Abstract

In this paper, we propose a neural network-based method for CVA computations of a portfolio of derivatives. In particular, we focus on portfolios consisting of a combination of derivatives, with and without true optionality, e.g., a portfolio of a mix of European- and Bermudan-type derivatives. CVA is computed, with and without netting, for different levels of WWR and for different levels of credit quality of the counterparty. We show that the CVA is overestimated with up to 25% by using the standard procedure of not adjusting the exercise strategy for the default-risk of the counterparty. For the Expected Shortfall of the CVA dynamics, the overestimation was found to be more than 100% in some non-extreme cases.

Highlights

  • In this paper, we consider a set of financial contracts, which we refer to as the portfolio of derivatives, or just the portfolio, written between two parties

  • This paper provides a method for computations of portfolio Credit Valuation Adjustment (CVA) without having to value each individual derivative

  • We further investigate the impact of netting and Wrong Way Risk (WWR) in the presence of derivatives with early exercise features and true optionality

Read more

Summary

Introduction

We consider a set of financial contracts, which we refer to as the portfolio of derivatives, or just the portfolio, written between two parties. The first party is referred to as the bank and is considered to be default-free. The second party, which may default, is referred to as the counterparty. We take the perspective of the default-free bank in order to investigate some of the risks associated with a defaultable counterparty. It is straightforward to extend the methodologies used in this paper to a defaultable bank as well as to multiple counterparties

Objectives
Methods
Findings
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call