Simulated maximum likelihood estimation of continuous time stochastic volatility models

Tore Selland Kleppe, Jun Yu and H.J. Skaug

A chapter in Maximum Simulated Likelihood Methods and Applications, 2010, pp 137-161 from Emerald Group Publishing Limited

Abstract: In this chapter we develop and implement a method for maximum simulated likelihood estimation of the continuous time stochastic volatility model with the constant elasticity of volatility. The approach does not require observations on option prices, nor volatility. To integrate out latent volatility from the joint density of return and volatility, a modified efficient importance sampling technique is used after the continuous time model is approximated using the Euler–Maruyama scheme. The Monte Carlo studies show that the method works well and the empirical applications illustrate usefulness of the method. Empirical results provide strong evidence against the Heston model.

Date: 2010
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Related works:
Working Paper: Stimulated Maximum Likelihood Estimation of Continuous Time Stochastic Volatility Models (2009)
Working Paper: Simulated Maximum Likelihood Estimation of Continuous Time Stochastic Volatility Models (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-9053(2010)0000026009

DOI: 10.1108/S0731-9053(2010)0000026009

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