Daniel Roesch

Stress-Testing Credit Risk Parameters: An Application to Retail Loan Portfolios

Financial institutions are faced with the challenge to forecast future credit portfolio losses. It is common practice to focus on portfolio models consisting of a limited set of parameters, such as the probability of default, asset correlation, loss given default or exposure at default. A simple portfolio model is also used in the Basel II framework for calculating regulatory capital. With regard to the stability of the financial system, these models have to be approved by regulators who have an interest in a conservative assessment of the credit portfolio risk and require the stress-testing of risk estimates. The present paper is the first in its kind to develop a framework to stress the smallest building block, the sensitivities of risk drivers and therefore any derivative such as a risk parameter or the credit portfolio loss. As a result, estimation uncertainties as well as the correlations are taken into account. In an empirical analysis, the stress scenarios for different loan categories are analyzed for US retail borrowers and the implications on economic as well as regulatory capital explored.

Uses and Misuses of Measures for Credit Rating Accuracy

The New Basel Capital Accord will allow the determination of banks’ regulatory capital requirements due to default probabilities which are estimated and forecasted from internal ratings. External ratings from rating agencies play fundamental roles in capital and credit markets. Discriminatory power of internal and external ratings is a key requirement for the soundness of a rating system in general and for the acceptation of a bank’s internal Rating systems under Basel II. Statistics such as the area under a receiver operating characteristic or the accuracy ratio, are widely used in practice as measures for the performance. This note shows that such measures should only be interpreted with caution. Firstly, the outcomes of the measures depend not only on the discrimination power of the rating system but mainly on the structure of the portfolio under consideration. Thus, the absolute values achieved do not measure the performance of a rating system solely. Secondly, comparisons of the outcomes between different portfolios, different time periods or both may be misleading. As a positive result we show that the value achieved by a rating system which predicts all default probabilities correctly can not be beaten.

Downturn Credit Portfolio Risk, Regulatory Capital and Prudential Incentives

This paper analyzes the level and cyclicality of bank capital requirement in relation to (i) the model methodologies through-the-cycle and point-in-time, (ii) four distinct downturn loss rate given default concepts, and (iii) US corporate and mortgage loans. The major finding is that less accurate models may lead to a lower bank capital requirement for real estate loans. In other words, the current capital regulations may not support the development of credit portfolio risk measurement models as these would lead to higher capital requirements and hence lower lending volumes. The finding explains why risk measurement techniques in real estate lending may be less developed than in other credit risk instruments. In addition, various policy recommendations for prudential regulators are made.

Mitigating Procyclicality in Basel II: A Value at Risk Based Remedy

A side-effect of the better differentiation of credit risk in the New Basel Capital Accord is the danger of a sharp rise of capital requirements in recessions due to a large number of borrower downgrades and defaults. Thus, the Accord may worsen recessions. In the present paper these worries about Basel II are analyzed in some detail. A “historical simulation” is calculated with S&P’s transition and default rates from 1982 to 2000. In accordance with the literature it turns out that procyclicality may be substantial - at least due to an increasing number of defaults in recessions. A solution is suggested which “buffers” the cyclicality effect by considering simple Value-at-Risk calculations. Its main advantage is the transparent reflection of a bank’s actual risk and the retention of risk sensitive weights - an essential goal of Basel II.

Rating Performance and Agency Incentives of Structured Finance Transactions

The mismatch between credit ratings o fstructured finance transactions and their true risks has been a source of the Global Financial Crisis which manifested in criticism of models and techniques applied by credit rating agencies (CRA). This paper provides an empirical study which assesses the historical performance of credit ratings for structured finance transactions and finds that CRAs do not include all factors explaining securitization impairment risk. In addition, CRA ratings for selected asset categories underestimate risk in origination years when the fee revenue is high.

Systemic Risk in Commercial Bank Lending

This paper develops a bank model for financial systemic risk in bank lending. The model analyzes the impact of a financial institution failure on the distribution of losses in the financial system. The fundamental idea is that bank loss rates may be decomposed into a level, momentum, systematic and systemic component. Financial institutions fail when unexpected losses exceed the capital buffer and the release of capital allocated to credits. Failed financial institutions pass these loss exceedances on to creditors, deposit insurance schemes or the general public. The benefits of the presented model framework are (i) the identification of systemically relevant financial institutions, and (ii) the measurement of the size of safety nets in terms of attachment likelihood and expected losses given attachment. The model is generally applicable as it does not rely on financial market data. The empirical evidence presented is based on information collected by US prudential regulators from 1997 to 2012. The parameter estimation is based on a novel maximum likelihood technique to derive the parameters in a non-linear mixed model with multiple random effects.

