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Parameter estimation and practical aspects of modeling stochastic volatility. (English) Zbl 1178.62113

Andersen, Torben G. (ed.) et al., Handbook of financial time series. With a foreword by Robert Engle. Berlin: Springer (ISBN 978-3-540-71296-1/hbk; 978-3-540-71297-8/ebook). 313-344 (2009).
Summary: Estimating parameters in a stochastic volatility (SV) model is a challenging task and therefore much research is devoted to 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 filtering, 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.
For the entire collection see [Zbl 1162.91004].

MSC:

62P05 Applications of statistics to actuarial sciences and financial mathematics
62M20 Inference from stochastic processes and prediction
65C05 Monte Carlo methods
91G10 Portfolio theory

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