Stress Testing Credit Risk: Is the Czech Republic Different from Germany?

This study deals with credit risk modelling and stress testing within the context of a Merton-type one-factor model. We analyse the corporate and household sectors of the Czech Republic and Germany to find determining variables of credit risk in both countries. We find that a set of similar variables explains corporate credit risk in both countries despite substantial differences in the default rate pattern. This does not apply to households, where further research seems to be necessary. Next, we establish a framework for the stress testing of credit risk. We use a country specific stress scenario that shocks macroeconomic variables with medium severity. The test results in credit risk increasing by more than 100% in the Czech Republic and by roughly 40% in Germany. The two outcomes are not fully comparable since the shocks are calibrated according to the historical development of the time series considered and the size of the shocks for the Czech Republic was driven by the transformation period.Credit risk, credit risk modelling, stress testing.

Conservative Stress Testing: The Role of Regular Verification

This paper focuses on how to calibrate models used to stress test the most important risks in the banking system. Based on the results of a verification of the Czech National Bank’s stress testing methodology, the paper argues that stress tests should be calibrated conservatively and slightly overestimate the risks. However, to ensure that the stress test framework is conservative enough over time, a verification, i.e. comparison of the actual values of key banking sector variables – in particular the capital adequacy ratio – with predictions generated by the stress-testing models should become a standard part of the stress-testing framework.stress testing; credit risk; bank capital

A Critical Review Of The Basel Margin Of Conservatism Requirement In A Retail Credit Context

The Basel II accord (2006) includes guidelines to financial institutions for the estimation of regulatory capital (RC) for retail credit risk. Under the advanced Internal Ratings Based (IRB) approach, the formula suggested for calculating RC is based on the Asymptotic Risk Factor (ASRF) model, which assumes that a borrower will default if the value of its assets were to fall below the value of its debts. The primary inputs needed in this formula are estimates of probability of default (PD), loss given default (LGD) and exposure at default (EAD). Banks for whom usage of the advanced IRB approach have been approved usually obtain these estimates from complex models developed in-house. Basel II recognises that estimates of PDs, LGDs, and EADs are likely to involve unpredictable errors, and then states that, in order to avoid over-optimism, a bank must add to its estimates a margin of conservatism (MoC) that is related to the likely range of errors. Basel II also requires several other measures of conservatism that have to be incorporated. These conservatism requirements lead to confusion among banks and regulators as to what exactly is required as far as a margin of conservatism is concerned. In this paper, we discuss the ASRF model and its shortcomings, as well as Basel II conservatism requirements. We study the MoC concept and review possible approaches for its implementation. Our overall objective is to highlight certain issues regarding shortcomings inherent to a pervasively used model to bank practitioners and regulators and to potentially offer a less confusing interpretation of the MoC concept

Modeling a Distribution of Mortgage Credit Losses

One of the biggest risks arising from financial operations is the risk of counterparty default, commonly known as a “credit risk”. Leaving unmanaged, the credit risk would, with a high probability, result in a crash of a bank. In our paper, we will focus on the credit risk quantification methodology. We will demonstrate that the current regulatory standards for credit risk management are at least not perfect, despite the fact that the regulatory framework for credit risk measurement is more developed than systems for measuring other risks, e.g. market risks or operational risk. Generalizing the well known KMV model, standing behind Basel II, we build a model of a loan portfolio involving a dynamics of the common factor, influencing the borrowers’ assets, which we allow to be non-normal. We show how the parameters of our model may be estimated by means of past mortgage deliquency rates. We give a statistical evidence that the non-normal model is much more suitable than the one assuming the normal distribution of the risk factors. We point out how the assumption that risk factors follow a normal distribution can be dangerous. Especially during volatile periods comparable to the current crisis, the normal distribution based methodology can underestimate the impact of change in tail losses caused by underlying risk factors.Credit Risk, Mortgage, Delinquency Rate, Generalized Hyperbolic Distribution, Normal Distribution

