Power transformation models and volatility forecasting
Version of Record online: 6 JUN 2008
Copyright © 2008 John Wiley & Sons, Ltd.
Journal of Forecasting
Volume 27, Issue 7, pages 587–606, November 2008
How to Cite
Sadorsky, P. and McKenzie, M. D. (2008), Power transformation models and volatility forecasting. J. Forecast., 27: 587–606. doi: 10.1002/for.1079
- Issue online: 15 OCT 2008
- Version of Record online: 6 JUN 2008
- power transformations;
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.