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Private Information and High-Frequency Stochastic Volatility

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  • Kelly, David L.
  • Steigerwald, Douglas G

Abstract

We study the e®ect of privately informed traders on measured high frequency price changes and trades in asset markets. We use a standard market microstructure framework where exogenous news is captured by signals that informed agents receive. We show that the entry and exit of informed traders following the arrival of news accounts for high-frequency serial correlation in squared price changes (stochastic volatility) and grades. Because the bid-ask spread of the market specialist tends to shrink as individuals trade and reveal their information, the model also accounts for the empirical observation that high-frequency serial correlation is more pronounced in trades than in squared price changes. A calibration test of the model shows that the features of the market microstructure, without serially correlated news, accounts qualitatively for the serial correlation in the data, but predicts less persistence than is present in the data.

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Paper provided by Department of Economics, UC Santa Barbara in its series University of California at Santa Barbara, Economics Working Paper Series with number qt00n4h4mw.

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Date of creation: 01 Aug 2003
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Handle: RePEc:cdl:ucsbec:qt00n4h4mw

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  1. French, Kenneth R. & Roll, Richard, 1986. "Stock return variances : The arrival of information and the reaction of traders," Journal of Financial Economics, Elsevier, vol. 17(1), pages 5-26, September.
  2. Robert F. Engle & Victor Ng & Michael Rothschild, 1988. "Asset Pricing with a Factor Arch Covariance Structure: Empirical Estimates for Treasury Bills," NBER Technical Working Papers 0065, National Bureau of Economic Research, Inc.
  3. Brock, William A & LeBaron, Blake D, 1996. "A Dynamic Structural Model for Stock Return Volatility and Trading Volume," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 94-110, February.
  4. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
  5. Goodhart, Charles A. E. & O'Hara, Maureen, 1997. "High frequency data in financial markets: Issues and applications," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 73-114, June.
  6. Lawrence R. Glosten & Paul R. Milgrom, 1983. "Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders," Discussion Papers 570, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
  7. Harris, Lawrence, 1986. "A transaction data study of weekly and intradaily patterns in stock returns," Journal of Financial Economics, Elsevier, vol. 16(1), pages 99-117, May.
  8. Haan, Wouter J. den & Spear, Scott A., 1998. "Volatility clustering in real interest rates Theory and evidence," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 431-453, May.
  9. Jamsheed Shorish & Stephen Spear, . "Shaking the Tree: An Agency Theoretic Model of Asset Pricing," GSIA Working Papers 2003-E19, Carnegie Mellon University, Tepper School of Business.
  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. John Owens & Douglas G. Steigerwald, 2005. "Inferring Information Frequency and Quality," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(4), pages 500-524.
  12. Easley, David & O'Hara, Maureen, 1992. " Time and the Process of Security Price Adjustment," Journal of Finance, American Finance Association, vol. 47(2), pages 576-605, June.
  13. 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.
  14. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
  15. Epps, Thomas W, 1975. "Security Price Changes and Transaction Volumes: Theory and Evidence," American Economic Review, American Economic Association, vol. 65(4), pages 586-97, September.
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Cited by:
  1. Yoichi Otsubo & Bruce Mizrach, 2012. "The Market Microstructure of the European Climate Exchange," LSF Research Working Paper Series 12-7, Luxembourg School of Finance, University of Luxembourg.
  2. Jonathan H. Wright, 2000. "Log-periodogram estimation of long memory volatility dependencies with conditionally heavy tailed returns," International Finance Discussion Papers 685, Board of Governors of the Federal Reserve System (U.S.).
  3. Booth, G. Geoffrey & Gurun, Umit G., 2008. "Volatility clustering and the bid-ask spread: Exchange rate behavior in early Renaissance Florence," Journal of Empirical Finance, Elsevier, vol. 15(1), pages 131-144, January.
  4. Steigerwald, Doug & Vagnoni, Richard J., 2001. "Option Market Microstructure and Stochastic Volatility," University of California at Santa Barbara, Economics Working Paper Series qt1v2059c2, Department of Economics, UC Santa Barbara.

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