Modeling and Calibration Errors in Measures of Portfolio Credit Risk

Tarashev, Nikola; Zhu, Hao

doi:10.2139/ssrn.1001939

Cited by 37 publications

(41 citation statements)

References 22 publications

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“…The number of factors, therefore, which is the only difference between these two models, appears to play a relatively minor role. This finding for the impact of the number of factors is consistent with recent results obtained by Tarashev and Haibin (2007).…”

Section: Var (%)supporting

confidence: 93%

Asset correlations and credit portfolio risk: an empirical analysis

Düllmann¹,

Scheicher²,

Schmieder³

2008

JCR

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In credit risk modelling, the correlation of unobservable asset returns is a crucial component for the measurement of portfolio risk. In this paper, we estimate asset correlations from monthly time series of Moody's KMV asset values for around 2,000 European firms from 1996 to 2004. We compare correlation and value-atrisk (VaR) estimates in a one-factor or market model and a multi-factor or sector model. Our main finding is a complex interaction of credit risk correlations and default probabilities affecting total credit portfolio risk. Differentiation between industry sectors when using the sector model instead of the market model has only a secondary effect on credit portfolio risk, at least for the underlying credit portfolio. Averaging firm-dependent asset correlations on a sector level can, however, cause a substantial underestimation of the VaR in a portfolio with heterogeneous borrower size. This result holds for the market as well as the sector model. Furthermore, the VaR of the IRB model is more stable over time than the VaR of the market model and the sector model, while its distance from the other two models fluctuates over time. Keywords: Asset correlations, sector concentration, credit portfolio risk JEL Classification: G 21, C 15 Non-Technical SummaryThe correlations between two firms' asset-value returns, commonly referred to as asset correlation, are a key factor in measuring the credit risk of loan portfolios.Since asset values are not directly observable, we employ time series of asset values of European firms which are based on the Moody's KMV model. A descriptive analysis of these asset correlations and correlations with industry sector indices is a first contribution of this paper. We observe a considerable fluctuation of asset correlations which suggests further research on their stability over time. The second contribution is a comprehensive analysis how asset correlations as input parameters into a credit risk model affect the value-at-risk which is a measure of credit risk for a portfolio. We observe that borrower-dependent asset correlations produces a substantially higher value-at-risk than median asset correlations computed on a sector level. We attribute this finding mainly to the empirical fact that asset correlations tend to increase with borrower size, which means that sector averages understate the correlation effect. We conclude that the way asset correlations are used in the credit risk model also has a substantial impact on the risk assessment of a portfolio. This methodological challenge adds to the empirical challenge of estimating asset correlations reliably. Furthermore, our results suggest that the regulatory capital charge of the internal ratings-based approach of Basel II is less volatile over time than value-at-risk in the other credit risk models in our study. Nichttechnische Zusammenfassung

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Section: Var (%)supporting

confidence: 93%

Asset correlations and credit portfolio risk: an empirical analysis

Düllmann¹,

Scheicher²,

Schmieder³

2008

JCR

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“…The price of insurance against distress equals the expectation (under the risk-neutral world) of portfolio credit losses that equal or exceed the predetermined threshold. To estimate this expectation, they rely on the Monte Carlo method described in appendix B of Tarashev and Zhu (2008), which is summarized below.…”

Section: E21 Definitionmentioning

confidence: 99%

A Survey of Systemic Risk Analytics

Bisias

Flood

et al. 2012

Annu. Rev. Financ. Econ.

523

180

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The Office of Financial Research (OFR) Working Paper Series allows staff and their co-authors to disseminate preliminary research findings in a format intended to generate discussion and critical comments. Papers in the OFR Working Paper Series are works in progress and subject to revision. We provide a survey of 31 quantitative measures of systemic risk in the economics and finance literature, chosen to span key themes and issues in systemic risk measurement and management. We motivate these measures from the supervisory, research, and data perspectives in the main text, and present concise definitions of each risk measure-including required inputs, expected outputs, and data requirements-in an extensive appendix. To encourage experimentation and innovation among as broad an audience as possible, we have developed open-source Matlab code for most of the analytics surveyed, which can be accessed through the Office of Financial Research (OFR) at http://www.treasury.gov/ofr.

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“…16 The nth-to-default probability does not have this property, see Section 4 for further discussion. 17 See Tarashev and Zhu (2008b), Appendix B, for the details of the Monte Carlo simulation procedure. 18 The adoption of symmetric triangular distribution follows Tarashev and Zhu (2008b) and is not essential to our results.…”

Section: Designing Stress Testing Scenariosmentioning

confidence: 99%

A framework for assessing the systemic risk of major financial institutions

Huang

Zhou

Zhu³

2009

Journal of Banking & Finance

Self Cite

545

311

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a b s t r a c tIn this paper we propose a framework for measuring and stress testing the systemic risk of a group of major financial institutions. The systemic risk is measured by the price of insurance against financial distress, which is based on ex ante measures of default probabilities of individual banks and forecasted asset return correlations. Importantly, using realized correlations estimated from high-frequency equity return data can significantly improve the accuracy of forecasted correlations. Our stress testing methodology, using an integrated micro-macro model, takes into account dynamic linkages between the health of major US banks and macro-financial conditions. Our results suggest that the theoretical insurance premium that would be charged to protect against losses that equal or exceed 15% of total liabilities of 12 major US financial firms stood at $110 billion in March 2008 and had a projected upper bound of $250 billion in July 2008.

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Modeling and Calibration Errors in Measures of Portfolio Credit Risk

Cited by 37 publications

References 22 publications

Asset correlations and credit portfolio risk: an empirical analysis

Asset correlations and credit portfolio risk: an empirical analysis

A Survey of Systemic Risk Analytics

A framework for assessing the systemic risk of major financial institutions

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