An estimation procedure for the Hawkes process

By Matthias Kirchner. 2015

100 citations recorded by the source corpus.

In this paper, we present a nonparametric estimation procedure for the multivariate Hawkes point process. The timeline is cut into bins and—for each component process—the number of points in each bin is counted. As a consequence of earlier results in Kirchner [Stoch. Process. Appl., 2016, 162, 2494–2525], the distribution of the resulting ‘bin-count sequences’ can be approximated by an integer-valued autoregressive model known as the (multivariate) INAR(p) model. We represent the INAR(p) model as a standard vector-valued linear autoregressive time series with white-noise innovations (VAR(p)). We establish consistency and asymptotic normality for conditional least-squares estimation of the VAR(p), respectively, the INAR(p) model. After appropriate scaling, these time-series estimates yield estimates for the underlying multivariate Hawkes process as well as corresponding variance estimates. The estimates depend on a bin-size and a support s. We discuss the impact and the choice of these parameters. All results are presented in such a way that computer implementation, e.g. in R, is straightforward. Simulation studies confirm the effectiveness of our estimation procedure. In the second part of the paper, we present a data example where the method is applied to bivariate event-streams in financial limit-order-book data. We fit a bivariate Hawkes model on the joint process of limit and market order arrivals. The analysis exhibits a remarkably asymmetric relation between the two component processes: incoming market orders excite the limit-order flow heavily whereas the market-order flow is hardly affected by incoming limit orders. For the estimated excitement functions, we observe power-law shapes, inhibitory effects for lags under 0.003 s, second periodicities and local maxima at 0.01, 0.1 and 0.5 s.

Subfields: Applied Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2f36a6fba50cb3c22a2b6f944ebe1680015fa47f

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In Applied Mathematics

  1. Alan G. Hawkes
  2. Emmanuel Bacry
  3. Jean-François Muzy
  4. Mathieu Rosenbaum
  5. Thibault Jaisson
  6. Iacopo Mastromatteo
  7. Fabrizio Lillo
  8. Omar El Euch

In Molecular Biology

  1. Emmanuel Bacry
  2. Jean-François Muzy
  3. Alan G. Hawkes
  4. Mathieu Rosenbaum
  5. Thibault Jaisson
  6. Omar El Euch

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