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Early News is Good News: The Effects of Market Opening on Market Volatility

Author

Listed:
  • Gallo Giampiero M.

    (Università di Firenze; European University Institute)

  • Pacini Barbara

    (Università di Firenze)

Abstract

In this paper, we examine the characteristics of market opening news and its impact on the estimated coefficients of the conditional volatility models of the GARCH class. We find that the differences between the opening price of one day and the closing price of the day before have different characteristics when considering various stock-market indices on which options are actively traded. The impact of a suitable positive-valued transformation of these differences has the effects of modifying the direct impact of daily innovations on volatility and reducing the estimated overall persistence of such innovations. The overall contribution of the variable is evaluated in an out-of-sample forecasting exercise, where we obtain significant improvements above the simple GARCH model.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:sndecm:v:2:y:1998:i:4:n:3
    DOI: 10.2202/1558-3708.1034
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    Cited by:

    1. Beatriz Vaz de Melo Mendes & Victor Bello Accioly, 2017. "Improving (E)GARCH forecasts with robust realized range measures: Evidence from international markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 41(4), pages 631-658, October.
    2. Qi Zhang & Charlie X Cai & Kevin Keasey, 2009. "Forecasting using high-frequency data: a comparison of asymmetric financial duration models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 371-386.
    3. Giampiero M. Gallo, 2001. "Modelling the Impact of Overnight Surprises on Intra‐daily Volatility," Australian Economic Papers, Wiley Blackwell, vol. 40(4), pages 567-580, December.
    4. Paola Zuccolotto, 2002. "Modelling the impact of open volume on inter-trade autoregressive durations," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3-4), pages 49-63.
    5. Li, Gang & Li, Yong, 2015. "Forecasting copper futures volatility under model uncertainty," Resources Policy, Elsevier, vol. 46(P2), pages 167-176.
    6. Ketterer, Janina C., 2014. "The impact of wind power generation on the electricity price in Germany," Energy Economics, Elsevier, vol. 44(C), pages 270-280.
    7. Chen, Chun-Hung & Yu, Wei-Choun & Zivot, Eric, 2012. "Predicting stock volatility using after-hours information: Evidence from the NASDAQ actively traded stocks," International Journal of Forecasting, Elsevier, vol. 28(2), pages 366-383.
    8. 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".
    9. Victor Bello Accioly & Beatriz Vaz de Melo Mendes, 2016. "Assessing the Impact of the Realized Range on the (E)GARCH Volatility: Evidence from Brazil," Brazilian Business Review, Fucape Business School, vol. 13(2), pages 1-26, March.

    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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