Efficient XVA Management: Pricing, Hedging, and Attribution using Trade-Level Regression and Global Conditioning

TL;DR

This paper introduces a novel, efficient framework for XVA management that combines trade-level regression, analytic sensitivities, and global conditioning, enabling faster, more accurate pricing, hedging, and allocation in trading books.

Contribution

It presents a unified methodology that simplifies and accelerates XVA calculations, including sensitivities and trade-level allocations, suitable for parallel computing environments.

Findings

Significant reduction in computational complexity for XVA management.

Framework enables exact trade-level allocation of prices and sensitivities.

Compatible with GPU acceleration for high-performance implementation.

Abstract

Banks must manage their trading books, not just value them. Pricing includes valuation adjustments collectively known as XVA (at least credit, funding, capital and tax), so management must also include XVA. In trading book management we focus on pricing, hedging, and allocation of prices or hedging costs to desks on an individual trade basis. We show how to combine three technical elements to radically simplify XVA management, both in terms of the calculations, and the implementation of the calculations. The three technical elements are: trade-level regression; analytic computation of sensitivities; and global conditioning. All three are required to obtain the radical efficiency gains and implementation simplification. Moreover, many of the calculations are inherently parallel and suitable for GPU implementation. The resulting methodology for XVA management is sufficiently general that we can cover pricing, first- and second-order sensitivities, and exact trade-level allocation of pricing and sensitivities within the same framework. Managing incremental changes to portfolios exactly is also radically simplified.