Local likelihood estimation of time-variant Hawkes models

Boris I. Godoy; Victor Solo; Jason Min; Syed Ahmed Pasha

ICASSP 2016accepted0 citations

Local likelihood estimation of time-variant Hawkes models

Boris I. Godoy, Victor Solo, Jason Min, Syed Ahmed Pasha

Abstract

The Hawkes process is the workhorse of dynamic point process modelling - the point process version of the autoregression. It has been applied, for example, in high frequency finance, electricity price spike modelling and gene regulatory network modelling. But, in all these and other applications, it is assumed the parameters are time invariant. However, it is becoming clear that in many applications the parameters vary with time. Here, we develop for the first time, a very simple local likelihood approach to estimation of time-variant Hawkes processes. The new algorithm is tested on simulations and then applied to data from the Australian electricity market.

BibTeX

@inproceedings{icassp2016_locallikelihoode,
  title = {Local likelihood estimation of time-variant Hawkes models},
  author = {Boris I. Godoy and Victor Solo and Jason Min and Syed Ahmed Pasha},
  booktitle = {ICASSP 2016},
  year = {2016}
}