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Power transformation models and volatility forecasting

✍ Scribed by Perry Sadorsky; Michael D. McKenzie

Publisher
John Wiley and Sons
Year
2008
Tongue
English
Weight
156 KB
Volume
27
Category
Article
ISSN
0277-6693

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✦ Synopsis

Abstract

This paper considers the forecast accuracy of a wide range of volatility models, with particular emphasis on the use of power transformations. Where one‐period‐ahead forecasts are considered, the power autoregressive models are ranked first by a range of error metrics. Over longer forecast horizons, however, generalized autoregressive conditional heteroscedasticity models are preferred. A value‐at‐risk‐based forecast assessment indicates that, while the forecast errors are independent, they are not independent and identically distributed, although this latter result is sensitive to the choice of forecast horizon. Our results are robust across a number of different asset markets. Copyright © 2008 John Wiley & Sons, Ltd.

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