Pricing and hedging of financial derivatives using a posteriori error estimates and adaptive methods for stochastic differential equations

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:235

Article

Nystrom, Kaj¹  Onskog, Thomas¹ 

[1] Umea Univ, Dept Math & Math Stat, SE-90187 Umea, Sweden

关键词: Sensitivity analysis;    Parabolic partial differential equations;    Stochastic differential equations;    Euler scheme;    A posteriori error estimate;    Adaptive algorithms;    Hedging;    Financial derivatives;   

DOI  :  10.1016/j.cam.2010.06.009

来源: Elsevier

【 摘 要 】

The efficient and accurate calculation of sensitivities of the price of financial derivatives with respect to perturbations of the parameters in the underlying model, the so-called 'Greeks', remains a great practical challenge in the derivative industry. This is true regardless of whether methods for partial differential equations or stochastic differential equations (Monte Carlo techniques) are being used. The computation of the 'Greeks' is essential to risk management and to the hedging of financial derivatives and typically requires substantially more computing time as compared to simply pricing the derivatives. Any numerical algorithm (Monte Carlo algorithm) for stochastic differential equations produces a time-discretization error and a statistical error in the process of pricing financial derivatives and calculating the associated 'Greeks'. In this article we show how a posteriori error estimates and adaptive methods for stochastic differential equations can be used to control both these errors in the context of pricing and hedging of financial derivatives. In particular, we derive expansions, with leading order terms which are computable in a posteriori form, of the time-discretization errors for the price and the associated 'Greeks'. These expansions allow the user to simultaneously first control the time-discretization errors in an adaptive fashion, when calculating the price, sensitivities and hedging parameters with respect to a large number of parameters, and then subsequently to ensure that the total errors are, with prescribed probability, within tolerance. (C) 2010 Elsevier B.V. All rights reserved.

【 授权许可】

Free