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Power transformation models and volatility forecasting

Listed author(s):
  • Perry Sadorsky

    (Schulich School of Business, York University, Toronto, Ontario, Canada, M3J 1P3)

  • Michael D. McKenzie

    (Centre for Financial Analysis and Policy, University of Cambridge, UK, and Department of Economics, Finance and Marketing, RMIT University, Melbourne, Victoria, Australia)

Registered author(s):

    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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    File URL: http://hdl.handle.net/10.1002/for.1079
    File Function: Link to full text; subscription required
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    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

    Volume (Year): 27 (2008)
    Issue (Month): 7 ()
    Pages: 587-606

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    Handle: RePEc:jof:jforec:v:27:y:2008:i:7:p:587-606
    DOI: 10.1002/for.1079
    Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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