Sharp Probability Tail Estimates for Portfolio Credit Risk

Jeffrey F. Collamore (), Hasitha de Silva and Anand N. Vidyashankar
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Jeffrey F. Collamore: Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen, Denmark
Hasitha de Silva: Department of Mathematics, George Mason University, 4000 University Drive, Fairfax, VA 22030, USA
Anand N. Vidyashankar: Department of Statistics, George Mason University, 4000 University Drive, Fairfax, VA 22030, USA

Risks, 2022, vol. 10, issue 12, 1-20

Abstract: Portfolio credit risk is often concerned with the tail distribution of the total loss, defined to be the sum of default losses incurred from a collection of individual loans made out to the obligors. The default for an individual loan occurs when the assets of a company (or individual) fall below a certain threshold. These assets are typically modeled according to a factor model, thereby introducing a strong dependence both among the individual loans, and potentially also among the multivariate vector of common factors. In this paper, we derive sharp tail asymptotics under two regimes: (i) a large loss regime , where the total number of defaults increases asymptotically to infinity; and (ii) a small default regime , where the loss threshold for an individual loan is allowed to tend asymptotically to negative infinity. Extending beyond the well-studied Gaussian distributional assumptions, we establish that—while the thresholds in the large loss regime are characterized by idiosyncratic factors specific to the individual loans—the rate of decay is governed by the common factors. Conversely, in the small default regime, we establish that the tail of the loss distribution is governed by systemic factors. We also discuss estimates for Value-at-Risk, and observe that our results may be extended to cases where the number of factors diverges to infinity.

Keywords: large deviations; tail approximations; Value-at-Risk; Expected Shortfall; model uncertainty; multiple types; risk management (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
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