Power transformation models and volatility forecasting

Perry Sadorsky, Michael D. McKenzie

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)587-606
Number of pages20
JournalJournal of Forecasting
Issue number7
Publication statusPublished - Nov 2008


  • Forecasting
  • Power transformations
  • Volatility


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