Parameter Estimation and Practical Aspects of Modeling Stochastic Volatility

Borus Jungbacker () and Siem Jan Koopman
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Borus Jungbacker: VU University Amsterdam, Department of Econometrics

Chapter 13 in Handbook of Financial Time Series, 2009, pp 313-344 from Springer

Abstract: 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.

Keywords: Stochastic Volatility; Stock Index; Observation Equation; Stochastic Volatility Model; Return Series (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-71297-8_13

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DOI: 10.1007/978-3-540-71297-8_13

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