabilities of banks using stock market information, and in this paper we apply an aggregated version of their approach to banking sectors around the world in both developed and emerging economies. We study the market’s assessment of the probability of systemic banking crises world wide over the last decade, including the Asian crisis 1997–1998. In addition, we investigate whether there is a relationship between the failure probability and institutional features of the actual banking sector. The quality of governance and the degree of law and order in a country is found to be significantly negatively related to the market based failure probabilities as is an explicit deposit insurance during periods of crisis.     

Banking systemic vulnerabilities: A tail-risk dynamic CIMDO approach

This study proposes a novel framework which combines marginal probabilities of default estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology and the generalized dynamic factor model (GDFM) supplemented by a dynamic t-copula. The framework models banks’ default dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. It measures banking systemic credit risk in the three forms categorized by the European Central Bank: (1) credit risk common to all banks; (2) credit risk in the banking system conditional on distress on a specific bank or combinations of banks; and (3) the buildup of banking system vulnerabilities over time which may unravel disorderly. In addition, the estimates of the common components of the banking sector short-term and conditional forward default measures contain early warning features, and the identification of their drivers is useful for macroprudential policy. Finally, the framework produces robust out-of-sample forecasts of the banking systemic credit risk measures. This paper advances the agenda of making macroprudential policy operational.     

A compound option approach to model the interrelation between banking crises and country defaults: The case of Hungary 2008

We analyze the Hungarian financial crisis of 2008 in a stochastic framework that advances structural credit risk models for country defaults: by applying compound option theory we consider payments for bailing-out the banking sector together with debt service payments in a joint crisis model. We estimate the model parameters by applying the time series maximum-likelihood approach of Duan (1994) on yield spreads of Hungarian Bonds. We find that difficulties in acquiring funds for debt servicing in combination with high outstanding debt triggered the crisis, rather than problems in the domestic banking sector. The estimated crisis probabilities dramatically rise during 2008.     

An Empirical Investigation of CDS Spreads Using a Regime-Switching Default Risk Model

Default risk in equity returns can be measured by structural models of default. In this article we propose a credit warning signal (CWS) based on the Merton Default Risk (MDR) model and a Regime-Switching Default Risk (RSDR) model. The RSDR model is a generalization of the MDR model, comprises regime-switching asset distribution dynamics, and thus produces more realistic default probability estimates in cases of deteriorating credit quality. Alternatively, it reduces to the MDR model. Using a dataset of U.S. credit default swap (CDS) contracts around the 2007-8 crisis we construct rating-based indices to investigate the MDR and RSDR implied probabilities of default in relation to the market-observed CDS spreads. The proposed CWS measure indicates an increase in implied default probabilities several months ahead of notable increases in CDS spreads.     

Aggregating sectors in the infectious defaults model

Abstract

How to model the dependence between defaults in a portfolio subject to credit risk is a question of great importance. The infectious default model of Davis and Lo offers a way to model the dependence. Every company defaulting in this model may ‘infect’ another company causing it to default. An unsolved question, however, is how to aggregate independent sectors, since a naive straightforward computation quickly gets cumbersome, even when homogeneous assumptions are made. Here, two algorithms are derived that overcome the computational problem and further make it possible to use different exposures and probabilities of default for each sector. For an ‘outbreak’ of defaults to occur in a sector, at least one company has to default by itself. This fact is used in the derivations of the two algorithms. The first algorithm is derived from the probability generating function of the total credit loss in each sector and the fact that the outbreaks are independent Bernoulli random variables. The second algorithm is an approximation and is based on a Poisson number of outbreaks in each sector. This algorithm is less cumbersome and more numerically stable, but still seems to work well in a realistic setting.     

The effects of loan portfolio concentration on Brazilian banks’ return and risk

This paper tests whether diversification of the credit portfolio at the bank level leads to better performance and lower risk. We employ a new high frequency (monthly) panel data for the Brazilian banking system with information at the bank level for loans by economic sector. We find that loan portfolio concentration increases returns and also reduces default risk; the impact of concentration on bank’s return is decreasing on bank’s risk; there are significant size effects; foreign and state-owned banks seem to be less affected by the degree of diversification. An important additional finding is that there is an increasing concentration trend after the breakout of the recent international financial crisis, specially after the failure of Lehman Brothers.     

