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Testing for Longer Horizon Predictability of Return Volatility with an Application to the German DAX

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Author Info
Burkhard Raunig () (Oesterreichische Nationalbank, Economic Studies Division)
Abstract

Volatility of financial returns as a measure of risk is a key parameter in asset pricing and risk management and holding periods for financial instruments of several weeks or month are common. Nevertheless, little is known about the predictability of return volatility at longer horizons. This paper investigates the predictability of return volatility of the German DAX for forecasting horizons from one day to 45 days with a new model-free test procedure that avoids joint assessments of predictability and assumed volatility models. In Monte Carlo simulatiost is compared with two alternative model-free test procedures. The simulations indicate that the new test has good statistical properties and is more powerful then the other two tests if the distribution of returns is fat tailed. Contrary to earlier findings according to which the return volatility of the DAX is only predictable for 10 to 15 trading days, the empirical evidence provided in this study suggests that the volatility of DAX returns is predictable for horizons of up to 35 trading days and may be forecastable at even longer horizons.

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Paper provided by Oesterreichische Nationalbank (Austrian Central Bank) in its series Working Papers with number 86.

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Length: 40 pages
Date of creation: 22 Sep 2003
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Handle: RePEc:onb:oenbwp:86

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Postal: Oesterreichische Nationalbank, Economic Studies Division, c/o Beate Hofbauer-Berlakovich, POB 61, A-1011 Vienna, Austria
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Related research
Keywords: financial returns volatility predictability forecasting interval forecast evaluation density forecast evaluation

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Find related papers by JEL classification:
G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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    Other versions:
  2. Peter F. Christoffersen & Francis X. Diebold, 2000. "How Relevant is Volatility Forecasting for Financial Risk Management?," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 12-22, February. [Downloadable!] (restricted)
    Other versions:
  3. repec:fth:bfdipa:14/2001 is not listed on IDEAS
  4. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59. [Downloadable!] (restricted)
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  7. Gregory, Allan W, 1989. "A Nonparametric Test for Autoregressive Conditional Heteroscedasticity: A Markov-Chain Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(1), pages 107-15, January.
  8. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, 1997. "Evaluating Density Forecasts," NBER Technical Working Papers 0215, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  9. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
  10. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society. [Downloadable!]
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  13. van Dijk, Dick & Franses, Philip Hans & Lucas, Andre, 1999. "Testing for ARCH in the Presence of Additive Outliers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 539-62, Sept.-Oct. [Downloadable!]
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