The New Basel Capital Accord and Questions for Research

The New Basel Accord for bank capital regulation is designed to better align regulatory capital to the underlying risks by encouraging better and more systematic risk management practices, especially in the area of credit risk. We provide an overview of the objectives, analytical foundations and main features of the Accord and then open the door to some research questions provoked by the Accord. We see these questions falling into three groups: what is the impact of the proposal on the global banking system through possible changes in bank behavior; a set of issues around risk analytics such as model validation, correlations and portfolio aggregation, operational risk metrics and relevant summary statistics of a bank’s risk profile; issues brought about by Pillar 2 (supervisory review) and Pillar 3 (public disclosure).Bank capital regulation, risk management, credit risk, operational risk

Regulatory Capital Modelling for Credit Risk

Abstract. The Basel II internal ratings-based (IRB) approach to capital adequacy for credit risk plays an important role in protecting the Australian banking sector against insolvency. We outline the mathematical foundations of regulatory capital modelling for credit risk, and extend the model specification of the IRB approach to a more general setting than the usual Gaussian case. It rests on the proposition that quantiles of the distribution of conditional expectation of portfolio percentage loss may be substituted for quantiles of the portfolio loss distribution. We present a more compact proof of this proposition under weaker assumptions. The IRB approach implements the so-called asymptotic single risk factor (ASRF) model, an asset value factor model of credit risk. The robustness of the model specification of the IRB approach to a relaxation in model assumptions is evaluated on a portfolio that is representative of the credit exposures of the Australian banking sector. We measure the rate of convergence, in terms of number of obligors, of empirical loss distributions to the asymptotic (infinitely fine-grained) portfolio loss distribution; and we evaluate the sensitivity of credit risk capital to dependence structure as modelled by asset correlations and elliptical copulas. A separate time series analysis takes measurements from the ASRF model of the prevailing state of Australia's economy and the level of capitalisation of its banking sector. These readings find general agreement with macroeconomic indicators, financial statistics and external credit ratings. However, given the range of economic conditions, from mild contraction to moderate expansion, experienced in Australia since the implementation of Basel II, we cannot attest to the validity of the model specification of the IRB approach for its intended purpose of solvency assessment. With the implementation of Basel II preceding the time when the effect of the financial crisis of 2007-09 was most acutely felt, our empirical findings offer a fundamental assessment of the impact of the crisis on the Australian banking sector. Access to internal bank data collected by the prudential regulator distinguishes our research from other empirical studies on the IRB approach and recent crisis

Assessing the Basel II Internal Ratings-Based Approach: Empirical Evidence from Australia

The Basel II internal ratings-based (IRB) approach to capital adequacy for credit risk implements an asymptotic single risk factor (ASRF) model. Measurements from the ASRF model of the prevailing state of Australia's economy and the level of capitalisation of its banking sector find general agreement with macroeconomic indicators, financial statistics and external credit ratings. However, given the range of economic conditions, from mild contraction to moderate expansion, experienced in Australia since the implementation of Basel II, we cannot attest to the validity of the model specification of the IRB approach for its intended purpose of solvency assessment. With the implementation of Basel II preceding the time when the effect of the financial crisis of 2007-09 was most acutely felt, our empirical findings offer a fundamental assessment of the impact of the crisis on the Australian banking sector. Access to internal bank data collected by the prudential regulator distinguishes our research from other empirical studies on the IRB approach and recent crisis.Comment: Addressed critiques of the Basel II IRB approach in the literature and updated figures, as well as general editing to tighten the pros

Lessons learned from the financial crisis for financial stability and banking supervision

The financial crisis that began in 2007 has revealed a need for a new supervisory and regulatory approach aimed at strengthening the system and containing the risk of future financial and economic disruptions. Three ingredients are needed to ensure financial stability: robust analysis, better regulation, and international cooperation. First, financial stability analysis must be improved to take full account of the different sources of systemic risk. Data coverage of the balance sheets of both non-bank financial institutions and the non-financial sectors should be increased. Moreover, to address the problems raised by the interconnections among financial institutions more granular and timely information on their exposures is needed. There must be further integration of macro- and micro-information and an upgrading of financial stability models. The second ingredient is the design of robust regulatory measures. Under the auspices of the G20 and the Financial Stability Board, the Basel Committee on Banking Supervision recently put forward substantial proposals on capital and liquidity. They will result in more robust capital base, lower leverage, less cyclical capital rules and better control of liquidity risk. Finally, the third ingredient is strong international cooperation. Ensuring more effective exchanges of information among supervisors in different jurisdictions and successful common actions is key in preserving financial integration, while avoiding negative cross-border spill-overs. Better resolution regimes are part of the efforts to ensure that the crisis of one institution does not impair the ability of the financial markets to provide essential services to the economy.financial crisis, international cooperation, macroprudential analysis, procyclicality, prudential regulation, stress tests