Euro area banking and monetary policy shocks in the QE era

This paper assesses the effects of monetary policy shocks on the macroeconomy and the euro area banking sector after the global financial crisis. First, financial risk-return indicators of the banking sector based on a compound option-based structural credit risk model are embedded in a large macro-financial quarterly database covering the period 2008Q4–2019Q4. Second, a SFAVAR identifies and estimates the shocks’ responses relating them to the endogenous build-up of banks’ vulnerabilities which are consistent with the internally coherent structure of the credit risk model. By introducing structure in the understanding of banks’ asset-liability management behavior following monetary policy shocks, the research strategy contributes to disentangling results that are often mixed in the empirical literature. The study finds that unconventional monetary policy, in particular the Asset Purchase Program of the European Central Bank, seems to have been more successful than conventional monetary policy in raising output and inflation. The desired boost to bank lending has been muted and loan cyclicality has varied across countries and loan types. The performance of the banking sector following monetary policy shocks can be characterized by a drop in expected return on equity and assets, a relaxation of lending conditions and increased correlation between banks’ assets return and the market return, a mechanism pointing to enhanced risk-taking. While banks’ probabilities of default fall following monetary policy shocks, the price of risk increases. Banks’ net worth rises via higher market capitalization and implied assets value together with lower volatility, despite often incurring more debt. Risk-taking in the banking sector may pose a risk to financial stability, especially if its effects on banks’ vulnerability spread and increase systemic risk. The unintended endogenous build-up of macro-financial vulnerabilities may need to become part of monetary policymaking.     

Toward a Bottom‐Up Approach to Assessing Sovereign Default Risk

The authors propose a new approach to assessing sovereign risk that focuses on the underlying profitability and financial condition of a nations private corporate sector. More specifically, the authors show how their new Z‐Metrics™ approach—an updated and expanded version of the Alt‐man Z‐score methodology—can be used to measure the (cumulative) median probability of default of the non‐financial sector for the next five years, both as an absolute measure of corporate vulnerability and a relative measure that can be compared to the risk of other sovereigns and to the market's assessment as reflected in the prices of credit default swaps. In testing their approach, the authors measure the default probabilities of listed corporate entities in nine European countries, as well as the U.S., at two different points in time: the start of 2009 (and thus prior to the recognition of the Euro crisis by markets and most credit professionals) and for the first four months in 2010 (essentially, the beginning of the recognition of the crisis). Based on these two observations, the authors suggest that their corporate health index of the private sector would not only have served as an effective early warning indicator, but provided a (mostly) useful hierarchy of relative sovereign risks. In addition to predicting sovereign default risk, another potentially valuable use of the authors' model is to remind policy makers of the importance of a profitable private sector to the financial health of sovereign governments.     

Measuring Credit Risk of Individual Corporate Bonds in US Energy Sector

In this paper, using the measures of the credit risk price spread (CRiPS) and the standardized credit risk price spread (S-CRiPS) proposed in Kariya’s (A CB (corporate bond) pricing model for deriving default probabilities and recovery rates. Eaton, IMS Collection Series: Festschrift for Professor Morris L., 2013) corporate bond model, we make a comprehensive empirical credit risk analysis on individual corporate bonds (CBs) in the US energy sector, where cross-sectional CB and government bond price data is used with bond attributes. Applying the principal component analysis method to the S-CRiPSs, we also categorize individual CBs into three different groups and study on their characteristics of S-CRiPS fluctuations of each group in association with bond attributes. Secondly, using the market credit rating scheme proposed by Kariya et al. (2014), we make credit-homogeneous groups of CBs and show that our rating scheme is empirically very timely and useful. Thirdly, we derive term structures of default probabilities for each homogeneous group, which reflect the investors’ views and perspectives on the future default probabilities or likelihoods implicitly implied by the CB prices for each credit-homogeneous group. Throughout this paper it is observed that our credit risk models and the associated measures for individual CBs work effectively and can timely provide the market credit information evaluated by investors.     

