Toward a coherent Monte Carlo simulation of CVA

Abbas-Turki Lokman A. (), Bouselmi Aych I. () and Mohammed Mikou
Additional contact information
Abbas-Turki Lokman A.: Institut für Mathematik, TU Berlin, Building MA, Straße des 17. Juni 136, 10623 Berlin, Germany
Bouselmi Aych I.: Laboratoire d' Analyse et de Mathématiques Appliquées, 5, Boulevard Descartes, 77454 Marne-la-Vallée Cedex 2, France

Monte Carlo Methods and Applications, 2014, vol. 20, issue 3, 195-216

Abstract: This paper is devoted to the simulation of the Credit Valuation Adjustment (CVA) using a pure Monte Carlo technique with Malliavin calculus (MCM). The procedure presented is based on a general theoretical framework that includes a large number of models as well as various contracts, and allows both the computation of CVA and its sensitivity with respect to the different assets. Moreover, we provide the expression of the backward conditional density of assets vector that can be simulated off-line in order to reduce the variance of the CVA estimator. Using the suitability of MCM to parallel architectures and thus to a Graphic Processing Unit (GPU) implementation, we show that the results obtained are accurate once a sufficient number of trajectories is simulated. Both complexity and accuracy are studied for MCM and regression methods and are compared to the square Monte Carlo benchmark.

Keywords: CVA; CVA sensitivity; Malliavin calculus (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/mcma-2013-0026 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:mcmeap:v:20:y:2014:i:3:p:195-216:n:3

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/mcma/html

DOI: 10.1515/mcma-2013-0026

Access Statistics for this article

Monte Carlo Methods and Applications is currently edited by Karl K. Sabelfeld

More articles in Monte Carlo Methods and Applications from De Gruyter
Bibliographic data for series maintained by Peter Golla ().