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

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Author Info

  • Peter F. Christoffersen

    (SMU)

  • Francis X. Diebold
  • Roberto S. Mariano
  • Anthony S. Tay
  • 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 22075.

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Date of creation: Jan 2006
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Handle: RePEc:eab:financ:22075

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

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References

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  1. Fleming, Jeff & Kirby, Chris & Ostdiek, Barbara, 2003. "The economic value of volatility timing using "realized" volatility," Journal of Financial Economics, Elsevier, Elsevier, vol. 67(3), pages 473-509, March.
  2. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  3. Peter F. Christoffersen & Francis X. Diebold, 2003. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," NBER Working Papers, National Bureau of Economic Research, Inc 10009, National Bureau of Economic Research, Inc.
  4. 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, Elsevier, vol. 72(2), pages 217-257, May.
  5. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001. "Modeling and Forecasting Realized Volatility," NBER Working Papers, National Bureau of Economic Research, Inc 8160, National Bureau of Economic Research, Inc.
  6. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper, Federal Reserve Bank of New York 9525, Federal Reserve Bank of New York.
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Cited by:
  1. Stanislav Anatolyev & Natalia Kryzhanovskaya, 2009. "Directional Prediction of Returns under Asymmetric Loss: Direct and Indirect Approaches," Working Papers, Center for Economic and Financial Research (CEFIR) w0136, Center for Economic and Financial Research (CEFIR).
  2. Stanislav Anatolyev & Nikolay Gospodinov, 2007. "Modeling Financial Return Dynamics by Decomposition," Working Papers, Center for Economic and Financial Research (CEFIR) w0095, Center for Economic and Financial Research (CEFIR).
  3. M. Bigeco & E. Grosso & E. Otranto, 2008. "Recognizing and Forecasting the Sign of Financial Local Trends using Hidden Markov Models," Working Paper CRENoS, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia 200803, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.

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