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Parameter Estimation and Practical Aspects of Modeling Stochastic Volatility

In: Handbook of Financial Time Series

Author

Listed:
  • Borus Jungbacker

    (VU University Amsterdam, Department of Econometrics)

  • Siem Jan Koopman

    (VU University Amsterdam, Department of Econometrics)

Abstract

Estimating parameters in a stochastic volatility (SV) model is a challenging task and therefore much research is devoted in this area of estimation. This chapter presents an overview and a practical guide of the quasi-likelihood and the Monte Carlo likelihood methods of estimation. The concepts of the methods are straightforward and the implementation is based on Kalman filter, smoothing, simulation smoothing, mode calculation and Monte Carlo simulation. These methods are general, transparent and computationally fast; therefore, they provide a feasible way for the estimation of parameters in SV models. Various extensions of the SV model are considered and some details are provided for the effective implementation of the Monte Carlo methods. Some empirical illustrations are given to show that the methods can be successful in measuring the unobserved volatility in financial time series.

Suggested Citation

  • Borus Jungbacker & Siem Jan Koopman, 2009. "Parameter Estimation and Practical Aspects of Modeling Stochastic Volatility," Springer Books, in: Thomas Mikosch & Jens-Peter Kreiß & Richard A. Davis & Torben Gustav Andersen (ed.), Handbook of Financial Time Series, chapter 13, pages 313-344, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-71297-8_13
    DOI: 10.1007/978-3-540-71297-8_13
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