 Model-free computation of risk contributions in credit portfoliosÁlvaro Leitao and Luis Ortiz-Gracia Applied Mathematics and Computation, 2020, vol. 382, issue C Abstract: In this work, we propose a non-parametric density estimation technique for measuring the risk in a credit portfolio, aiming at efficiently computing the marginal risk contributions. The novel method is based on wavelets, and we derive closed-form expressions to calculate the Value-at-Risk (VaR), the Expected Shortfall (ES) as well as the individual risk contributions to VaR (VaRC) and ES (ESC). We consider the multi-factor Gaussian and t-copula models for driving the defaults. The results obtained along the numerical experiments show the impressive accuracy and speed of this method when compared with crude Monte Carlo simulation. The presented methodology applies in the same manner regardless of the used model, and the computational performance is invariant under a considerable change in the dimension of the selected model. The speed-up with respect to the classical Monte Carlo approach ranges from twenty-five to one-thousand depending on the used model. Keywords: Credit risk; Value-at-risk; Expected shortfall; Portfolio risk contributions; Shannon wavelets; Non-parametric density estimation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:382:y:2020:i:c:s0096300320303155 DOI: 10.1016/j.amc.2020.125351 Access Statistics for this article Applied Mathematics and Computation is currently edited by Theodore Simos More articles in Applied Mathematics and Computation from Elsevier |
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