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Univariate Dynamic Intensity Models

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Econometrics of Financial High-Frequency Data
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Abstract

This chapter presents dynamic parameterizations of the intensity function. We model the intensity in continuous time which allows to update the intensity process whenever required. This is in contrast to Chaps. 5, 6 and 10 discussing discrete-time processes which are only updated at discrete points in time. Moreover, instead of specifying a model for durations (Chaps. 5 and 6) or the integrated (baseline) hazard function (Chap. 10), we discuss dynamic models which are built directly on the intensity. As illustrated in this and the next chapter, such a framework yields a valuable approach to account for time-varying covariates and multivariate structures.

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Notes

  1. 1.

    Such a process might be associated, for instance, with the arrival of new (limit) orders in the market.

References

  1. Bowsher CG (2007) Modelling security markets in continuous time: intensity based, multivariate point process models. J Econom 141:876–912

    Article  Google Scholar 

  2. Brémaud P, Massoulié L (1996) Stability of nonlinear Hawkes processes. Ann Probab 24: 1563–1588

    Article  Google Scholar 

  3. Brownlees C, Cipollini F, Gallo GM (2011) Intra-daily volume modeling and prediction for algorithmic trading. J Financ Econom 9:489–518

    Article  Google Scholar 

  4. Daley D, Vere-Jones D (2005) An introduction to the theory of point processes. Volume I: Elementary theory and methods. Springer, New York

    Google Scholar 

  5. Ding Z, Granger CWJ (1996) Modeling volatility persistence of speculative returns: a new approach. J Econom 73:185–215

    Article  Google Scholar 

  6. Engle RF (2000) The econometrics of ultra-high-frequency data. Econometrica 68(1):1–22

    Article  Google Scholar 

  7. Hamilton JD, Jorda O (2002) A model of the federal funds rate target. J Polit Econ 110:1135

    Article  Google Scholar 

  8. Hawkes AG (1971) Spectra of some self-exciting and mutually exciting point processes. Biometrika 58:83–90

    Article  Google Scholar 

  9. Hawkes AG, Oakes D (1974) A cluster process representation of a self-exciting process. J Appl Probab 11:493–503

    Article  Google Scholar 

  10. Kalbfleisch JD, Prentice RL (1980) The statistical analysis of failure time data, Wiley, New York

    Google Scholar 

  11. Koulikov D (2003) Modeling sequences of long memory non-negative covariance stationary random variables. Discussion Paper 156, CAF

    Google Scholar 

  12. Møller J, Rasmussen J (2005) Perfect simulation of Hawkes processes. Adv Appl Probab 37: 629–646

    Article  Google Scholar 

  13. Nelson D (1991) Conditional heteroskedasticity in asset returns: a new approach. J Econom 43:227–251

    Google Scholar 

  14. Ogata Y (1988) Statistical models for earthquake occurrences and residual analysis for point processes. J Am Stat Assoc 83:9–27

    Article  Google Scholar 

  15. Ogata Y, Akaike H (1982) On linear intensity models for mixed Doubly stochastic poisson and self-exciting point processes. J R Stat Soc Series B 44:102–107

    Google Scholar 

  16. Russell JR (1999) Econometric modeling of multivariate irregularly-spaced high-frequency data. Working Paper, University of Chicago

    Google Scholar 

  17. Vere-Jones D (1970) Stochastic models for earthquake occurrence. J R Stat Soc Series B 32:1–62

    Google Scholar 

  18. Vere-Jones D, Ozaki T (1982) Some examples of statistical inference applied to earthquake data. Ann Inst Stat Math 34:189–207

    Article  Google Scholar 

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Correspondence to Nikolaus Hautsch .

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Hautsch, N. (2012). Univariate Dynamic Intensity Models. In: Econometrics of Financial High-Frequency Data. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21925-2_11

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  • DOI: https://doi.org/10.1007/978-3-642-21925-2_11

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  • Print ISBN: 978-3-642-21924-5

  • Online ISBN: 978-3-642-21925-2

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