Credit Valuation Adjustment with Replacement Closeout: Theory and Algorithms

TL;DR

This paper develops a theoretical framework and neural network algorithms to accurately and efficiently value credit risk under the replacement closeout convention, addressing nonlinearity and high dimensionality issues.

Contribution

It proves the unique solvability of the nonlinear valuation system and introduces a neural network-based method to solve high-dimensional problems efficiently.

Findings

Theoretical proof of unique solution existence.

Neural network algorithm effectively solves high-dimensional valuation.

Numerical results show improved accuracy and efficiency.

Abstract

The replacement closeout convention has drawn more and more attention since the 2008 financial crisis. Compared with the conventional risk-free closeout, the replacement closeout convention incorporates the creditworthiness of the counterparty and thus providing a more accurate estimate of the Mark-to-market value of a financial claim. In contrast to the risk-free closeout, the replacement closeout renders a nonlinear valuation system, which constitutes the major difficulty in the valuation of the counterparty credit risk. In this paper, we show how to address the nonlinearity attributed to the replacement closeout in the theoretical and computational analysis. In the theoretical part, we prove the unique solvability of the nonlinear valuation system and study the impact of the replacement closeout on the credit valuation adjustment. In the computational part, we propose a neural network-based algorithm for solving the (high dimensional) nonlinear valuation system and effectively alleviating the curse of dimensionality. We numerically compare the computational cost for the valuations with risk-free and replacement closeouts. The numerical tests confirm both the accuracy and the computational efficiency of our proposed algorithm for the valuation of the replacement closeout.