Optimal Predictions of Powers of Conditionally Heteroscedastic Processes

Christian Francq () and Jean-Michel Zakoïan
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Christian Francq: CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - GENES - Groupe des Écoles Nationales d'Économie et Statistique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - GENES - Groupe des Écoles Nationales d'Économie et Statistique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique, IP Paris - Institut Polytechnique de Paris
Jean-Michel Zakoïan: LFA - Laboratoire de Finance Assurance - Centre de Recherche en Économie et Statistique (CREST) - GENES - Groupe des Écoles Nationales d'Économie et Statistique, EQUIPPE - Economie Quantitative, Intégration, Politiques Publiques et Econométrie - Université de Lille, Sciences et Technologies - Université de Lille, Sciences Humaines et Sociales - PRES Université Lille Nord de France - Université de Lille, Droit et Santé

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Abstract: Summary In conditionally heteroscedastic models, the optimal prediction of powers, or logarithms, of the absolute value has a simple expression in terms of the volatility and an expectation involving the independent process. A natural procedure for estimating this prediction is to estimate the volatility in the first step, for instance by Gaussian quasi-maximum-likelihood or by least absolute deviations, and to use empirical means based on rescaled innovations to estimate the expectation in the second step. The paper proposes an alternative one-step procedure, based on an appropriate non-Gaussian quasi-maximum-likelihood estimator, and establishes the asymptotic properties of the two approaches. Asymptotic comparisons and numerical experiments show that the differences in accuracy can be important, depending on the prediction problem and the innovations distribution. An application to indices of major stock exchanges is given.

Date: 2013-03-01
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Published in Journal of the Royal Statistical Society: Series B, 2013, 75 (2), pp.345-367. ⟨10.1111/j.1467-9868.2012.01045.x⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05417520

DOI: 10.1111/j.1467-9868.2012.01045.x

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