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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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File URL: http://local.disia.unifi.it/ricerca/pubblicazioni/working_papers/2001/wp2001_03.pdf
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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
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. 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.
  2. 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.
  3. 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 261-268, July.
  4. 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 901-935.
  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. Dow, James & Gorton, Gary, 1993. "Trading, Communication and the Response of Asset Prices to News," Economic Journal, Royal Economic Society, vol. 103(418), pages 639-646, May.
  7. 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 273-276, July.
  8. 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.
  9. Gallo Giampiero M. & Pacini Barbara, 1998. "Early News is Good News: The Effects of Market Opening on Market Volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(4), pages 1-19, January.
  10. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
  11. 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.
  12. 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.
  13. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
  14. 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 272-272, July.
  15. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  16. 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.
  17. 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 276-279, July.
  18. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
  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. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
  21. 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 268-269, July.
  22. repec:adr:anecst:y:1995:i:40:p:04 is not listed on IDEAS
  23. Romer, David, 1993. "Rational Asset-Price Movements without News," American Economic Review, American Economic Association, vol. 83(5), pages 1112-1130, December.
  24. 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 269-271, July.
  25. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  26. Amihud, Yakov & Mendelson, Haim, 1987. " Trading Mechanisms and Stock Returns: An Empirical Investigation," Journal of Finance, American Finance Association, vol. 42(3), pages 533-553, July.
  27. 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.
  28. repec:adr:anecst:y:1995:i:40 is not listed on IDEAS
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Cited by:

  1. Nicholas Taylor, 2008. "The predictive value of temporally disaggregated volatility: evidence from index futures markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 721-742.
  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.
  3. 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.
  4. Blanc, Pierre & Chicheportiche, Rémy & Bouchaud, Jean-Philippe, 2014. "The fine structure of volatility feedback II: Overnight and intra-day effects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 58-75.
  5. Peter Reinhard Hansen & Asger Lunde, 2005. "A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(4), pages 525-554.

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