Distributionally Robust XVA via Wasserstein Distance: Wrong Way Counterparty Credit and Funding Risk

This paper investigates calculations of robust X-Value adjustment (XVA), in particular, credit valuation adjustment (CVA) and funding valuation adjustment (FVA), for over-the-counter derivatives under distributional ambiguity using Wasserstein distance as the ambiguity measure. Wrong way counterparty credit risk and funding risk can be characterized (and indeed quantified) via the robust XVA formulations. The simpler dual formulations are derived using recent Lagrangian duality results. Next, some computational experiments are conducted to measure the additional XVA charges due to distributional ambiguity under a variety of portfolio and market configurations. Finally some suggestions for further work are discussed.

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RANDOM TIME FORWARD-STARTING OPTIONS

International Journal of Theoretical and Applied Finance ◽

10.1142/s0219024916500503 ◽

2016 ◽

Vol 19 (08) ◽

pp. 1650050 ◽

Cited By ~ 1

Author(s):

F. ANTONELLI ◽

A. RAMPONI ◽

S. SCARLATTI

Keyword(s):

Credit Risk ◽

Natural Generalization ◽

Random Time ◽

Over The Counter ◽

Market Models ◽

Ex Ante ◽

Counterparty Credit Risk

We introduce a natural generalization of the forward-starting options. The main feature of the contract presented here is that the strike-determination time is not fixed ex-ante, but allowed to be random, usually related to the occurrence of some event, either of financial nature or not. We will call these options random time forward-starting (RTFS). We show that, under an appropriate “martingale preserving” hypothesis, we can exhibit arbitrage free prices, which can be explicitly computed in many classical market models, at least under independence and in absence of simultaneous jumps between the random time and the assets' prices. Practical implementations of the pricing methodologies are also provided. Finally, a credit value adjustment (CVA) formula for these over the counter (OTC) options is computed for the unilateral counterparty credit risk.

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Calculation of Credit Valuation Adjustment Based on Least Square Monte Carlo Methods

Mathematical Problems in Engineering ◽

10.1155/2015/959312 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Qian Liu

Keyword(s):

Monte Carlo ◽

Credit Risk ◽

Financial Markets ◽

Financial Institutions ◽

Least Square ◽

Copula Functions ◽

Default Intensity ◽

Interest Rate Swaps ◽

Counterparty Credit Risk ◽

Credit Valuation Adjustment

Counterparty credit risk has become one of the highest-profile risks facing participants in the financial markets. Despite this, relatively little is known about how counterparty credit risk is actually priced mathematically. We examine this issue using interest rate swaps. This largely traded financial product allows us to well identify the risk profiles of both institutions and their counterparties. Concretely, Hull-White model for rate and mean-reverting model for default intensity have proven to be in correspondence with the reality and to be well suited for financial institutions. Besides, we find that least square Monte Carlo method is quite efficient in the calculation of credit valuation adjustment (CVA, for short) as it avoids the redundant step to generate inner scenarios. As a result, it accelerates the convergence speed of the CVA estimators. In the second part, we propose a new method to calculate bilateral CVA to avoid double counting in the existing bibliographies, where several copula functions are adopted to describe the dependence of two first to default times.

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