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Simulated maximum likelihood estimation of continuous time stochastic volatility models

In: Maximum Simulated Likelihood Methods and Applications

Author

Listed:
  • Tore Selland Kleppe
  • Jun Yu
  • H.J. Skaug

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.

Suggested Citation

  • Tore Selland Kleppe & Jun Yu & H.J. Skaug, 2010. "Simulated maximum likelihood estimation of continuous time stochastic volatility models," Advances in Econometrics, in: Maximum Simulated Likelihood Methods and Applications, pages 137-161, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-9053(2010)0000026009
    DOI: 10.1108/S0731-9053(2010)0000026009
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    Cited by:

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    2. Niu Wei-Fang, 2013. "Maximum likelihood estimation of continuous time stochastic volatility models with partially observed GARCH," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 421-438, September.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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