Model-free computation of risk contributions in credit portfolios

Highlights：

• 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, the Expected Shortfall as well as the individual risk contributions.

• 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.

摘要

•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, the Expected Shortfall as well as the individual risk contributions.•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.

论文关键词：Credit risk,Value-at-risk,Expected shortfall,Portfolio risk contributions,Shannon wavelets,Non-parametric density estimation

论文评审过程：Received 10 December 2018, Revised 13 April 2020, Accepted 3 May 2020, Available online 18 May 2020, Version of Record 18 May 2020.