Abstract
AbstractThis paper considers the forecast accuracy of a wide range of volatility models, with particular emphasis on the use of power transformations. Where oneโperiodโahead 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 valueโatโriskโbased 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 ยฉ 2008 John Wiley & Sons, Ltd.
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