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Using news analytics data in GARCH models

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  • Sidorov, Sergei

    ()
    (Saratov State University, Russia)

  • Date, Paresh

    ()
    (Brunel University, London)

  • Balash, Vladimir

    ()
    (Saratov State University, Russia)

Abstract

In this paper we analyze the impact of extraneous sources of information (viz. news and trade volume) on stock volatility by considering some augmented GARCH models. We suppose that trading volume can be considered as a proportional proxy for information arrivals to the market. Then we will consider the daily number of press releases on a stock (news intensity) as an alternative explanatory variable in the basic equation of GARCH model. We will show that the GARCH(1,1) model augmented with volume does remove GARCH and ARCH effects for the most of the companies, while the GARCH(1,1) model augmented with news intensity has difficulties in removing the impact of log return on volatility.

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Bibliographic Info

Article provided by Publishing House "SINERGIA PRESS" in its journal Applied Econometrics.

Volume (Year): 29 (2013)
Issue (Month): 1 ()
Pages: 82-96

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Handle: RePEc:ris:apltrx:0204

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Related research

Keywords: stock volatility modeling; GARCH models;

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References

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  1. Miyakoshi, Tatsuyoshi, 2002. "ARCH versus information-based variances: evidence from the Tokyo Stock Market," Japan and the World Economy, Elsevier, vol. 14(2), pages 215-231, April.
  2. Karpoff, Jonathan M., 1987. "The Relation between Price Changes and Trading Volume: A Survey," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(01), pages 109-126, March.
  3. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  4. Gust Janssen, 2004. "Public information arrival and volatility persistence in financial markets," The European Journal of Finance, Taylor & Francis Journals, vol. 10(3), pages 177-197.
  5. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
  6. Ederington, Louis H & Lee, Jae Ha, 1993. " How Markets Process Information: News Releases and Volatility," Journal of Finance, American Finance Association, vol. 48(4), pages 1161-91, September.
  7. Vanitha Ragunathan & Albert Peker, 1997. "Price variability, trading volume and market depth: evidence from the Australian futures market," Applied Financial Economics, Taylor & Francis Journals, vol. 7(5), pages 447-454.
  8. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
  9. Kalev, Petko S. & Liu, Wai-Man & Pham, Peter K. & Jarnecic, Elvis, 2004. "Public information arrival and volatility of intraday stock returns," Journal of Banking & Finance, Elsevier, vol. 28(6), pages 1441-1467, June.
  10. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-55, January.
  11. Andersen, Torben G, 1996. " Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility," Journal of Finance, American Finance Association, vol. 51(1), pages 169-204, March.
  12. Lamoureux, Christopher G & Lastrapes, William D, 1990. " Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects," Journal of Finance, American Finance Association, vol. 45(1), pages 221-29, March.
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