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Mixture distribution-based forecasting using stochastic volatility models

Clements, Adam E., Hurn, Stanley, & White, Scott I. (2006) Mixture distribution-based forecasting using stochastic volatility models. Applied Stochastic Models in Business and Industry, 22(5-6), 547 -557.

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Abstract

Many traditional econometric methods forecast the conditional distribution of asset returns by a point prediction of volatility. Alternatively, forecasts of this distribution may be generated from a mixture of distributions. This paper proposes a method by which information extracted from the estimation of a standard stochastic volatility model (using non-linear filtering) can be used to generate mixture distribution forecasts. In general, it is found that forecasts based on mixture distributions are superior to those simply using point predictions of volatility. In terms of mixture distribution forecasts, the method proposed in this paper is found to be superior to a number of competing approaches.

Impact and interest:

2 citations in Scopus
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1 citations in Web of Science®

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ID Code: 8570
Item Type: Journal Article
Additional Information: For more information, please refer to the journal's website (see hypertext link) or contact the author. Author contact details: a.clements@qut.edu.au
Keywords: forecasting, mixture distributions, non, linear filtering, stochastic volatility
DOI: 10.1002/asmb.647
ISSN: 1524-1904
Subjects: Australian and New Zealand Standard Research Classification > ECONOMICS (140000)
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Copyright Owner: Copyright 2006 John Wiley & Sons
Deposited On: 13 Jul 2007
Last Modified: 23 Sep 2013 16:33

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