Efficient exposure computation by risk factor decomposition

TL;DR

This paper introduces a novel PDE-based method using risk factor decomposition to efficiently compute counterparty credit risk exposure, significantly reducing computational effort and variance in high-dimensional models.

Contribution

It presents a new approach combining PDEs with anchored-ANOVA decompositions for high-dimensional risk calculations, enabling faster and more accurate exposure estimation.

Findings

Achieves significant computational speed-up over traditional methods.

Provides effective variance reduction using control variates.

Demonstrates high accuracy on realistic financial portfolios.

Abstract

The focus of this paper is the efficient computation of counterparty credit risk exposure on portfolio level. Here, the large number of risk factors rules out traditional PDE-based techniques and allows only a relatively small number of paths for nested Monte Carlo simulations, resulting in large variances of estimators in practice. We propose a novel approach based on Kolmogorov forward and backward PDEs, where we counter the high dimensionality by a generalisation of anchored-ANOVA decompositions. By computing only the most significant terms in the decomposition, the dimensionality is reduced effectively, such that a significant computational speed-up arises from the high accuracy of PDE schemes in low dimensions compared to Monte Carlo estimation. Moreover, we show how this truncated decomposition can be used as control variate for the full high-dimensional model, such that any approximation errors can be corrected while a substantial variance reduction is achieved compared to the standard simulation approach. We investigate the accuracy for a realistic portfolio of exchange options, interest rate and cross-currency swaps under a fully calibrated ten-factor model.