Linear‐representation Based Estimation of Stochastic Volatility Models

Christian Francq and Jean‐michel Zakoïan
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Jean‐michel Zakoïan: CREST - Centre de Recherche en Economie et Statistique [Bruz] - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique, IP Paris - Institut Polytechnique de Paris

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Abstract: Abstract. A new way of estimating stochastic volatility models is developed. The method is based on the existence of autoregressive moving average (ARMA) representations for powers of the log‐squared observations. These representations allow to build a criterion obtained by weighting the sums of squared innovations corresponding to the different ARMA models. The estimator obtained by minimizing the criterion with respect to the parameters of interest is shown to be consistent and asymptotically normal. Monte‐Carlo experiments illustrate the finite sample properties of the estimator. The method has potential applications to other non‐linear time‐series models.

Date: 2006-01-30
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Published in Scandinavian Journal of Statistics, 2006, 33 (4), pp.785-806. ⟨10.1111/j.1467-9469.2006.00495.x⟩

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Journal Article: Linear‐representation Based Estimation of Stochastic Volatility Models (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05431364

DOI: 10.1111/j.1467-9469.2006.00495.x

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