Likelihood Ratio Method and Algorithmic Differentiation: Fast Second Order Greeks

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

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. 15 years of Adjoint Algorithmic Differentiation (AAD) in finance;Quantitative Finance;2024-03-22

2. A Python Application Software to Option Pricing and the Estimation of Price Sensitivities as a Component of a Financial Information System;2022 7th International Conference on Mathematics and Computers in Sciences and Industry (MCSI);2022-08

3. Second-order Monte Carlo sensitivities in linear or constant time;The Journal of Computational Finance;2020

4. The second order price sensitivities for markets in a crisis;Journal of King Saud University - Science;2020-01

5. A Likely Gamma;SSRN Electronic Journal;2020

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