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Optimal Predictions of Powers of Conditionally Heteroskedastic Processes

Listed author(s):
  • Christan Francq


    (Crest and University Lille 3)

  • Jean-Michel Zakoian


    (Crest and University Lille 3)

In conditionally heteroskedastic 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 a first step, for instance by Gaussian quasi-maximum likelihood (QML) or by least-absolute deviations, and to use empirical means based on rescaled innovations to estimate the expectation in a second step. This paper proposes an alternative one-step procedure, based on an appropriate non-Gaussian QML 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 indexes of major stock exchanges is given

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Paper provided by Centre de Recherche en Economie et Statistique in its series Working Papers with number 2012-17.

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Length: 49
Date of creation: Aug 2012
Handle: RePEc:crs:wpaper:2012-17
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