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On the calibration of stochastic volatility models: A comparison study

Zhai, Jia and Cao, Yi (2014) On the calibration of stochastic volatility models: A comparison study In: 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), 27-28 Mar 2014, London, UK.

On the Calibration of Stochastic Volatility Models A Comparison Study.pdf - Version of Record

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
Zhai, Jia
Date : 16 October 2014
DOI : 10.1109/CIFEr.2014.6924088
Copyright Disclaimer : Copyright © 2014 by the Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
Uncontrolled Keywords : Mathematical model; Stochastic processes; Sensitivity; Biological system modelling; Calibration; Europe; Monte Carlo methods
Depositing User : Clive Harris
Date Deposited : 12 Sep 2017 12:38
Last Modified : 12 Sep 2017 12:38

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