Estimating the parameters of stochastic volatility models using option price data

Hurn, A. Stan, Lindsay, Kenneth A., & McClelland, Andrew J. (2015) Estimating the parameters of stochastic volatility models using option price data. Journal of Business and Economic Statistics, 33(4), pp. 579-594.

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Abstract

This article describes a maximum likelihood method for estimating the parameters of the standard square-root stochastic volatility model and a variant of the model that includes jumps in equity prices. The model is fitted to data on the S&P 500 Index and the prices of vanilla options written on the index, for the period 1990 to 2011. The method is able to estimate both the parameters of the physical measure (associated with the index) and the parameters of the risk-neutral measure (associated with the options), including the volatility and jump risk premia. The estimation is implemented using a particle filter whose efficacy is demonstrated under simulation. The computational load of this estimation method, which previously has been prohibitive, is managed by the effective use of parallel computing using graphics processing units (GPUs). The empirical results indicate that the parameters of the models are reliably estimated and consistent with values reported in previous work. In particular, both the volatility risk premium and the jump risk premium are found to be significant.

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ID Code: 89591
Item Type: Journal Article
Refereed: Yes
Keywords: stochastic volatility, jumps, risk premia, maximum likelihood, particle filter
DOI: 10.1080/07350015.2014.981634
ISSN: 0735-0015
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Current > Schools > School of Economics & Finance
Funding:
Copyright Owner: Copyright 2015 American Statistical Association
Copyright Statement: The Version of Record of this manuscript has been published and is available in Journal of Business and Economic Statistics, 27 October 2015, http://www.tandfonline.com/10.1080/07350015.2014.981634
Deposited On: 28 Oct 2015 02:39
Last Modified: 07 Sep 2016 00:56

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