Systemic Bank Risk in Brazil: A Comprehensive Simulation of Correlated Market, Credit, Sovereign and Inter-Bank Risks

In this study we present a comprehensive forward‐looking portfolio simulation methodology for assessing the correlated impacts of market risk, private sector and Sovereign credit risk, and inter‐bank default risk. In order to produce better integrated risk assessment for banks and systemic risk assessments for financial systems, we argue that reasonably detailed modeling of bank asset and liability structures, loan portfolio credit quality, and loan concentrations by sector, region and type, as well as a number of financial and economic environment risk drivers, is required. Sovereign and inter‐bank default risks are increasingly important in the current economic environment and their inclusion is an important model extension. This extended model is demonstrated through an application to both individual Brazilian banks (i.e., 28 of the largest banks) and groups of banks (i.e., the Brazilian banking system) as of December 2004. When omitting Sovereign risk, our analysis indicates that none of the banks face significant default risk over a 1‐year horizon. This low default risk stems primarily from the large amount of government securities held by Brazilian banks, but also reflects the banks' adequate capitalizations and extraordinarily high interest rate spreads. We note that none of the banks which we modeled failed during the very stressful 2007‐2008 period, consistent with our results. Our results also show that a commonly used approach of aggregating all banks into one single bank, for purposes of undertaking a systemic banking system risk assessment, results in a misestimate of both the probability and the cost of systemic banking system failures. Once Sovereign risk is considered and losses in the market value of government securities reach 10% (or higher), we find that several banks could fail during the same time period. These results demonstrate the well known risk of concentrated lending to a borrower, or type of borrower, which has a non‐zero probability of default (e.g., the Government of Brazil). Our analysis also indicates that, in the event of a Sovereign default, the Government of Brazil would face constrained debt management alternatives. To the best of our knowledge no one else has put forward a systematic methodology for assessing bank asset, liability, loan portfolio structure and correlated market and credit (private sector, Sovereign, and inter‐bank) default risk for banks and banking systems. We conclude that such forward‐looking risk assessment methodologies for assessing multiple correlated risks, combined with the targeted collection of specific types of data on bank portfolios, have the potential to better quantify overall bank and banking system risk levels, which can assist bank management, bank regulators, Sovereigns, rating agencies, and investors to make better informed and proactive risk management and investment decisions.     

Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data

Credit to the private sector has risen rapidly in European emerging markets, but its risk evaluation has been largely neglected. Using retail-loan banking data from the Czech Republic, we construct two credit risk models based on logistic regression and classification and regression trees. Both methods are comparably efficient and detect similar financial and socioeconomic variables as the key determinants of default behavior. We also construct a model without the most important financial variable (amount of resources), which performs very well. This way, we confirm significance of sociodemographic variables and link our results with specific issues characteristic to new EU members.     

Contagion risk in the Australian banking and property sectors

The Australian banking system emerged from the global crisis virtually unhurt, with most banks still profitable, adequately capitalized, and with AA credit ratings. Are there any risks or vulnerabilities in this success story? This paper analyzes Australia’s systemic banking risk and attempts to determine if this risk increased with the recent global crisis and whether this risk is related to the downturn experienced in the real estate market. We use extreme value theory to measure banks’ and property firms’ univariate Value at Risk, as well as multivariate intra-sector and inter-sector contagion risks. Of the 13 sectors analyzed, we find that the property sector exhibits the highest level of extremal dependence with the banking sector. The credit crisis significantly increased the probability of a bank or property firm crashing. Moreover, contagion risks significantly increased not only within the banking and property sectors, but also between those sectors.     

Credit default swap prices as risk indicators of listed German banks

This paper explores empirically the usefulness of credit default swap (CDS) prices as market indicators. The sample of reference entities consists of large, internationally active German banks and the observation period covers 3 years. By analysing the explanatory power of three risk sources: idiosyncratic credit risk, systematic credit risk and liquidity risk, we gain important insights into modeling the dynamics of CDS spreads. The impact of systematic risk, for example, has three components; one is related to the overall state of the economy, another related to the risk of the internationally active banking sector, and the third is an unobservable systematic factor. Default probabilities, inferred from a tractable reduced form model for CDS spreads, are compared with expected default frequencies from the Moody’s KMV model. The results lend empirical support to the hypothesis that structural models can be less informative than reduced-form models of CDS spreads in the case of banks with major investment banking activities as the leverage loses explanatory power. Although the CDS market appears to have matured over the observation period, during certain periods premiums for liquidity risk can increase substantially thus limiting the value of CDS spreads as market indicators. We conclude that equity prices and CDS premia should be considered together to fully exploit the information content of both market indicators and to mitigate their respective drawbacks.

