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The econometrics of randomly spaced financial data: a survey

  • Monteiro, André A.

This paper provides an introduction to the problem of modeling randomly spaced longitudinal data. Although Point Process theory was developed mostly in the sixties and early seventies, only in the nineties did this field of Probability theory attract the attention of researchers working in Financial Econometrics. The large increase, observed since, in the number of different classes of Econometric models for dealing with financial duration data, has been mostly due to the increased availability of both trade-by-trade data from equity markets and daily default and rating migration data from credit markets. This paper provides an overview of the main Econometric models available in the literature for dealing with what is sometimes called tick data. Additionally, a synthesis of the basic theory underlying these models is also presented. Finally, a new theorem dealing with the identifiability of latent intensity factors from point process data, jointly with a heuristic proof, is introduced.

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Paper provided by Universidad Carlos III de Madrid. Departamento de Estadística in its series DES - Working Papers. Statistics and Econometrics. WS with number ws097924.

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Date of creation: Dec 2009
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Handle: RePEc:cte:wsrepe:ws097924
Contact details of provider: Web page: http://portal.uc3m.es/portal/page/portal/dpto_estadistica

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  1. Frank Gerhard & Nikolaus Hautsch, . "Semiparametric autoregressive conditional proportional hazard models," Economics Papers 2002-W2, Economics Group, Nuffield College, University of Oxford.
  2. Clive Bowsher, 2002. "Modelling Security Market Events in Continuous Time: Intensity based, Multivariate Point Process Models," Economics Papers 2002-W22, Economics Group, Nuffield College, University of Oxford.
  3. Jarrow, Robert A & Lando, David & Turnbull, Stuart M, 1997. "A Markov Model for the Term Structure of Credit Risk Spreads," Review of Financial Studies, Society for Financial Studies, vol. 10(2), pages 481-523.
  4. 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).
  5. FERNANDES, Marcelo & GRAMMIG, Joachim, 2001. "A family of autoregressive conditional duration models," CORE Discussion Papers 2001036, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  6. Drost, Feike C & Werker, Bas J M, 2004. "Semiparametric Duration Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 40-50, January.
  7. Luc Bauwens & Nikolaus Hautsch, 2006. "Stochastic Conditional Intensity Processes," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(3), pages 450-493.
  8. Gagliardini, P. & Gourieroux, C., 2005. "Migration correlation: Definition and efficient estimation," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 865-894, April.
  9. Patrick Gagliardini & Christian Gourieroux, 2004. "Stochastic Migration Models with Application to Corporate Risk," Working Papers 2004-35, Centre de Recherche en Economie et Statistique.
  10. Koopman, Siem Jan & Lucas, Andre & Monteiro, Andre, 2008. "The multi-state latent factor intensity model for credit rating transitions," Journal of Econometrics, Elsevier, vol. 142(1), pages 399-424, January.
  11. Bauwens, L. & Galli, F., 2009. "Efficient importance sampling for ML estimation of SCD models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1974-1992, April.
  12. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
  13. Sergio M. Focardi & Frank J. Fabozzi, 2005. "An autoregressive conditional duration model of credit-risk contagion," Journal of Risk Finance, Emerald Group Publishing, vol. 6(3), pages 208-225, May.
  14. Eric Ghysels & Christian Gourieroux & Joanna Jasiak, 1997. "Stochastic Volatility Duration Models," Working Papers 97-46, Centre de Recherche en Economie et Statistique.
  15. Meitz, Mika & Terasvirta, Timo, 2006. "Evaluating Models of Autoregressive Conditional Duration," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 104-124, January.
  16. Luc Bauwens & Nikolaus Hautsch, 2007. "Modelling Financial High Frequency Data Using Point Processes," SFB 649 Discussion Papers SFB649DP2007-066, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  17. Andrew Harvey & Esther Ruiz & Neil Shephard, 1994. "Multivariate Stochastic Variance Models," Review of Economic Studies, Oxford University Press, vol. 61(2), pages 247-264.
  18. Drost, F.C. & Werker, B.J.M., 2004. "Semiparametric duration models," Other publications TiSEM a1895e3e-f720-454b-9613-f, Tilburg University, School of Economics and Management.
  19. Richard, Jean-Francois & Zhang, Wei, 2007. "Efficient high-dimensional importance sampling," Journal of Econometrics, Elsevier, vol. 141(2), pages 1385-1411, December.
  20. Ruiz, Esther, 1994. "Quasi-maximum likelihood estimation of stochastic volatility models," Journal of Econometrics, Elsevier, vol. 63(1), pages 289-306, July.
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  23. repec:pit:wpaper:321 is not listed on IDEAS
  24. 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.
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