Disentangling wrong-way risk: pricing credit valuation adjustment via change of measures

Brigo, Damiano; Vrins, Frédéric

doi:10.1016/j.ejor.2018.03.015

Cited by 26 publications

(26 citation statements)

References 21 publications

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“…To overcame this difficulty, several methods have been proposed: Monte Carlo methods, from brute force to enhanced ones (see [22] and [38]), the copula method or static approach (see [35], [13]), sharp bounding estimates (see [19]). Here, we propose a new method and we compare it with another recently investigated in [8].…”

Section: Introductionmentioning

confidence: 99%

“…In the next section we introduce the general problem and setting, in the third section we define our market model, while in the fourth we give the appropriate conditions for the convergence of the series and we show in detail how the method works in absence of interest rate risk, finally a stochastic interest rate is considered in the fifth section. It follows a short section recalling the main features and results of the method [8], based on a change of measure technique and in the last section we provide numerical comparisons among the different methodologies previously discussed.…”

Section: Introductionmentioning

confidence: 99%

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CVA and vulnerable options pricing by correlation expansions

2019

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We consider the problem of computing the Credit Value Adjustment (CVA) of a European option in presence of the Wrong Way Risk (WWR) in a default intensity setting. Namely we model the asset price evolution as solution to a linear equation that might depend on different stochastic factors and we provide an approximate evaluation of the option's price, by exploiting a correlation expansion approach, introduced in [2]. We compare the numerical performance of such a method with that recently proposed by Brigo et al. ([8], [10]) in the case of a call option driven by a GBM correlated with the CIR default intensity. We additionally report some numerical evaluations obtained by other methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CVA and vulnerable options pricing by correlation expansions

2019

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“…However, when the volatility is large, the performances deteriorate [9]. One can use reflected schemes to avoid negative samples along the path, however, the time step required to en-sure the convergence is too small.…”

Section: Monte Carlo Simulation For Stochastic Intensitymentioning

confidence: 99%

“…The process t ζ is a kind of model-tomarket survival rate change ratio [9]. For more discussion of t ζ , we refer to [9].…”

Section: Cva Ee With Wwrmentioning

confidence: 99%

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CVA under Bates Model with Stochastic Default Intensity

Feng¹

2017

JMF

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Counterparty credit risk has received increasing attention and become a topical issue since 2007 credit crisis, particularly for its impact on the valuation of the OTC derivatives. Credit Value Adjustment (CVA) has become an import field and it is required in Basel III. This paper studies CVA for European options under Bates model with stochastic default intensity. We develop a Monte Carlo and finite difference method framework for assessing exposure profiles and impact of counterparty credit risk in pricing. The exposures are computed by solving a partial integro-differential Equation (PIDE) using implicit-explicit (IMEX) time discretization schemes. CVA in presence of wrong way risk (WWR) is embedded in the correlation between risk factor and default intensity. Meanwhile, the jump-at-default feature of the models offers an effective means to assess WWR. Our results show that both jump and WWR play an important role in evaluating CVA and exposures. The impact is significant and it is crucial for risk management purpose.

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A moment matching method for option pricing under stochastic interest rates

Antonelli

Ramponi

Scarlatti

2021

Appl Stoch Models Bus & Ind

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Summary In this paper, we present a new and straightforward approximation methodology for pricing a call option in a Black and Scholes market, characterized by stochastic interest rates. The method relies on a Gaussian moment matching technique applied to a conditional Black and Scholes formula, used to disentangle the distributional complexity of the underlying price process. The problem then reduces to exploiting the Gaussian density and the expression of the bond price induced by the interest rate. To check its accuracy and computational time, we implement it for a CIR interest rate model correlated with the underlying, using Monte Carlo simulations as a benchmark. The method performance turns out to be quite remarkable, even when compared with similar results obtained by the affine approximation technique presented in Grzelak and Oosterlee, and by the expansion formula introduced in Kim and Kunimoto. In the last section, we apply the method also to the pricing of Forward‐Starting options, to the evaluation of the credit spreads in the Merton structural approach to credit risk, and we outline a possible application to a stochastic volatility model.

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Disentangling wrong-way risk: pricing credit valuation adjustment via change of measures

Cited by 26 publications

References 21 publications

CVA and vulnerable options pricing by correlation expansions

CVA and vulnerable options pricing by correlation expansions

CVA under Bates Model with Stochastic Default Intensity

A moment matching method for option pricing under stochastic interest rates

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