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Testing weak exogeneity in the exponential family : an application to financial point processes

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  • DOLADO , Juan J.
  • RODRIGUEZ-POO, Juan
  • VEREDAS, David

Abstract

In this paper, two tests for weak exogeneity in the econometric modelling of financial point processes are proposed. They are motivated by the common practice in many econometric studies of tick-by-tick data of making inference on the joint density of durations and marks through the conditional (marks given durations) density. However, this inference is only valid if the process of the marginal (durations) is weakly exogenous for the parameters of the conditional density, a hypothesis which is often left untested. Under standard pseudo-maximum likelihood conditions, we first derive a simple parametric score/LM teststatistic when the potential dependence between the parameters of interest in the conditional model and the marginal process is assumed to be linear. Next, an alternative consistent test is proposed when the functional form of the dependence is left unspecified. To illustrate the use of these tests, we analyze two types of financial point processes, linked with market microstructure theory and stealth trading hypothesis, for five stocks traded at NYSE: (i) the relationship between tradesize and trade durations and (ii) the relationship between volume and price durations. In general we reject the null hypothesis of weak exogeneity, therefore questioning some results in the literature which rely on separate estimation of each density.

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Bibliographic Info

Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2004049.

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Date of creation: 00 Jul 2004
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Handle: RePEc:cor:louvco:2004049

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Related research

Keywords: weak exogeneity; pseudo-maximum likelihood; semiparametric models; point processes; high-frequency data; stealth trading; mixture of distribution hypothesis;

References

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  1. Xiaohong Chen & Xiaotong Shen, 1998. "Sieve Extremum Estimates for Weakly Dependent Data," Econometrica, Econometric Society, vol. 66(2), pages 289-314, March.
  2. Gourieroux Christian & Monfort Alain & Trognon A, 1981. "Pseudo maximum likelihood methods : theory," CEPREMAP Working Papers (Couverture Orange) 8129, CEPREMAP.
  3. Gourieroux Christian & Monfort Alain & Trognon A, 1982. "Pseudo maximum lilelihood methods : applications to poisson models," CEPREMAP Working Papers (Couverture Orange) 8203, CEPREMAP.
  4. 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.
  5. Hasbrouck, Joel, 1991. " Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
  6. Bauwens, Luc & Giot, Pierre & Grammig, Joachim & Veredas, David, 2004. "A comparison of financial duration models via density forecasts," International Journal of Forecasting, Elsevier, vol. 20(4), pages 589-609.
  7. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
  8. Dolado, Juan J & Maria-Dolores, Ramon, 2002. " Evaluating Changes in the Bank of Spain's Interest Rate Target: An Alternative Approach Using Marked Point Processes," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(2), pages 159-82, May.
  9. BAUWENS, Luc & VEREDAS, David, 1999. "The stochastic conditional duration model: a latent factor model for the analysis of financial durations," CORE Discussion Papers 1999058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  10. Engle, R. & Hendry, D., 1990. "Testing Super Exogeneity And Invariance In Regression Models," Economics Series Working Papers 99100, University of Oxford, Department of Economics.
  11. 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.
  12. Robert F. Engle, 1996. "The Econometrics of Ultra-High Frequency Data," NBER Working Papers 5816, National Bureau of Economic Research, Inc.
  13. BAUWENS, Luc & GIOT, Pierre, . "The logarithmic ACD model: an application to the bid-ask quote process of three NYSE stocks," CORE Discussion Papers RP -1497, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  14. GIOT, Pierre, 1999. "Time transformations, intraday data and volatility models," CORE Discussion Papers 1999044, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  15. 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.
  16. 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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Cited by:
  1. Georges Dionne & Pierre Duchesne & Maria Pacurar, 2005. "Intraday Value at Risk (IVaR) Using Tick-by-Tick Data with Application to the Toronto Stock Exchange," Cahiers de recherche 0533, CIRPEE.

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