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Direction-of-Change Forecasts Based on Conditional Variance, Skewness and Kurtosis Dynamics : International Evidence

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Author Info

  • Anthony S. Tay

    (SMU)

  • Peter F. Christoffersen
  • Francis X. Diebold
  • Roberto S. Mariano
  • Yiu Kuen Tse

Abstract

Recent theoretical work has revealed a direct connection between asset return volatility forecastability and asset return sign forecastability. This suggests that the pervasive volatility forecastability in equity returns could, via induced sign forecastability, be used to produce direction-of change forecasts useful for market timing. We attempt to do so in an international sample of developed equity markets, with some success, as assessed by formal probability forecast scoring rules such as the Brier score. An important ingredient is our conditioning not only on conditional mean and variance information, but also conditional skewness and kurtosis information, when forming direction-of-change forecasts.

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Bibliographic Info

Paper provided by East Asian Bureau of Economic Research in its series Finance Working Papers with number 22481.

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Date of creation: Jan 2006
Date of revision:
Handle: RePEc:eab:financ:22481

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Postal: JG Crawford Building #13, Asia Pacific School of Economics and Government, Australian National University, ACT 0200
Web page: http://www.eaber.org
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Related research

Keywords: Volatility; variance; skewness; kurtosis; market timing; asset management; asset allocation; portfolio management;

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References

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  1. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
  2. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).
  3. Peter F. Christoffersen & Francis X. Diebold, 2006. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," Management Science, INFORMS, vol. 52(8), pages 1273-1287, August.
  4. Francis X. Diebold & Jose A. Lopez, 1996. "Forecast Evaluation and Combination," NBER Technical Working Papers 0192, National Bureau of Economic Research, Inc.
  5. Brandt, Michael W. & Kang, Qiang, 2004. "On the relationship between the conditional mean and volatility of stock returns: A latent VAR approach," Journal of Financial Economics, Elsevier, vol. 72(2), pages 217-257, May.
  6. Fleming, Jeff & Kirby, Chris & Ostdiek, Barbara, 2003. "The economic value of volatility timing using "realized" volatility," Journal of Financial Economics, Elsevier, vol. 67(3), pages 473-509, March.
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Cited by:
  1. M. Bigeco & E. Grosso & E. Otranto, 2008. "Recognizing and Forecasting the Sign of Financial Local Trends using Hidden Markov Models," Working Paper CRENoS 200803, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  2. Stanislav Anatolyev & Natalia Kryzhanovskaya, 2009. "Directional Prediction of Returns under Asymmetric Loss: Direct and Indirect Approaches," Working Papers w0136, Center for Economic and Financial Research (CEFIR).
  3. Stanislav Anatolyev & Nikolay Gospodinov, 2007. "Modeling Financial Return Dynamics by Decomposition," Working Papers w0095, Center for Economic and Financial Research (CEFIR).

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