Simulation-analytical approach for calculating VaR contributions in credit portfolios

Jayden Zian Wang, Zhenzhen Huang and Yue Kuen Kwok

Quantitative Finance, 2026, vol. 26, issue 5, 743-759

Abstract: Risk capital allocation involves the decomposition of the overall portfolio risk in a credit portfolio into marginal risk contributions associated with individual obligors. The Value-at-Risk contribution (VaRC) measures how much each obligor contributes to the overall portfolio VaR. We propose two forms of simulation-analytical approach to calculate VaRC in Gaussian copula credit portfolios with correlation between probability of default (PD) and loss given default (LGD). Our method resolves the challenge of computing the expectation of the default loss of an individual obligor conditional on an event with zero probability mass. By employing ingenious analytical and simulation procedures, we recast the VaRC calculation to involve simulating the distribution function of the random portfolio loss, thereby avoiding numerical instabilities in the common kernel estimation method of computing expectations conditional on a zero-probability event. The inclusion of an analytical component in our algorithms helps reduce simulation effort when compared with the full simulation algorithms. Numerical experiments demonstrate that our proposed algorithms perform favorably well in terms of accuracy and computational efficiency, outperforming typical direct Monte Carlo simulation methods such as the iterative cross entropy method.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2026.2641764 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:26:y:2026:i:5:p:743-759

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2026.2641764

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().