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News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons

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  • Xilong Chen
  • Eric Ghysels
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    Abstract

    We introduce a new class of parametric models applicable to a mixture of high and low frequency returns and revisit the concept of news impact curves introduced by Engle and Ng (1993). Overall, we find that moderately good (intra-daily) news reduces volatility (the next day), while both very good news (unusual high intra-daily positive returns) and bad news (negative returns) increase volatility, with the latter having a more severe impact. The asymmetries disappear over longer horizons. Models featuring asymmetries dominate in terms of out-of-sample forecasting performance, especially during the 2007--2008 financial crisis. The Author 2010. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.

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    Bibliographic Info

    Article provided by Society for Financial Studies in its journal Review of Financial Studies.

    Volume (Year): 24 (2011)
    Issue (Month): 1 (October)
    Pages: 46-81

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    Handle: RePEc:oup:rfinst:v:24:y:2011:i:1:p:46-81

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    Cited by:
    1. Chevallier, Julien & Sévi, Benoît, 2012. "On the volatility–volume relationship in energy futures markets using intraday data," Energy Economics, Elsevier, vol. 34(6), pages 1896-1909.
    2. Dimpfl, Thomas & Jank, Stephan, 2011. "Can internet search queries help to predict stock market volatility?," CFR Working Papers 11-15, University of Cologne, Centre for Financial Research (CFR).
    3. Ornthanalai, Chayawat, 2014. "Lévy jump risk: Evidence from options and returns," Journal of Financial Economics, Elsevier, vol. 112(1), pages 69-90.
    4. Mark J. Jensen & John M. Maheu, 2012. "Estimating a semiparametric asymmetric stochastic volatility model with a Dirichlet process mixture," Working Paper 2012-06, Federal Reserve Bank of Atlanta.
    5. Emre Alper, C. & Fendoglu, Salih & Saltoglu, Burak, 2012. "MIDAS volatility forecast performance under market stress: Evidence from emerging stock markets," Economics Letters, Elsevier, vol. 117(2), pages 528-532.
    6. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
    7. Mun, Kyung-Chun, 2012. "The joint response of stock and foreign exchange markets to macroeconomic surprises: Using US and Japanese data," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 383-394.
    8. Enno Mammen & Byeong U. Park & Melanie Schienle, 2012. "Additive Models: Extensions and Related Models," SFB 649 Discussion Papers SFB649DP2012-045, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    9. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.

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