Credit portfolio risk and asset price cycles

Computational Management Science - Tập 5 - Trang 337-354 - 2007
Klaus Rheinberger1, Martin Summer2
1Research Center Process and Product Engineering, University of Applied Sciences Vorarlberg, Dornbirn, Austria
2Economic Studies Division, Oesterreichische Nationalbank, Wien, Austria

Tóm tắt

It is a stylized fact that credit risk is high at the same time when asset values are depressed. However, most of the standard credit risk models ignore this kind of correlation, leading to underestimation of risk measures of portfolio credit risk such as Value at Risk and Expected Shortfall. In our paper we make an attempt to quantify the underestimation of these risk measures when the dependence between credit risk and asset values is ignored and show that credit risk is underestimated by a significant margin.

Tài liệu tham khảo

Acharya V, Sreedar B, Srinivasan A (2003) Understanding the recovery rates on defaulted securities. Working Paper Altman E, Resti A, Sironi A (2003) Default recovery rates in credit risk modeling: a review of the literature and empirical evidence. Working Paper Altman E, Brady B, Resti A, Sironi A (2004) The link between default and recovery rates: theory, empirical evidence and implications. Working Paper Arthur S (2004) Experience with constructing composite asset price indices. BIS Bakshi G, Madan D, Zhang F (2001) Understanding the role of recovery in default risk models: Empirical comparisons and implied recovery rates. Finance and economics discussion Series, Federal Reserve Board of Governors Bernanke B, Gilchrist S (1999) The financial accelerator in a quantitative business cycle framework. In Taylor JB, Woodford M (eds) Handbook of macroeconomics, vol 1C, pp 1341–1393 Bordo M, Eichengreen B, Klingebiel D, Martinez-Peria MS (2001) Is the crises problem growing more severe?. Econ Policy 32:51–82 Borio C (2002) Towards a macro-prudential framework for financial supervision and regulation? CESifo Lecture, Summer institute: banking regulation and financial stability, Venice Borio C, Kennedy N, Prowse SD (1994) Exploring aggregate asset price fluctuations across countries: measurement, determinants and monetary policy implications. BIS Economic Papers, (40) Borwein J, Goodrich R, Limber M (1996) A comparison of entropies in the undetermined moment problem. Working Paper, Department of Mathematics, Boulder Carey M, Gordy M (2003) Systematic risk in recoveries on defaulted debt. mimeo, Federal Reserve Board, Whashington Chib S, Greenberg E (1995) Understanding the Metropolis-Hastings Algorithm. Am Stat 49(4):327–335 Crouhy M, Galai D, Mark R (2000) A comparative analysis of current credit risk models. J Banking Finance 24(1–2):59–117 DiCiccio T, Efron B (1996) Bootstrap confidence intervals. Stat Sci 11(3):189–228 Duellmann K, Trapp M (2004) Systematic risk in recovery rates: An empirical analysis of us corporate credit exposures. Deutsche Bundesbank, Discussion Paper Finger C (1999) Conditional approaches for creditmetrics portfolio distributions creditmetrics monitor Frye J (2000a) Collateral damage. Risk pages 91–94 Frye J (2000b) Collateral damage detected. Federal Reserve bank of Chicago, Working Paper, pp 1–14 Goodhart C, Hofmann B, Segoviano M (2004) Bank regulation and macroeconomic fluctuations. Oxford Rev Econ Policy 20(4):591–615 Gordy M (2000) A comparative anatomy of credit risk models. J Banking Finance:119–149 Grunert J, Weber M (2005) Recovery rates of bank loans: empirical evidence for germany. University of Mannheim, Working Paper Gürtler M, Heithecker D (2005) Systematic credit cycle risk of financial collaterals: modelling and evidence. TU Braunschweig, Working Paper Hellwig M (1995) Systemic aspects of risk management in banking and finance. Schweizerische Zeitschrift fuer Volkswirtschaft und Statistik/Swiss. J Econ Stat 131(4/2):723–737 Hu Y-T, Perraudin W (2002) The dependence of recovery rates and defaults. Birkbeck College Jarrow R (2001) Default parameter estimation using market prices. Financ Anal J 57(5):75–92 Jaynes ET (1957) Information theory and statistical mechanics. Phys Rev 106(108):171–190 Jokivuolle E, Peura S (2003) A model for estimationg recovery rates and collateral haircuts for bank loans. Eur Financ Manage (forthcoming) Kyotaki N, Moore J (1997) Credit cycles. J Polit Econ 105:211–248 Marechal P (1998) On the principle of maximum entropy on the mean as a methodology for the regularization of inverse problems. The Pacific Insitute for the Mathematical Sciences (8) McNeil A, Frey R, Embrechts P (2005) Quantitative risk management: concepts, techniques and tools. Princeton University Press, New Jersey Merino S, Nyfeler M (2004) Numerical techniques for determining portfolio credit risk. In: Grundlach M, Lehrbass F (eds) CreditRisk+ in the banking industry. Springer, Heidelberg, pp. 279–311 Molins J, Vives E (2004) Long range ising model for credit risk modeling in homogeneous portfolios. URL http://arxiv.org/abs/cond-mat/0401378. Schuermann T (2004) What do we know about loss given default? Wharton, Working paper Segoviano M (2004) Consistent information multivariate density optimizing methodology. Mimeo