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Modelling the Impact of Overnight Surprises on Intra-daily Stock Returns

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Abstract

In this paper we examine under what circumstances the information accumulated during market closing time and conveyed to the price formation at market opening may be exploited to predict where the stock price will be at the end of the trading day. In our sample of three financial time series, we find that, in spite of linear uncorrelatedness, there exists a strong nonlinear dependence structure in the conditional mean of the intra-daily returns. To model this structure we use the functional-coefficient (FC) model of Cai, Fan, and Yao (2000) where the coefficients are time-varying and dependent on the state of stock return volatility. Out-of-sample forecast performances of the FC models and linear models where the coefficients are constant are also compared using the criteria of mean square forecast errors, trading returns, and directional forecasts.

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Paper provided by Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti" in its series Econometrics Working Papers Archive with number wp2001_03.

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Length: 17 pages
Date of creation: 24 Oct 2001
Date of revision:
Handle: RePEc:fir:econom:wp2001_03
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Keywords: Functional-coefficient model; Nonlinearity; Predictive ability; Volatility.;

Find related papers by JEL classification:

  • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
  • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
  • F3 - International Economics - - International Finance

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  1. Cooper, Michael, 1999. "Filter Rules Based on Price and Volume in Individual Security Overreaction," Review of Financial Studies, Society for Financial Studies, vol. 12(4), pages 901-35.
  2. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
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  4. Ryan Sullivan & Allan Timmermann & Halbert White, 1999. "Data-Snooping, Technical Trading Rule Performance, and the Bootstrap," Journal of Finance, American Finance Association, vol. 54(5), pages 1647-1691, October.
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  6. Geweke, John, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data: Comment," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 269-71, July.
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  12. C. W. J. Granger & Zhuanxin Ding, 1995. "Some Properties of Absolute Return: An Alternative Measure of Risk," Annals of Economics and Statistics, GENES, issue 40, pages 67-91.
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  17. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  18. repec:adr:anecst:y:1995:i:40:p:04 is not listed on IDEAS
  19. Granger, Clive W. J., 1992. "Forecasting stock market prices: Lessons for forecasters," International Journal of Forecasting, Elsevier, vol. 8(1), pages 3-13, June.
  20. Stoll, Hans R & Whaley, Robert E, 1990. "Stock Market Structure and Volatility," Review of Financial Studies, Society for Financial Studies, vol. 3(1), pages 37-71.
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  22. Amihud, Yakov & Mendelson, Haim, 1987. " Trading Mechanisms and Stock Returns: An Empirical Investigation," Journal of Finance, American Finance Association, vol. 42(3), pages 533-53, July.
  23. Robinson, P M, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data: Comment," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 276-79, July.
  24. repec:adr:anecst:y:1995:i:40 is not listed on IDEAS
  25. A. Ronald Gallant & Chien-Te Hsu & George Tauchen, 1999. "Using Daily Range Data To Calibrate Volatility Diffusions And Extract The Forward Integrated Variance," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 617-631, November.
  26. Zongwu Cai & Jianqing Fan & Qiwei Yao, 2000. "Functional-coefficient regression models for nonlinear time series," LSE Research Online Documents on Economics 6314, London School of Economics and Political Science, LSE Library.
  27. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
  28. Dow, James & Gorton, Gary, 1993. "Trading, Communication and the Response of Asset Prices to News," Economic Journal, Royal Economic Society, vol. 103(418), pages 639-46, May.
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Cited by:

  1. Tong, Bin & Diao, Xundi & Wu, Chongfeng, 2015. "Modeling asymmetric and dynamic dependence of overnight and daytime returns: An empirical evidence from China Banking Sector," Economic Modelling, Elsevier, vol. 51(C), pages 366-382.
  2. Ana-Maria Fuertes & Elena Kalotychou & Natasa Todorovic, 2015. "Daily volume, intraday and overnight returns for volatility prediction: profitability or accuracy?," Review of Quantitative Finance and Accounting, Springer, vol. 45(2), pages 251-278, August.

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