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Modelling Financial High Frequency Data Using Point Processes

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Author Info
Luc Bauwens
Nikolaus Hautsch

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Abstract

In this paper, we give an overview of the state-of-the-art in the econometric literature on the modeling of so-called financial point processes. The latter are associated with the random arrival of specific financial trading events, such as transactions, quote updates, limit orders or price changes observable based on financial high-frequency data. After discussing fundamental statistical concepts of point process theory, we review durationbased and intensity-based models of financial point processes. Whereas duration-based approaches are mostly preferable for univariate time series, intensity-based models provide powerful frameworks to model multivariate point processes in continuous time. We illustrate the most important properties of the individual models and discuss major empirical applications.

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Publisher Info
Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2007-066.

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Length: 35 pages
Date of creation: Nov 2007
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Handle: RePEc:hum:wpaper:sfb649dp2007-066

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Related research
Keywords: Financial point processes; dynamic duration models; dynamic intensity models.;

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis

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Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Nikolaus Hautsch, 2007. "Capturing Common Components in High-Frequency Financial Time Series: A Multivariate Stochastic Multiplicative Error Model," CFS Working Paper Series 2007/25, Center for Financial Studies. [Downloadable!]
    Other versions:
  2. Frank Gerhard & Nikolaus Hautsch, 2006. "A Dynamic Semiparametric Proportional Hazard Model," FRU Working Papers 2006/05, University of Copenhagen. Department of Economics. Finance Research Unit. [Downloadable!]
  3. Frank Gerhard & Nikolaus Hautsch, 2007. "A Dynamic Semiparametric Proportional Hazard Model," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 11(2). [Downloadable!]
  4. Sebastian Braun & Nadja Dwenger & Dorothea Kübler, 2007. "Telling the Truth May Not Pay Off: An Empirical Study of Centralised University Admissions in Germany," SFB 649 Discussion Papers SFB649DP2007-070, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany. [Downloadable!]
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