Quasi-Maximum Likelihood estimation of Stochastic Volatility models
AbstractChanges in variance or volatility over time can be modelled using stochastic volatility (SV) models. This approach is based on treating the volatility as an unobserved vatiable, the logarithm of which is modelled as a linear stochastic process, usually an autoregression. This article analyses the asymptotic and finite sample properties of a Quasi-Maximum Likelihood (QML) estimator based on the Kalman filter. The relative efficiency of the QML estimator when compared with estimators based on the Generalized Method of Moments is shown to be quite high for parameter values often found in empirical applications. The QML estimator can still be employed when the SV model is generalized to allow for distributions with heavier tails than the normal. SV models are finally fitted to daily observations on the yen/dollar exchange rate.
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Bibliographic InfoPaper provided by Universidad Carlos III de Madrid in its series Open Access publications from Universidad Carlos III de Madrid with number info:hdl:10016/4786.
Length: 308 p.
Date of creation: 1994
Date of revision:
Publication status: Published in Journal of Econometrics (1994) v. 63, p.289-306
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Web page: http://www.uc3m.es
Exchange rates; Generalized method of moments; Kalman filter; Quasi- maximum likelihood; Stochastic volatility;
Other versions of this item:
- Ruiz, Esther, 1994. "Quasi-maximum likelihood estimation of stochastic volatility models," Journal of Econometrics, Elsevier, vol. 63(1), pages 289-306, July.
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