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On the Effects of Private Information on Volatility

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Author Info

  • Anne Opschoor

    ()
    (Erasmus University Rotterdam and the Tinbergen Institute)

  • Michel van der Wel

    ()
    (Erasmus University Rotterdam, Tinbergen Institute, ERIM and CREATES)

  • Dick van Dijk

    ()
    (Erasmus University Rotterdam, Tinbergen Institute and ERIM.)

  • Nick Taylor

    ()
    (Cardiff Business School)

Abstract

We study the impact of private information on volatility. We develop a comprehensive framework to investigate this link while controlling for the effects of both public information (such as macroeconomic news releases) and private information on prices and the effect of public information on volatility. Using high-frequency 30-year U.S. Treasury bond futures data, we find that private information, measured by order flow, is statistically and economically significant for explaining volatility. Private information is more important than public information, with the effect of an order flow shock on volatility being 18% larger than the effect of the most influential macroeconomic announcement.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2012-08.

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Length: 42
Date of creation: 02 2012
Date of revision:
Handle: RePEc:aah:create:2012-08

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Information; order flow; macroeconomic announcements; Treasury futures.;

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References

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  1. He, Yan & Lin, Hai & Wang, Junbo & Wu, Chunchi, 2009. "Price discovery in the round-the-clock U.S. Treasury market," Journal of Financial Intermediation, Elsevier, vol. 18(3), pages 464-490, July.
  2. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
  3. Manaster, Steven & Mann, Steven C, 1996. "Life in the Pits: Competitive Market Making and Inventory Control," Review of Financial Studies, Society for Financial Studies, vol. 9(3), pages 953-75.
  4. Michael J. Fleming & Eli M. Remolona, 1999. "Price Formation and Liquidity in the U.S. Treasury Market: The Response to Public Information," Journal of Finance, American Finance Association, vol. 54(5), pages 1901-1915, October.
  5. Paolo Pasquariello & Clara Vega, 2006. "Informed and strategic order flow in the bond markets," International Finance Discussion Papers 874, Board of Governors of the Federal Reserve System (U.S.).
  6. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
  7. Alessandro Beber & Michael W. Brandt, 2010. "When It Cannot Get Better or Worse: The Asymmetric Impact of Good and Bad News on�Bond�Returns in Expansions and Recessions," Review of Finance, European Finance Association, vol. 14(1), pages 119-155.
  8. Brenner, Menachem & Pasquariello, Paolo & Subrahmanyam, Marti, 2009. "On the Volatility and Comovement of U.S. Financial Markets around Macroeconomic News Announcements," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(06), pages 1265-1289, December.
  9. de Goeij, Peter & Marquering, Wessel, 2006. "Macroeconomic announcements and asymmetric volatility in bond returns," Journal of Banking & Finance, Elsevier, vol. 30(10), pages 2659-2680, October.
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Cited by:
  1. Heejoon Han & Dennis Kristensen, 2013. "Asymptotic theory for the QMLE in GARCH-X models with stationary and non-stationary covariates," CeMMAP working papers CWP18/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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