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Linear-representation Based Estimation of Stochastic Volatility Models

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  • CHRISTIAN FRANCQ
  • JEAN-MICHEL ZAKO�AN

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. Copyright 2006 Board of the Foundation of the Scandinavian Journal of Statistics..

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Bibliographic Info

Article provided by Danish Society for Theoretical Statistics & Finnish Statistical Society & Norwegian Statistical Association & Swedish Statistical Association in its journal Scandinavian Journal of Statistics.

Volume (Year): 33 (2006)
Issue (Month): 4 ()
Pages: 785-806

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Handle: RePEc:bla:scjsta:v:33:y:2006:i:4:p:785-806

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Cited by:
  1. Boubacar Mainassara, Y. & Carbon, M. & Francq, C., 2012. "Computing and estimating information matrices of weak ARMA models," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 345-361.
  2. Genaro Sucarrat & Alvaro Escribano, 2010. "The power log-GARCH model," Economics Working Papers we1013, Universidad Carlos III, Departamento de Economía.
  3. repec:imd:wpaper:wp2010-25 is not listed on IDEAS
  4. Sucarrat, Genaro & Grønneberg, Steffen & Escribano, Alvaro, 2013. "Estimation and Inference in Univariate and Multivariate Log-GARCH-X Models When the Conditional Density is Unknown," MPRA Paper 49344, University Library of Munich, Germany.

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