Power transformation models and volatility forecasting
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.
Year of publication: |
2008
|
---|---|
Authors: | Sadorsky, Perry ; McKenzie, Michael D. |
Published in: |
Journal of Forecasting. - John Wiley & Sons, Ltd.. - Vol. 27.2008, 7, p. 587-606
|
Publisher: |
John Wiley & Sons, Ltd. |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
Power transformation models and volatility forecasting
Sadorsky, Perry A., (2008)
-
Power transformation models and volatility forecasting
Sadorsky, Perry, (2008)
-
Short-selling and credit default swap spreads-Where do informed traders trade?
Lecce, Steven, (2018)
- More ...