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Beating the Random Walk: Intraday Seasonality and Volatility in a Developing Stock Market

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Author Info

  • Kim-Leng Goh

    (Faculty of Economics and Administration, University of Malaya, Malaysia)

  • Kim-Lian Kok

    (Taylor's Business School, Malaysia)

Abstract

Historical prices information has not been exhaustively exploited in forecasting the 10-minute-ahead Composite Index of the Malaysian stock market. A simple model incorporating intraday seasonality can have lower forecast errors than a random walk. Improved accuracy is achieved when time-varying volatility is included in the time-of-day seasonal model for both in-sample and out-of-sample forecasts. The updating of parameter estimates of these volatility models at each new forecast origin to incorporate the latest available information leads to further improvement in forecast performance.

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Bibliographic Info

Article provided by College of Business, and College of Finance, Feng Chia University, Taichung, Taiwan in its journal International Journal of Business and Economics.

Volume (Year): 5 (2006)
Issue (Month): 1 (April)
Pages: 41-59

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Handle: RePEc:ijb:journl:v:5:y:2006:i:1:p:41-59

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Postal: 100 Wenhwa Road, Seatwen, Taichung
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Related research

Keywords: calendar effects; forecast; ARCH models; random walk;

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References

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  1. Sharma, J. L. & Kennedy, Robert E., 1977. "A Comparative Analysis of Stock Price Behavior on the Bombay, London, and New York Stock Exchanges," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(03), pages 391-413, September.
  2. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
  3. Andrew W. Lo & A. Craig MacKinlay, 1987. "Stock Market Prices Do Not Follow Random Walks: Evidence From a Simple Specification Test," NBER Working Papers 2168, National Bureau of Economic Research, Inc.
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  7. Frennberg, Per & Hansson, Bjorn, 1993. "Testing the random walk hypothesis on Swedish stock prices: 1919-1990," Journal of Banking & Finance, Elsevier, vol. 17(1), pages 175-191, February.
  8. Poterba, James M. & Summers, Lawrence H., 1988. "Mean reversion in stock prices : Evidence and Implications," Journal of Financial Economics, Elsevier, vol. 22(1), pages 27-59, October.
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  10. Akgiray, Vedat, 1989. "Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts," The Journal of Business, University of Chicago Press, vol. 62(1), pages 55-80, January.
  11. Sullivan, Ryan & Timmermann, Allan & White, Halbert, 2001. "Dangers of data mining: The case of calendar effects in stock returns," Journal of Econometrics, Elsevier, vol. 105(1), pages 249-286, November.
  12. David Walsh & Glenn Yu-Gen Tsou, 1998. "Forecasting index volatility: sampling interval and non-trading effects," Applied Financial Economics, Taylor & Francis Journals, vol. 8(5), pages 477-485.
  13. Andersen, Torben G. & Bollerslev, Tim & Lange, Steve, 1999. "Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 457-477, December.
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Cited by:
  1. Jeffrey E. Jarrett, 2008. "Predicting Daily Stock Returns: A Lengthy Study of the Hong Kong and Tokyo Stock Exchanges," International Journal of Business and Economics, College of Business, and College of Finance, Feng Chia University, Taichung, Taiwan, vol. 7(1), pages 37-51, April.

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