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State‐space stochastic volatility models: A review of estimation algorithms

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  • Enrico Capobianco

Abstract

Stochastic volatility models (SVMs) represent an important framework for the analysis of financial time series data, together with ARCH‐type models; but unlike the latter, the former, at least from the statistical point of view, cannot rely on the possibility of obtaining exact inference, in particular with regard to maximum likelihood estimates for the parameters of interest. For SVMs, usually only approximate results can be obtained, unless particularly sophisticated estimation strategies like exact non‐gaussian filtering methods or simulation techniques are employed. In this paper we review SVM and present a new characterization for them, called ‘generalized bilinear stochastic volatility’. © 1996 John Wiley & Sons, Ltd.

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  • Enrico Capobianco, 1996. "State‐space stochastic volatility models: A review of estimation algorithms," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 12(4), pages 265-279, December.
    • Handle: RePEc:wly:apsmda:v:12:y:1996:i:4:p:265-279
    DOI: 10.1002/(SICI)1099-0747(199612)12:43.0.CO;2-N
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    Cited by:

    1. Carmen Broto & Esther Ruiz, 2004. "Estimation methods for stochastic volatility models: a survey," Journal of Economic Surveys, Wiley Blackwell, vol. 18(5), pages 613-649, December.

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