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

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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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  • Giampiero M. Gallo & Yongmiao Hong & Tae-Why Lee, 2001. "Modelling the Impact of Overnight Surprises on Intra-daily Stock Returns," Econometrics Working Papers Archive wp2001_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    • Handle: RePEc:fir:econom:wp2001_03
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    Cited by:

    1. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2020. "Volatility forecasting using related markets’ information for the Tokyo stock exchange," Economic Modelling, Elsevier, vol. 90(C), pages 143-158.
    2. Dimos S. Kambouroudis & David G. McMillan & Katerina Tsakou, 2021. "Forecasting realized volatility: The role of implied volatility, leverage effect, overnight returns, and volatility of realized volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(10), pages 1618-1639, October.
    3. 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.
    4. Insana, Alessandra, 2022. "Does systematic risk change when markets close? An analysis using stocks’ beta," Economic Modelling, Elsevier, vol. 109(C).
    5. Peter Reinhard Hansen & Asger Lunde, 2005. "A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data," Journal of Financial Econometrics, Oxford University Press, vol. 3(4), pages 525-554.
    6. 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.
    7. 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.
    8. 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.
    9. Tseng-Chan Tseng & Hung-Cheng Lai & Cha-Fei Lin, 2012. "The impact of overnight returns on realized volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 22(5), pages 357-364, March.
    10. Vincenzo Candila & Giampiero M. Gallo & Lea Petrella, 2020. "Mixed--frequency quantile regressions to forecast Value--at--Risk and Expected Shortfall," Papers 2011.00552, arXiv.org, revised Mar 2023.
    11. Dimos Kambouroudis & David McMillan & Katerina Tsakou, 2019. "Forecasting Realized Volatility: The role of implied volatility, leverage effect, overnight returns and volatility of realized volatility," Working Papers 2019-03, Swansea University, School of Management.

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    More about this item

    Keywords

    Functional-coefficient model; Nonlinearity; Predictive ability; Volatility.;
    All these keywords.

    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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