Variance-covariance based risk allocation in credit portfolios: analytical approximation

TL;DR

This paper introduces a high-precision analytical method for risk allocation in credit portfolios, outperforming Monte Carlo simulations in accuracy and speed within a multi-factor Merton-type model with stochastic recovery.

Contribution

It presents a novel analytical approximation technique for variance-covariance risk allocation in credit portfolios, applicable to complex multi-factor models with stochastic recovery.

Findings

Analytical approximation achieves higher accuracy than Monte Carlo methods.

The method significantly reduces computational time.

It is effective in multi-factor Merton-type models with stochastic recovery.

Abstract

High precision analytical approximation is proposed for variance-covariance based risk allocation in a portfolio of risky assets. A general case of a single-period multi-factor Merton-type model with stochastic recovery is considered. The accuracy of the approximation as well as its speed are compared to and shown to be superior to those of Monte Carlo simulation.