On the calibration of stochastic volatility models: A comparison study

Jia Zhai*, Yi Cao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We studied the application of gradient based optimization methods for calibrating stochastic volatility models. In this study, the algorithmic differentiation is proposed as a novel approach for Greeks computation. The 'payoff function independent' feature of algorithmic differentiation offers a unique solution cross distinct models. To this end, we derived, analysed and compared Monte Carlo estimators for computing the gradient of a certain payoff function using four different methods: algorithmic differentiation, Pathwise delta, likelihood ratio and finite differencing. We assessed the accuracy and efficiency of the four methods and their impacts into the optimisation algorithm. Numerical results are presented and discussed.

Original languageEnglish
Title of host publication2014 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr Proceedings
EditorsRui Jorge Almeida, Dietmar Maringer, Vasile Palade, Antoaneta Serguieva
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages303-309
Number of pages7
ISBN (Electronic)9781479923809
DOIs
Publication statusPublished - 14 Oct 2014
Event2014 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2014 - London, United Kingdom
Duration: 27 Mar 201428 Mar 2014

Publication series

NameIEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr)

Conference

Conference2014 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2014
Country/TerritoryUnited Kingdom
CityLondon
Period27/03/1428/03/14

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