Modelling and Predicting of Australian Mortgage Delinquency Risk: A Preliminary Data Analysis

This paper employs the parametric probit regression model, estimates the probability of default (PD) of Australian mortgages, and examines the nature of the relationships between the PD and some loan level variables such as loan-to-value ratio (LVR), loan documentation, loan type, loan purpose, and state. The data covers a cross-section of 25,537 mortgage loans, which were originated in the years 2004 to 2010. The data set has 694 default events defined by the delinquency of the mortgage borrower. In this preliminary analysis, we find that the parametric model specification does not capture the underlying relationships between the dependent variable PD and the other variables included in the model. In addition, we find that the PD and the LVR, which is known to be a key determinant of mortgage default, have a nonlinear relationship that is not fully captured by the probit model. Despite many forms of parametric nonlinear models being available in the literature, the process of finding a suitable parametric nonlinear model may not lead to a model that would capture the true nonlinear relationship between the PD and LVR. To overcome this problem, in our future research, we will assume an unknown functional form for this relationship, and then propose an estimation method for this semi parametric probit model. Based on the overall findings of our preliminary analysis, we provide a roadmap for the future research directions on robust modelling and predicting the PD of Australian mortgages, and for the need to expand the size of the data and the variables sets.

Downturn Risk: Another View on the Current Financial Crisis

The current financial crisis had its origins in the US subprime mortgage market and led to downturns in global equity, credit and commodity markets. This paper identifies the lack of economic information in risk valuation models as one reason why the financial industry was unable to predict, mitigate and cover the current losses. This is at first sight rather surprising as credit and credit derivative products have existed for centuries. However, the markets have experienced an exponential growth in size as well as variety. In particular, the associated transparency may have not matched this development in relation to the underlying risks, risk models and model risks.

Empirical Performance of LGD Prediction Models

The Global Financial Crisis highlighted that default and recovery rates of multiple borrowers generally deteriorate jointly during economic downturns. The vast majority of the literature, as well as many industry credit portfolio risk models ignore this and analyze default probabilities and recoveries in the event of default separately. As a result, the models project losses which are too low in economic downturns such as the recent financial crisis. Nevertheless, alternatives of incorporating the dependence between probabilities of default and recovery rates have been proposed. This paper is the first of its kind to assess the performance of these structurally different approaches. Four banks using different estimation procedures are compared. We use RMSE and RAE to measure the predictive accuracy of each procedure. The results show, that indeed models accounting for the correlation of default and recovery perform better than models ignoring it.

Risikofaktoren und Korrelationen für Bonitätsveränderungen (Risk Factors and Correlations for Credit Quality Changes)

Bei der Modellierung von Kreditportfoliorisiken stellt die Quantifizierung von Korrelationen zwischen Ausfallen bzw. Bonitatsveranderungen eine zentrale Herausforderung dar. Es last sich zwischen direkten und indirekten Modellierungsansatzen unterscheiden. Wahrend erstere den Korrelationsparameter direkt spezifizieren, kommen in indirekten Modellen die Korrelationen uber Exposures gegenuber gemeinsamen Risikofaktoren zustande. Gegeben die Werte dieser Risikofaktoren werden die Adressen als bedingt unabhangig angenommen. Die Identitat solcher Risikofaktoren ist jedoch bislang noch nicht geklart. Ziel dieses Beitrags ist es, im Rahmen eines neuen, dynamischen Ansatzes diese Risikofaktoren zu identifizieren und die bedingte Unabhangigkeitsannahme zu uberprufen. Die durchgefuhrte Studie stutzt die bedingte Unabhangigkeitsannahme, was erhebliche Vereinfachungen bei Value-at-Risk Analysen bedeutet. Weiterhin geben die Ergebnisse Hinweise darauf, das eine dynamische Modellierung von Kreditrisiken gegenuber der bislang vorherrschenden statischen Sichtweise zu bevorzugen ist.One of the greatest challenges in modeling credit portfolio risk is the issue of correlations between borrowers. Up to now no consistent methodology for identifying correlations exists. In general two approaches are employed: “direct” and “indirect” modeling. While the former specify correlation parameters themselves, indirect models assume that correlations between credit qualities or defaults are due to exposures to common risk factors. Given the values of the risk factors borrowers are assumed to be conditionally independent. However, the identity of these risk factors is still ambiguous. We present a new dynamic approach which identifies these common factors and tests the assumption of conditional independence. Our empirical study supports this assumption. This considerably facilitates Value-at-Risk analyses. Furthermore the results indicate that a dynamic modeling of credit risk should be favored against the prevalent static setting.