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

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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  1. repec:att:wimass:9417 is not listed on IDEAS
  2. Romer, David, 1993. "Rational Asset-Price Movements without News," American Economic Review, American Economic Association, vol. 83(5), pages 1112-30, December.
  3. Charles Cao & Eric Ghysels & Frank Hatheway, 2000. "Price Discovery without Trading: Evidence from the Nasdaq Preopening," Journal of Finance, American Finance Association, vol. 55(3), pages 1339-1365, 06.
  4. Kenneth D. West, 1994. "Asymptotic Inference About Predictive Ability," Macroeconomics 9410002, EconWPA.
  5. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
  6. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  7. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 261-68, July.
  8. 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.
  9. Granger, Clive W J, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data: Comment," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 268-69, July.
  10. Ho, Hwai-Chung Jeff & Lin, Chien-fu, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data: Comment," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 272, July.
  11. Sullivan, Ryan & Timmermann, Allan G & White, Halbert, 1998. "Data-Snooping, Technical Trading Rule Performance and the Bootstrap," CEPR Discussion Papers 1976, C.E.P.R. Discussion Papers.
  12. 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.
  13. Gallo, G.M. & Pacini, B., 1998. "Early News Is Good News. The Effects of Market Opening on Market Volatility," Economics Working Papers eco98/3, European University Institute.
  14. C. W. J. GRANGER & Zhuanxin DING, 1995. "Some Properties of Absolute Return: An Alternative Measure of Risk," Annales d'Economie et de Statistique, ENSAE, issue 40, pages 67-91.
  15. Granger, Clive W. J., 1992. "Forecasting stock market prices: Lessons for forecasters," International Journal of Forecasting, Elsevier, vol. 8(1), pages 3-13, June.
  16. 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.
  17. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  18. Gallant, A. Ronald & Hsu, Chien-Te & Tauchen, George, 2000. "Using Daily Range Data to Calibrate Volatility Diffusions and Extract the Forward Integrated Variance," Working Papers 00-04, Duke University, Department of Economics.
  19. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-55, January.
  20. Baillie, Richard T, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data: Comment," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 273-76, July.
  21. 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.
  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. 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.
  24. 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.
  25. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  26. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
  27. 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.
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