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Infinite-server queues with Hawkes input

Published online by Cambridge University Press:  16 November 2018

D. T. Koops*
Affiliation:
University of Amsterdam
M. Saxena*
Affiliation:
Eindhoven University of Technology
O. J. Boxma*
Affiliation:
Eindhoven University of Technology
M. Mandjes*
Affiliation:
University of Amsterdam
*
* Postal address: Korteweg-de Vries Institute, University of Amsterdam, PO Box 94248, 1090GE, Amsterdam, The Netherlands.
*** Postal address: Eurandom and Department of Mathematics and Computer Science, Eindhoven University of Technology, PO Box 513, 5600MB, Eindhoven, The Netherlands.
*** Postal address: Eurandom and Department of Mathematics and Computer Science, Eindhoven University of Technology, PO Box 513, 5600MB, Eindhoven, The Netherlands.
* Postal address: Korteweg-de Vries Institute, University of Amsterdam, PO Box 94248, 1090GE, Amsterdam, The Netherlands.

Abstract

In this paper we study the number of customers in infinite-server queues with a self-exciting (Hawkes) arrival process. Initially we assume that service requirements are exponentially distributed and that the Hawkes arrival process is of a Markovian nature. We obtain a system of differential equations that characterizes the joint distribution of the arrival intensity and the number of customers. Moreover, we provide a recursive procedure that explicitly identifies (transient and stationary) moments. Subsequently, we allow for non-Markovian Hawkes arrival processes and nonexponential service times. By viewing the Hawkes process as a branching process, we find that the probability generating function of the number of customers in the system can be expressed in terms of the solution of a fixed-point equation. We also include various asymptotic results: we derive the tail of the distribution of the number of customers for the case that the intensity jumps of the Hawkes process are heavy tailed, and we consider a heavy-traffic regime. We conclude by discussing how our results can be used computationally and by verifying the numerical results via simulations.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 2018 

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References

[1]Abate, J. and Whitt, W. (1992). Numerical inversion of probability generating functions. Operat. Res. Lett. 12, 245251.Google Scholar
[2]Abate, J. and Whitt, W. (1992). Solving probability transform functional equations for numerical inversion. Operat. Res. Lett. 12, 275281.Google Scholar
[3]Bacry, E., Mastromatteo, I. and Muzy, J.-F. (2015). Hawkes processes in finance. Market Microstructure Liquidity 1, 1550005.Google Scholar
[4]Bingham, N. H., Goldie, C. M. and Teugels, J. L. (1989). Regular Variation. Cambridge University Press.Google Scholar
[5]Blanchet, J., Chen, X. and Pei, Y. (2017). Unraveling limit order books using just bid/ask prices. Preprint. Available at http://www.columbia.edu/~jb2814/papers/LOB_v1.pdf.Google Scholar
[6]Bogachev, V. I. (2007). Measure Theory, I. Springer, Berlin.Google Scholar
[7]Cont, R. and De Larrard, A. (2012). Order book dynamics in liquid markets: limit theorems and diffusion approximations. Preprint. Available at https://arxiv.org/abs/1202.6412.Google Scholar
[8]Dassios, A. and Zhao, H. (2011). A dynamic contagion process. Adv. Appl. Prob. 43, 814846.Google Scholar
[9]Daw, A. and Pender, J. (2018). Queues driven by Hawkes processes. Stoch. Systems 8, 192229.Google Scholar
[10]Gao, X. and Zhu, L. (2018). Functional central limit theorems for stationary Hawkes processes and application to infinite-server queues. Queueing Systems 90, 161206.Google Scholar
[11]Ghufran, S. M. (2010). The computation of matrix functions in particular, the matrix exponential. Masters Thesis, University of Birmingham.Google Scholar
[12]Harris, T. E. (1963). The Theory of Branching Processes. Springer, Berlin.Google Scholar
[13]Hawkes, A. G. (1971). Point spectra of some mutually exciting point processes. J. R. Statist. Soc. B 33, 438443.Google Scholar
[14]Hawkes, A. G. (1971). Spectra of some self-exciting and mutually exciting point processes. Biometrika 58, 8390.Google Scholar
[15]Hawkes, A. G. and Oakes, D. (1974). A cluster process representation of a self-exciting process. J. Appl. Prob. 11, 493503.Google Scholar
[16]Heemskerk, M., van Leeuwaarden, J. and Mandjes, M. (2017). Scaling limits for infinite-server systems in a random environment. Stoch. Systems 7, 131.Google Scholar
[17]Jongbloed, G. and Koole, G. (2001). Managing uncertainty in call centres using Poisson mixtures. Appl. Stoch. Models Business Industry 17, 307318.Google Scholar
[18]Kirchner, M. (2017). An estimation procedure for the Hawkes process. Quant. Finance 17, 571595.Google Scholar
[19]Koops, D. T., Boxma, O. J. and Mandjes, M. R. H. (2017). Networks of ·/G/∞ queues with shot-noise-driven arrival intensities. Queueing Systems 86, 301325.Google Scholar
[20]Laub, P. J., Taimre, T. and Pollett, P. K. (2015). Hawkes processes. Preprint. Available at https://arxiv.org/abs/1507.02822.Google Scholar
[21]Lewis, P. A. W. and Shedler, G. S. (1979). Simulation of nonhomogenous Poisson processes by thinning. Naval Res. Logistics Quart. 26, 403413.Google Scholar
[22]Mathijsen, B. W. J., Janssen, A. J. E. M., van Leeuwaarden, J. S. H. and Zwart, B. (2018). Robust heavy-traffic approximations for service systems facing overdispersed demand. Queueing Systems 90, 257289.Google Scholar
[23]Ogata, Y. (1978). The asymptotic behaviour of maximum likelihood estimators for stationary point processes. Ann. Inst. Statist. Math. 30, 243261.Google Scholar
[24]Ogata, Y. (1981). On Lewis' simulation method for point processes. IEEE Trans. Inf. Theory 27, 2331.Google Scholar
[25]Ogata, Y. and Akaike, H. (1982). On linear intensity models for mixed doubly stochastic Poisson and self-exciting point processes. J. R. Statist. Soc. B 44, 102107.Google Scholar
[26]Rizoiu, M.-A. and Xie, L. (2017). Online popularity under promotion: viral potential, forecasting, and the economics of time. Preprint. Available at https://arxiv.org/abs/1703.01012.Google Scholar
[27]Rizoiu, M.-A. et al. (2017). Expecting to be HIP: Hawkes intensity processes for social media popularity. Preprint. Available at https://arxiv.org/abs/1602.06033.Google Scholar
[28]Toke, I. M. and Pomponio, F. (2012). Modelling trades-through in a limit order book using Hawkes processes. Economics Open-Access Open-Assessment E-Journal 6, 2012–22.Google Scholar
[29]Tricomi, F. G. (1957). Integral Equations. Interscience, New York.Google Scholar
[30]Van der Vaart, A. W. (1998). Asymptotic Statistics. Cambridge University Press.Google Scholar
[31]Williams, D. (1991). Probability with Martingales. Cambridge University Press.Google Scholar