For the risk assessment of credit portfolios single-period credit portfolio models are by now widely accepted and used in the practical analysis of loan respectively bonds books in the context of capital modeling. But already Finger (2000) pointed to the role of inter-period correlation in structural models and Thompson, McLeod, Teklos and Gupta (2005) strongly advocated that it is ‘time for multi-period capital models’. With the emergence of structured credit products like CDOs the default-times/Gaussian-copula framework became standard for valuing and quoting liquid tranches at different maturities, Bluhm, Overbeck and Wagner (2002). Although it is known that the standard Gaussian-copula-default-times approach has questionable term structure properties the approach is quite often also used for the risk assessment by simply switching from a risk-neutral to a historical or subjective default measure.
From a pricing perspective Andersen (2006) investigates term structure effects and inter-temporal dependencies in credit portfolio loss models as these characteristics become increasingly important for new structures like forwardstart CDOs. But the risk assessment is also affected by inter-temporal dependencies. For the risk analysis at different time horizons the standard framework is not really compatible with a single-period correlation structure; Morokoff (2003) highlighted the necessity for multi-period models in that case. Long-only investors in the bespoke tranche market with a risk-return and hold-to-maturity objective have built in the past CDO books with various vintage and maturity years, based only on a limited universe of underlying credits with significant overlap between the pools. A proper assessment of such a portfolio requires a consistent multi-period portfolio framework with reasonable inter-temporal dependence. Similarly, an investor with a large loan or bond book, enhanced with non-linear credit products, needs a reliable multi-period model with sensible inter-temporal properties as both bond or structured investments display different term structure characteristics.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Bibliography
Andersen, L. (2006). Portfolio Losses in Factor Models: Term Structures and Inter-temporal Loss Dependence, working paper, http://www.defaultrisk.com, 2006.
Bluhm, C., Overbeck, L. and Wagner, C. 2002. Introduction to Credit Risk Modeling, Chapman & Hall/CRC.
Bluhm, C. and Overbeck, L. 2006. Structured Credit Portfolio Analysis, Baskets and CDOs, Chapman and Hall.
Finger, C. 2000. A comparison of stochastic default rate models, RiskMetrics Journal, 1: 49-73.
Hull, J. and White, A. 2001. Valuing credit default swaps II: Modeling default correlations, Journal of Derivatives 8(3): 12-21.
Kalkbrener, M., Lotter, H. and Overbeck, L. 2004. Sensible and efficient capital allocation for credit portfolios, RISK 17(1): 19-24.
Tabe, R. and Rosa, D. (2004). Moody‘s Capital Model, Moody‘s.
Thompson, K., McLeod, A., Teklos, P. and Gupta, S. 2005. Time for multi-period capital models, RISK 74-78.
Morokoff, W. (2003). Simulation methods for risk analysis of collateralized debt obligations, Moody’s KMV New Product Research Publication.
Overbeck, L. and Schmidt, W. 2005). Modeling default dependence with threshold models, Journal of Derivatives 12(4): 10-19.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Berlin Heidelberg
About this chapter
Cite this chapter
Wagner, C.K.J. (2009). Cross- and Autocorrelation in Multi-Period Credit Portfolio Models. In: Härdle, W.K., Hautsch, N., Overbeck, L. (eds) Applied Quantitative Finance. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69179-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-540-69179-2_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69177-8
Online ISBN: 978-3-540-69179-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)