Analysis of operational risk of banks – catastrophe modelling

Nowadays financial institutions due to regulation and internal motivations care more intensively on their risks. Besides previously dominating market and credit risk new trend is to handle operational risk systematically. Operational risk is the risk of loss resulting from inadequate or failed internal processes, people and systems or from external events. First we show the basic features of operational risk and its modelling and regulatory approaches, and after we will analyse operational risk in an own developed simulation model framework. Our approach is based on the analysis of latent risk process instead of manifest risk process, which widely popular in risk literature. In our model the latent risk process is a stochastic risk process, so called Ornstein- Uhlenbeck process, which is a mean reversion process. In the model framework we define catastrophe as breach of a critical barrier by the process. We analyse the distributions of catastrophe frequency, severity and first time to hit, not only for single process, but for dual process as well. Based on our first results we could not falsify the Poisson feature of frequency, and long tail feature of severity. Distribution of “first time to hit” requires more sophisticated analysis. At the end of paper we examine advantages of simulation based forecasting, and finally we concluding with the possible, further research directions to be done in the future

Australian Bank Credit Risk Management: A Regulatory Examination of Provisioning, Capital Adequacy & Stress-Testing

Recent widespread turmoil in the banking sector has renewed the notion that improper credit risk management continues to be the catalyst for serious banking problems. The Australian banking sector emerged relatively unscathed from recent global turmoil, providing an interesting setting for research. This thesis investigates the credit risk management practice of Australian banks’ loan portfolios from a regulatory perspective by examining regulatory loan-loss provisioning, regulatory capital, and regulatory stress-testing in Australia. Findings suggest the potential for Australian banks to under-provision when faced with pressure to raise capital ratios. IRB banks, on the other hand, report regulatory provisions in line with expected loss estimates as intended under the Basel II capital framework. Australian bank supervisors are encouraged to remain vigilant when assessing bank capital adequacy by paying particular attention to the sufficiency of bank regulatory provisioning practices as a reserve against expected credit losses. This thesis finds Australian banks target a level of regulatory capital above the imposed regulatory minimum, with quarterly speed of adjustment coefficients of 19 and 15 per cent for total and tier 1 capital ratios, respectively. Bank risk, size and ROE are found to be significant determinants of Australian bank capital buffers and Basel II is found to have increased bank capital buffers. Findings suggest a positive relationship exists between the business cycle and Australian bank capital buffers, interpreted to be a countercyclical effect and results indicate bank-specific regulatory imposed PCRs are having their intended effect on capital ratios. An investigation into current regulatory stress-testing practice reveals that variation in the performance between banks is widely disregarded. A simulation experiment suggests that once key stress-testing variables are decomposed from mean values into their empirically fitted distributions, the increase in average banking system losses is substantial. Both the average and worst performing group of banks suffer a significantly greater magnitude of loss. Under a severe stress scenario of a 30 per cent decline in the property index and a 10 per cent default rate, failing to decompose the mean results in an underestimation of average bank losses of 1.86 per cent of total assets. The worst performing group of banks have losses underestimated by a total of 2.32 per cent of total assets, a substantial amount of loss with significant implications for current regulatory stress-testing practice. This thesis provides insights into the credit risk management practices of a banking sector which, in recent times, has outperformed global counterparts. The final implication of the findings of this thesis is relevant for all authorities with vested interest in the resilience of worldwide banking systems. For proper management of credit risk, regulatory provisioning, regulatory capital and regulatory stress-testing cannot be assessed in isolation, a simultaneous assessment is essential. This thesis provides an essential step in promoting a greater awareness of the interrelations between these three components in the Australian banking system