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Estimating the impact of good news on stock market volatility

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  • Farooq Malik
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    Abstract

    The literature agrees that bad news increases volatility but disagrees over the impact of good news on stock market volatility and often report it as statistically insignificant. This article shows that accounting for endogenously determined structural breaks within the asymmetric Generalized Autoregressive Conditional Heteroscedastic (GARCH) model reduces volatility persistence and good news significantly decreases volatility. However, good news does not affect volatility if structural breaks are ignored. We validate our empirical results with Monte Carlo simulations and provide an intuitive explanation for our results. Our results resolve earlier inconsistencies in the literature and have important practical implications for building accurate asset pricing models and forecasting of stock market volatility.

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    File URL: http://www.tandfonline.com/doi/abs/10.1080/09603107.2010.534063
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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

    Volume (Year): 21 (2011)
    Issue (Month): 8 ()
    Pages: 545-554

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    Handle: RePEc:taf:apfiec:v:21:y:2011:i:8:p:545-554

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    Web page: http://www.tandfonline.com/RAFE20

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    Cited by:
    1. Hartwell , Christopher A., 2014. "The impact of institutional volatility on financial volatility in transition economies: a GARCH family approach," BOFIT Discussion Papers 6/2014, Bank of Finland, Institute for Economies in Transition.
    2. Worapree Maneesoonthorn & Catherine S. Forbes & Gael M. Martin, 2013. "Inference on Self-Exciting Jumps in Prices and Volatility using High Frequency Measures," Monash Econometrics and Business Statistics Working Papers 28/13, Monash University, Department of Econometrics and Business Statistics.
    3. Hood, Matthew & Malik, Farooq, 2013. "Is gold the best hedge and a safe haven under changing stock market volatility?," Review of Financial Economics, Elsevier, vol. 22(2), pages 47-52.

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