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Power transformation models and volatility forecasting

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  • 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)

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    Abstract

    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
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    Bibliographic Info

    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

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    Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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    Cited by:
    1. Proietti, Tommaso & Lütkepohl, Helmut, 2013. "Does the Box–Cox transformation help in forecasting macroeconomic time series?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 88-99.
    2. Benoît Sévi, 2014. "Forecasting the volatility of crude oil futures using intraday data," Working Papers 2014-053, Department of Research, Ipag Business School.
    3. Jui-Cheng Hung & Ren-Xi Ni & Matthew C. Chang, 2009. "The Information Contents of VIX Index and Range-based Volatility on Volatility Forecasting Performance of S&P 500," Economics Bulletin, AccessEcon, vol. 29(4), pages 2592-2604.

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