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Stochastic conditional intensity processes

  • BAUWENS, Luc
  • HAUTSCH, Nikolaus

In this article, we introduce the so-called stochastic conditional intensity (SCI) model by extending Russell's (1999) autoregressive conditional intensity (ACI) model by a latent common dynamic factor that jointly drives the individual intensity components. We show by simulations that the proposed model allows for a wide range of (cross-)autocorrelation structures in multivariate point processes. The model is estimated by simulated maximum likelihood (SML) using the efficient importance sampling (EIS) technique. By modeling price intensities based on NYSE trading, we provide significant evidence for a joint latent factor and show that its inclusion allows for an improved and more parsimonious specification of the multivariate intensity process. Copyright 2006, Oxford University Press.

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Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers RP with number 1937.

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Handle: RePEc:cor:louvrp:1937
Note: In : Journal of Financial Econometrics, 4(3), 450-493, 2006
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  1. Lancaster, Tony, 1979. "Econometric Methods for the Duration of Unemployment," Econometrica, Econometric Society, vol. 47(4), pages 939-56, July.
  2. BAUWENS, Luc & HAUTSCH, Nikolaus, 2003. "Dynamic latent factor models for intensity processes," CORE Discussion Papers 2003103, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. Joel L. Horowitz, 1999. "Semiparametric Estimation of a Proportional Hazard Model with Unobserved Heterogeneity," Econometrica, Econometric Society, vol. 67(5), pages 1001-1028, September.
  4. Nikolaus Hautsch, 2005. "The latent factor VAR model: Testing for a common component in the intraday trading process," FRU Working Papers 2005/03, University of Copenhagen. Department of Economics. Finance Research Unit.
  5. Frank Gerhard & Nikolaus Hautsch, 1999. "Volatility Estimation on the Basis of Price Intensities," CoFE Discussion Paper 99-19, Center of Finance and Econometrics, University of Konstanz.
  6. 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.
  7. Anthony D. Hall & Nikolaus Hautsch, 2004. "A Continuous-Time Measurement of the Buy-Sell Pressure in a Limit Order Book Market," Discussion Papers 04-07, University of Copenhagen. Department of Economics.
  8. Anthony Hall & Nikolaus Hautsch, 2006. "Order aggressiveness and order book dynamics," Empirical Economics, Springer, vol. 30(4), pages 973-1005, January.
  9. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
  10. Zhang, Michael Yuanjie & Russell, Jeffrey R. & Tsay, Ruey S., 2001. "A nonlinear autoregressive conditional duration model with applications to financial transaction data," Journal of Econometrics, Elsevier, vol. 104(1), pages 179-207, August.
  11. Engle, Robert F. & Russell, Jeffrey R., 1997. "Forecasting the frequency of changes in quoted foreign exchange prices with the autoregressive conditional duration model," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 187-212, June.
  12. repec:bla:restud:v:63:y:1996:i:1:p:145-68 is not listed on IDEAS
  13. Roll, Richard, 1984. " A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market," Journal of Finance, American Finance Association, vol. 39(4), pages 1127-39, September.
  14. Liesenfeld, Roman & Richard, Jean-Francois, 2003. "Univariate and multivariate stochastic volatility models: estimation and diagnostics," Journal of Empirical Finance, Elsevier, vol. 10(4), pages 505-531, September.
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