Likelihood Ratio Method and Algorithmic Differentiation: Fast Second Order Greeks-Reference-Cited by-同舟云学术

Likelihood Ratio Method and Algorithmic Differentiation: Fast Second Order Greeks

Published:2015-04-13 Issue:1-2 Volume:4 Page:81-87
ISSN:2157-6203
Container-title:Algorithmic Finance
language:
Short-container-title:AF

Author:

Capriotti Luca¹

Affiliation:

1. Quantitative Strategies, Investment Banking Division, Credit Suisse Group, One Cabot Square, London E14 4QJ, United Kingdom

Abstract

We show how Adjoint Algorithmic Differentiation can be combined with the so-called Pathwise Derivative and Likelihood Ratio Method to construct efficient Monte Carlo estimators of second order price sensitivities of derivative portfolios. We demonstrate with a numerical example how the proposed technique can be straightforwardly implemented to greatly reduce the computation time of second order risk.

Publisher

IOS Press

Subject

Computational Mathematics,Computer Science Applications,Computer Vision and Pattern Recognition,Finance

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