# Gaussian Process Regression for Derivative Portfolio Modeling and Application to CVA Computations

## TL;DR

This paper introduces a multi-Gaussian process regression method for efficient and accurate valuation of OTC derivative portfolios in CVA calculations, avoiding nested simulations.

## Contribution

The paper develops a Gaussian process metamodel for derivative portfolios, enabling fast CVA computation without nested simulation or cash flow regression.

## Key findings

- High accuracy in CVA estimation demonstrated
- Convergence properties validated through numerical experiments
- Effective modeling of risk factors with Gaussian processes

## Abstract

Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all the trades with each counterparty under both market and credit risk. We present a multi-Gaussian process regression approach, which is well suited for OTC derivative portfolio valuation involved in CVA computation. Our approach avoids nested simulation or simulation and regression of cash flows by learning a Gaussian metamodel for the mark-to-market cube of a derivative portfolio. We model the joint posterior of the derivatives as a Gaussian process over function space, with the spatial covariance structure imposed on the risk factors. Monte-Carlo simulation is then used to simulate the dynamics of the risk factors. The uncertainty in portfolio valuation arising from the Gaussian process approximation is quantified numerically. Numerical experiments demonstrate the accuracy and convergence properties of our approach for CVA computations, including a counterparty portfolio of interest rate swaps.