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

Although 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.     

违约与杠杆周期——一个带有救助的金融加速器模型

我国为应对2008年国际金融危机的冲击采取了一系列经济刺激政策,在发挥“稳增长”作用的同时,也在一定程度上导致我国企业部门杠杆水平快速上升,但与此同时,不良贷款率并没有随企业部门杠杆的上升而显著增加。为了解释企业部门违约与杠杆的周期特征,本文在金融加速器模型(Bernanke et al.,1999)基础上,引入政府对企业部门的违约救助机制,建立DSGE模型进行讨论。进一步地,本文还通过一个不合意的去杠杆政策试验表明,忽略资产价格稳定(或者说金融稳定)前提下的去杠杆政策,反而会使企业部门的杠杆和违约率同时上升到一个较高水平。最后,引入一个盯住预期资产价格的动态救助规则能够发挥稳定经济的作用,并提高社会福利水平。     

Government interventions and default risk: Does one size fit all?

We examine the effectiveness of the financial sector rescue packages provided by the national governments during the 2008 financial crisis. This study questions the implicit assumption that government interventions have an uniform effect on the default risk of individual banks. After testing the results for sensitivity, our main findings suggest that there exists a significant negative relationship between the announcement of the financial sector rescue packages and the daily change of the credit default premium. However, quantile regressions show that the effectiveness of these packages differs across banks: most interventions do not decrease the risk of intermediate to low-risk banks, while they do reduce the risk of high-risk banks. Besides, we find that interventions aimed at specific financial institutions are more effective in restraining banking risk than broad interventions taken to stabilize the financial market as a whole.     

Predicting banking distress in the EMEAP economies

This study develops a panel probit model to identify the leading indicators of banking distress and to estimate the banking distress probability for EMEAP economies. Macroeconomic fundamentals, currency crisis vulnerability, credit risks of banks and non-financial companies, asset price gaps, credit growth, and the occurrence of distress in other economies are found to be important leading indicators. The model is applied to stress test the Hong Kong banking sector. Simulation results suggest that compared with the period before the Asian financial crisis, the banking sector is currently more capable of withstanding shocks similar to those that occurred during the crisis.     

A perspective on the symptoms and causes of the financial crisis

Prior to the 2007–2008 financial crisis, banking sector profits were very high but the profitability of financial intermediation was poor. Using a novel model of banking, this article argues that the high profits were achieved through balance sheet expansion and growing default, liquidity, and term risk mismatches between assets and liabilities. As a result, large banks’ financial leverage rose as they became less liquid, setting the conditions for a systemic banking crisis. This article argues that the increase in financial leverage was possible due to misguided changes in the regulatory framework, specifically, the Basel I capital accord and reductions in reserve requirements. Finally, this article overviews and assesses the policy response in the aftermath of the crisis.     

A practical approach to validating a PD model

The capital adequacy framework Basel II aims to promote the adoption of stronger risk management practices by the banking industry. The implementation makes validation of credit risk models more important. Lenders therefore need a validation methodology to convince their supervisors that their credit scoring models are performing well. In this paper we take up the challenge to propose and implement a simple validation methodology that can be used by banks to validate their credit risk modelling exercise. We will contextualise the proposed methodology by applying it to a default model of mortgage loans of a commercial bank in the Netherlands.     

Credit booms,banking crises,and the current account

A number of papers have shown that rapid growth in private sector credit is a strong predictor of a banking crisis. This paper will ask if credit growth is itself the cause of a crisis, or is it the combination of credit growth and external deficits? This paper estimates a probabilistic model to find the marginal effect of private sector credit growth on the probability of a banking crisis. The model contains an interaction term between credit growth and the level of the current account, so the marginal effect of private sector credit growth may itself be a function of the level of the current account. We find that the marginal effect of rising private sector debt levels depends on an economy's external position. When the current account is in balance, the marginal effect of an increase in debt is rather small. However, when the economy is running a sizable current account deficit, implying that any increase in the debt ratio is financed through foreign borrowing, this marginal effect is large.