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Exact simulation of Hawkes process with exponentially decaying intensity

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  • Dassios, Angelos
  • Zhao, Hongbiao

Abstract

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.

Suggested Citation

  • Dassios, Angelos & Zhao, Hongbiao, 2013. "Exact simulation of Hawkes process with exponentially decaying intensity," LSE Research Online Documents on Economics 51370, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:51370
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    File URL: http://eprints.lse.ac.uk/51370/
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    References listed on IDEAS

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    1. Yacine Aït-Sahalia & Thomas Robert Hurd, 2016. "Portfolio Choice in Markets with Contagion," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 1-28.
    2. Bowsher, Clive G., 2007. "Modelling security market events in continuous time: Intensity based, multivariate point process models," Journal of Econometrics, Elsevier, vol. 141(2), pages 876-912, December.
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    Cited by:

    1. Maciak, Matúš & Okhrin, Ostap & Pešta, Michal, 2021. "Infinitely stochastic micro reserving," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 30-58.
    2. Cui, Zhenyu & Kirkby, J. Lars & Nguyen, Duy, 2021. "Efficient simulation of generalized SABR and stochastic local volatility models based on Markov chain approximations," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1046-1062.
    3. Dassios, Angelos & Zhao, Hongbiao, 2017. "Efficient simulation of clustering jumps with CIR intensity," LSE Research Online Documents on Economics 74205, London School of Economics and Political Science, LSE Library.
    4. Tomasz R. Bielecki & Jacek Jakubowski & Mariusz Niewęgłowski, 2022. "Construction and Simulation of Generalized Multivariate Hawkes Processes," Methodology and Computing in Applied Probability, Springer, vol. 24(4), pages 2865-2896, December.
    5. Dharmaraja Selvamuthu & Paola Tardelli, 2022. "Infinite-server systems with Hawkes arrivals and Hawkes services," Queueing Systems: Theory and Applications, Springer, vol. 101(3), pages 329-351, August.
    6. Patrick Chang & Etienne Pienaar & Tim Gebbie, 2020. "Using the Epps effect to detect discrete processes," Papers 2005.10568, arXiv.org, revised Oct 2021.
    7. Riccardo Brignone & Carlo Sgarra, 2020. "Asian options pricing in Hawkes-type jump-diffusion models," Annals of Finance, Springer, vol. 16(1), pages 101-119, March.
    8. Emmanuel Bacry & Iacopo Mastromatteo & Jean-Franc{c}ois Muzy, 2015. "Hawkes processes in finance," Papers 1502.04592, arXiv.org, revised May 2015.
    9. Angelos Dassios & Hongbiao Zhao, 2017. "Efficient Simulation of Clustering Jumps with CIR Intensity," Operations Research, INFORMS, vol. 65(6), pages 1494-1515, December.
    10. Li, Chenxu & Wu, Linjia, 2019. "Exact simulation of the Ornstein–Uhlenbeck driven stochastic volatility model," European Journal of Operational Research, Elsevier, vol. 275(2), pages 768-779.
    11. Cavaliere, Giuseppe & Lu, Ye & Rahbek, Anders & Stærk-Østergaard, Jacob, 2023. "Bootstrap inference for Hawkes and general point processes," Journal of Econometrics, Elsevier, vol. 235(1), pages 133-165.
    12. Gerrit Großmann & Luca Bortolussi & Verena Wolf, 2020. "Efficient simulation of non-Markovian dynamics on complex networks," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-18, October.
    13. Roger Martins & Dieter Hendricks, 2016. "The statistical significance of multivariate Hawkes processes fitted to limit order book data," Papers 1604.01824, arXiv.org, revised Apr 2016.
    14. Cui, Zhenyu & Lars Kirkby, J. & Nguyen, Duy, 2019. "A general framework for time-changed Markov processes and applications," European Journal of Operational Research, Elsevier, vol. 273(2), pages 785-800.
    15. Martin Magris, 2019. "On the simulation of the Hawkes process via Lambert-W functions," Papers 1907.09162, arXiv.org.
    16. Hainaut, Donatien, 2020. "Fractional Hawkes processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    17. Gonzato, Luca & Sgarra, Carlo, 2021. "Self-exciting jumps in the oil market: Bayesian estimation and dynamic hedging," Energy Economics, Elsevier, vol. 99(C).
    18. Liu, Guo & Jin, Zhuo & Li, Shuanming, 2021. "Optimal investment, consumption, and life insurance strategies under a mutual-exciting contagious market," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 508-524.
    19. Santitissadeekorn, Naratip & Lloyd, David J.B. & Short, Martin B. & Delahaies, Sylvain, 2020. "Approximate filtering of conditional intensity process for Poisson count data: Application to urban crime," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    20. Charlotte Dion & Sarah Lemler, 2020. "Nonparametric drift estimation for diffusions with jumps driven by a Hawkes process," Statistical Inference for Stochastic Processes, Springer, vol. 23(3), pages 489-515, October.
    21. Marius Pfeuffer & Goncalo dos Reis & Greig smith, 2018. "Capturing Model Risk and Rating Momentum in the Estimation of Probabilities of Default and Credit Rating Migrations," Papers 1809.09889, arXiv.org, revised Feb 2020.
    22. Buccioli, Alice & Kokholm, Thomas & Nicolosi, Marco, 2019. "Expected shortfall and portfolio management in contagious markets," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 100-115.

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    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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