University of Surrey

Test tubes in the lab Research in the ATI Dance Research

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.

[img]
Preview
Text
On the Calibration of Stochastic Volatility Models A Comparison Study.pdf - Version of Record

Download (395kB) | Preview

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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
NameEmailORCID
Zhai, JiaUNSPECIFIEDUNSPECIFIED
Cao, Yiyc0006@surrey.ac.ukUNSPECIFIED
Date : 16 October 2014
Identification Number : 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
URI: http://epubs.surrey.ac.uk/id/eprint/842246

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800