Optimal Predictions of Powers of Conditionally Heteroskedastic Processes

Christian Francq and Jean-Michel Zakoian

No 2012-17, Working Papers from Center for Research in Economics and Statistics

Abstract: 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

Keywords: Efficiency of estimators; GARCH; Least-absolute deviations estimation; Prediction; Quasi maximum likelihood estimation (search for similar items in EconPapers)
Pages: 49
Date: 2012-08
New Economics Papers: this item is included in nep-ets, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://crest.science/RePEc/wpstorage/2012-17.pdf Crest working paper version (application/pdf)

Related works:
Journal Article: Optimal predictions of powers of conditionally heteroscedastic processes (2013)
Working Paper: Optimal predictions of powers of conditionally heteroskedastic processes (2010)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:crs:wpaper:2012-17

Access Statistics for this paper

More papers in Working Papers from Center for Research in Economics and Statistics Contact information at EDIRC.
Bibliographic data for series maintained by Secretariat General () and Murielle Jules Maintainer-Email : murielle.jules@ensae.Fr.