Exact Simulation of Hawkes Process with Exponentially Decaying Intensity

We introduce a numerically efficient simulation algorithm for Hawkes process with exponentially decaying intensity, a special case of general Hawkes process that is most widely implemented in practice. This computational method is able to exactly generate the point process and intensity process, by sampling interarrival-times directly via the underlying analytic distribution functions without numerical inverse, and hence avoids simulating intensity paths and introducing discretisation bias. Moreover, it is flexible to generate points with either stationary or non-stationary intensity, starting from any arbitrary time with any arbitrary initial intensity. It is also straightforward to implement, and can easily extend to multi-dimensional versions, for further applications in modelling contagion risk or clustering arrival of events in finance, insurance, economics and many other fields. Simulation algorithms for one dimension and multi-dimension are represented, with numerical examples of univariate and bivariate processes provided as illustrations.

[1]  A. Hawkes Spectra of some self-exciting and mutually exciting point processes , 1971 .

[2]  A. Hawkes,et al.  A cluster process representation of a self-exciting process , 1974, Journal of Applied Probability.

[3]  David Oakes,et al.  The Markovian self-exciting process , 1975, Journal of Applied Probability.

[4]  G. Shedler,et al.  Simulation of Nonhomogeneous Poisson Processes by Thinning , 1979 .

[5]  Yosihiko Ogata,et al.  On Lewis' simulation method for point processes , 1981, IEEE Trans. Inf. Theory.

[6]  Yosihiko Ogata,et al.  Statistical Models for Earthquake Occurrences and Residual Analysis for Point Processes , 1988 .

[7]  Trades and Quotes: A Bivariate Point Process , 1998 .

[8]  Kluwer Academic Publishers Methodology and computing in applied probability , 1999 .

[9]  Clive G. Bowsher Modelling Security Market Events in Continuous Time: Intensity Based, Multivariate Point Process Models , 2003 .

[10]  Wilfrid S. Kendall,et al.  DEPARTMENT OF STATISTICS UNIVERSITY OF WARWICK Simulation of cluster point processes without edge effects , 2002 .

[11]  Jeffrey D. Scargle,et al.  An Introduction to the Theory of Point Processes, Vol. I: Elementary Theory and Methods , 2004, Technometrics.

[12]  E. S. Chornoboy,et al.  Maximum likelihood identification of neural point process systems , 1988, Biological Cybernetics.

[13]  J. Rasmussen,et al.  Perfect simulation of Hawkes processes , 2005, Advances in Applied Probability.

[14]  Jesper Møller,et al.  Approximate Simulation of Hawkes Processes , 2006 .

[15]  Kay Giesecke,et al.  Estimating tranche spreads by loss process simulation , 2007, 2007 Winter Simulation Conference.

[16]  Thomas Josef Liniger,et al.  Multivariate Hawkes processes , 2009 .

[17]  Yacine Ait-Sahalia,et al.  Modeling Financial Contagion Using Mutually Exciting Jump Processes , 2010 .

[18]  Kay Giesecke,et al.  Affine Point Processes and Portfolio Credit Risk , 2010, SIAM J. Financial Math..

[19]  Shilin Zhu,et al.  Monte Carlo Algorithms for Default Timing Problems , 2011, Manag. Sci..

[20]  Angelos Dassios,et al.  Ruin by Dynamic Contagion Claims , 2011 .

[21]  P. Embrechts,et al.  Multivariate Hawkes processes: an application to financial data , 2011, Journal of Applied Probability.

[22]  A. Dassios,et al.  A dynamic contagion process , 2011, Advances in Applied Probability.

[23]  Yacine Ait-Sahalia,et al.  Portfolio Choice in Markets with Contagion , 2012, 1210.1598.

[24]  J. Rasmussen Bayesian Inference for Hawkes Processes , 2013 .