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Conditionally Heteroskedastic Factor Models: Identification and Instrumental Variables Estimation

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  • Catherine Doz
  • Eric Renault

Abstract

This paper provides a semiparametric framework for modelling multivariate conditional heteroskedasticity. First, we show that stochastic volatility factor models with possibly cross-correlated disturbances cannot be identified from returns conditional variance structure only, except when strong restrictions on the support of the probability distribution of latent factors volatility are maintained. Second, we provide an alternative way to maintain identifying restrictions through either higher order moments or through a specification of risk premiums based on constant prices of factor risks. In both cases, identification is obtained with conditional moment restrictions which pave the way for instrumental variables estimation and inference. A preliminary step of determination of the number of factors and identification of mimicking portfolios is proposed through a sequence of GMM overidentification tests which encompass Engle and Kozicki (1993) tests for common features. Cet article propose un cadre semi-paramétrique adapté à la modélisation de l'hétéroscédasticité conditionnelle multivariée. Nous montrons d'abord qu'un modèle factoriel à volatilité stochastique ne peut pas être identifié seulement à partir de la structure de variance conditionnelle des rendements, sauf si l'on impose des restrictions importantes au support de la loi de probabilité des facteurs latents. Nous proposons ensuite des restrictions alternatives permettant d'identifier le modèle de volatilité multivariée. Ces restrictions portent soit sur les moments d'ordre supérieur, soit sur une spécification de la prime de risque fondée sur un prix constant du risque des facteurs. Dans les deux cas, l'identification du modèle est obtenue à partir de restrictions sur les moments conditionnels, ce qui permet l'estimation par variables instrumentales. Une étape préliminaire de détermination du nombre de facteurs et d'identification de portefeuilles représentatifs est proposée. Elle est fondée sur une séquen
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  • Catherine Doz & Eric Renault, 2004. "Conditionally Heteroskedastic Factor Models: Identification and Instrumental Variables Estimation," CIRANO Working Papers 2004s-37, CIRANO.
    • Handle: RePEc:cir:cirwor:2004s-37
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    Cited by:

    1. Prosper Dovonon & Alastair R. Hall, 2017. "The Asymptotic Properties of GMM and Indirect Inference Under Second-Order Identification," Economics Discussion Paper Series 1705, Economics, The University of Manchester.
    2. Dovonon, Prosper & Hall, Alastair R., 2018. "The asymptotic properties of GMM and indirect inference under second-order identification," Journal of Econometrics, Elsevier, vol. 205(1), pages 76-111.
    3. Prosper Dovonon & Alastair Hall, 2018. "The Asymptotic Properties of GMM and Indirect Inference under Second-order Identi?cation," CIRANO Working Papers 2018s-37, CIRANO.
    4. Anderson, Heather M. & Vahid, Farshid, 2007. "Forecasting the Volatility of Australian Stock Returns: Do Common Factors Help?," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 76-90, January.
    5. García-Ferrer, Antonio & González-Prieto, Ester & Peña, Daniel, 2012. "A conditionally heteroskedastic independent factor model with an application to financial stock returns," International Journal of Forecasting, Elsevier, vol. 28(1), pages 70-93.
    6. Weber, Enzo, 2013. "Decomposing U.S. Stock Market Comovement into spillovers and common factors," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 106-118.

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