We consider the problem of computing the Value Adjustment of European contingent claims when the possibility of default of either party is considered, possibly including also funding and collateralization requirements. As shown in Brigo et al. (Brigo, Liu, Pallavicini, and Sloth (2016b), Brigo, Francischello, and Pallavicini (2016a)), this leads to a more articulate variety of Value Adjustments (XVA) that introduce some non-linear features. When exploiting a reduced-form approach for the default times, the adjusted price can be characterized as the solution to a possibly nonlinear Backward Stochastic Differential Equation (BSDE). The expectation representing the solution of the BSDE is usually quite hard to compute even in a Marko-vian setting, and one might resort either to the discretization of the Partial Differential Equation char-acterizing it or to Monte Carlo Simulations. Both choices are computationally quite expensive, In this paper, we suggest an alternative method based on an appropriate change of numeraire and a Taylor poly-nomial expansion when intensities are represented by affine processes correlated with the asset price. The numerical discussion at the end of this work shows that, at least in the case of the Cox-Ingersoll-Ross (CIR) intensity model, even the simple first-order approximation has a remarkable computational efficiency. (c) 2021 Elsevier B.V. All rights reserved.
Approximate value adjustments for European claims
Antonelli, F
;Ramponi, A;
2022-01-01
Abstract
We consider the problem of computing the Value Adjustment of European contingent claims when the possibility of default of either party is considered, possibly including also funding and collateralization requirements. As shown in Brigo et al. (Brigo, Liu, Pallavicini, and Sloth (2016b), Brigo, Francischello, and Pallavicini (2016a)), this leads to a more articulate variety of Value Adjustments (XVA) that introduce some non-linear features. When exploiting a reduced-form approach for the default times, the adjusted price can be characterized as the solution to a possibly nonlinear Backward Stochastic Differential Equation (BSDE). The expectation representing the solution of the BSDE is usually quite hard to compute even in a Marko-vian setting, and one might resort either to the discretization of the Partial Differential Equation char-acterizing it or to Monte Carlo Simulations. Both choices are computationally quite expensive, In this paper, we suggest an alternative method based on an appropriate change of numeraire and a Taylor poly-nomial expansion when intensities are represented by affine processes correlated with the asset price. The numerical discussion at the end of this work shows that, at least in the case of the Cox-Ingersoll-Ross (CIR) intensity model, even the simple first-order approximation has a remarkable computational efficiency. (c) 2021 Elsevier B.V. All rights reserved.Pubblicazioni consigliate
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