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Dynamic latent factor models for intensity processes

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  • BAUWENS, Luc
  • HAUTSCH, Nikolaus

Abstract

This paper introduces a new framework for the dynamic modelling of univariate and multivariate point processes. The so-called latent factor intensity (LFI) model is based on the assumption that the intensity function consists of univariate or multivariate observation driven dynamic components and a univariate dynamic latent factor. In this sense, the model corresponds to a dynamic extension of a doubly stochastic Poisson process. We illustrate alternative parameterizations of the observation driven component based on autoregressive conditional intensity (ACI) specifications, as well as Hawkes types models. Based on simulation studies, it is shown that the proposed model provides a flexible tool to capture the joint dynamics of multivariate point processes. Since the latent component has to be integrated out, the model is estimated by simulated maximum likelihood based upon efficient importance sampling techniques. Applications of univariate and bivariate LFI models to transaction data extracted from the German XETRA trading system provide evidence for an improvement of the econometric specification when observable as well as unobservable dynamic components are taken into account.

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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 2003103.

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Date of creation: 00 Dec 2003
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Handle: RePEc:cor:louvco:2003103

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Related research

Keywords: multivariate point process; latent factor; transaction durations; efficient importance sampling;

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References

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  1. repec:fth:louvco:2001/36 is not listed on IDEAS
  2. Fernandes, Marcelo & Grammig, Joachim, 2003. "A family of autoregressive conditional duration models," Economics Working Papers (Ensaios Economicos da EPGE) 501, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil).
  3. Nikolaus Hautsch, 2006. "Testing the Conditional Mean Function of Autoregressive Conditional Duration Models," FRU Working Papers 2006/06, University of Copenhagen. Department of Economics. Finance Research Unit.
  4. Clive G. Bowsher, 2005. "Modelling Security Market Events in Continuous Time: Intensity Based, Multivariate Point Process Models," Economics Papers 2005-W26, Economics Group, Nuffield College, University of Oxford.
  5. repec:fth:louvco:9958 is not listed on IDEAS
  6. Engle, Robert F & Lunde, Asger, 1998. "Trades and Quotes: A Bivariate Point Process," University of California at San Diego, Economics Working Paper Series qt8bh079sq, Department of Economics, UC San Diego.
  7. Dufour, Alfonso & Engle, Robert F, 1999. "Time and the Price Impact of a Trade," University of California at San Diego, Economics Working Paper Series qt62c0h04j, Department of Economics, UC San Diego.
  8. 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.
  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. 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.
  11. Heinen, Andreas & Rengifo, Erick, 2007. "Multivariate autoregressive modeling of time series count data using copulas," Journal of Empirical Finance, Elsevier, vol. 14(4), pages 564-583, September.
  12. 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.
  13. Joachim Grammig & Kai-Oliver Maurer, 2000. "Non-monotonic hazard functions and the autoregressive conditional duration model," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 16-38.
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Citations

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Cited by:
  1. Siem Jan Koopman & André Lucas & André Monteiro, 2005. "The Multi-State Latent Factor Intensity Model for Credit Rating Transitions," Tinbergen Institute Discussion Papers 05-071/4, Tinbergen Institute, revised 04 Jul 2005.
  2. Clive G. Bowsher, 2003. "Modelling Security Market Events in Continuous Time: Intensity Based, Multivariate Point Process Models," Economics Papers 2003-W03, Economics Group, Nuffield College, University of Oxford.
  3. Large, Jeremy, 2007. "Measuring the resiliency of an electronic limit order book," Journal of Financial Markets, Elsevier, vol. 10(1), pages 1-25, February.
  4. Roman Liesenfeld & Jean-Francois Richard, 2006. "Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 335-360.
  5. Liesenfeld, Roman & Richard, Jean-François, 2008. "Improving MCMC, using efficient importance sampling," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 272-288, December.
  6. GRAMMIG, Joachim & HEINEN, Andréas & RENGIFO, Erick, 2004. "Trading activity and liquidity supply in a pure limit order book market," CORE Discussion Papers 2004058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  7. Simonsen, Ola, 2005. "An Empirical Model for Durations in Stocks," UmeÃ¥ Economic Studies 657, Umeå University, Department of Economics.
  8. Anthony D. Hall & Nikolaus Hautsch, 2004. "Order Aggressiveness and Order Book Dynamics," FRU Working Papers 2005/04, University of Copenhagen. Department of Economics. Finance Research Unit.
  9. Grammig, Joachin & Heinen, Andreas & Rengifo, Erick, 2004. "Trading activity and liquidity supply in a pure limit order book market: An empirical analysis using a multivariate count data model," MPRA Paper 8115, University Library of Munich, Germany.

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