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Is after-hours trading informative?

  • Ulibarri, Carlos A.

This article investigates price and trading volume relations for near term crude oil contracts at the New York Mercantile Exchange (NYMEX). The study investigates the informativeness of after-hours trading under the prior assumption that daytime and after-hours trading sessions are completely segmented. The research methodology uses a vector autoregressive (VAR) structural model to identify the lead/lag structure between the leading overnight session and the lagging daytime session. This framework permits us to impose testable restrictions in considering the view that after-hours price changes and trading volumes provide contemporaneous information in the daytime price discovery process. Furthermore, the reduced-form VAR allows testing whether innovations (surprises) in daytime prices and trading activity influence overnight price/volume behavior.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 14818.

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Date of creation: 1998
Date of revision:
Publication status: Published in Journal of Futures Markets 5.18(1998): pp. 563-579
Handle: RePEc:pra:mprapa:14818
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  1. Ben S. Bernanke, 1986. "Alternative Explanations of the Money-Income Correlation," NBER Working Papers 1842, National Bureau of Economic Research, Inc.
  2. Christopher A. Sims, 1986. "Are forecasting models usable for policy analysis?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-16.
  3. Olivier J. Blanchard & Mark W. Watson, 1984. "Are Business Cycles All Alike?," NBER Working Papers 1392, National Bureau of Economic Research, Inc.
  4. Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1992. "Stock Prices and Volume," Review of Financial Studies, Society for Financial Studies, vol. 5(2), pages 199-242.
  5. Jain, Prem C. & Joh, Gun-Ho, 1988. "The Dependence between Hourly Prices and Trading Volume," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 23(03), pages 269-283, September.
  6. Wang, Jiang & Grossman, Sanford & Campbell, John, 1993. "Trading Volume and Serial Correlation in Stock Returns," Scholarly Articles 3128710, Harvard University Department of Economics.
  7. Grunbichler Andreas & Longstaff Francis A. & Schwartz Eduardo S., 1994. "Electronic Screen Trading and the Transmission of Information: An Empirical Examination," Journal of Financial Intermediation, Elsevier, vol. 3(2), pages 166-187, March.
  8. 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.
  9. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-38, July.
  10. John W. Keating, 1992. "Structural approaches to vector autoregressions," Review, Federal Reserve Bank of St. Louis, issue Sep, pages 37-57.
  11. Zellner, Arnold, 1979. "Causality and econometrics," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 10(1), pages 9-54, January.
  12. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  13. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-72, June.
  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.
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