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Fractional Moments of Solutions to Stochastic Recurrence Equations

Published online by Cambridge University Press:  30 January 2018

Thomas Mikosch*
Affiliation:
University of Copenhagen
Gennady Samorodnitsky*
Affiliation:
Cornell University
Laleh Tafakori*
Affiliation:
Shiraz University
*
Postal address: Department of Mathematics, University of Copenhagen, Universitetsparken 5, DK-2100 Copenhagen, Denmark. Email address: [email protected]
∗∗ Postal address: School of Operations Research and Industrial Engineering, Cornell University, 220 Rhodes Hall, Ithaca, NY 14853, USA. Email address: [email protected]
∗∗∗ Postal address: Department of Statistics, Shiraz University, College of Sciences, Shiraz, 7146713565, Iran. Email address: [email protected]
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Abstract

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In this paper we study the fractional moments of the stationary solution to the stochastic recurrence equation Xt = AtXt−1 + Bt, tZ, where ((At, Bt))tZ is an independent and identically distributed bivariate sequence. We derive recursive formulae for the fractional moments E|X0|p, pR. Special attention is given to the case when Bt has an Erlang distribution. We provide various approximations to the moments E|X0|p and show their performance in a small numerical study.

Type
Research Article
Copyright
© Applied Probability Trust 

Footnotes

Research partly supported by the Danish Natural Science Research Council (FNU) grant 10-084172 ‘Heavy tail phenomena: Modeling and estimation’.

Research partially supported by the ARO grant W911NF-07-1-0078, NSF grant DMS-1005903, and NSA grant H98230-11-1-0154 at Cornell University.

This paper was written when Laleh Tafakori visited the Department of Mathematics at the University of Copenhagen in 2011. She takes pleasure to thank the host institution for its hospitality.

References

Alsmeyer, G., Iksanov, A. and Rösler, U. (2009). On distributional properties of perpetuities. J. Theoret. Prob. 22, 666682.Google Scholar
Andrews, G. E., Askey, R. and Roy, R. (1999). Special Functions. Cambridge University Press.Google Scholar
Basrak, B., Davis, R. A. and Mikosch, T. (2002). A characterization of multivariate regular variation. Ann. Appl. Prob. 12, 908920.Google Scholar
Basrak, B., Davis, R. A. and Mikosch, T. (2002). Regular variation of GARCH processes. Stoch. Process. Appl. 99, 95115.Google Scholar
Behme, A., Lindner, A. and Maller, R. (2011). Stationary solutions of the stochastic differential equation dV t =V t -dU t dL with Lévy noise. Stoch. Process. Appl. 121, 91108.Google Scholar
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. J. Econometrics 31, 307327.Google Scholar
Boxma, O., Kella, O. and Perry, D. (2011). On some tractable growth-collapse processes with renewal collapse epochs. In New Frontiers in Applied Probability (J. Appl. Prob. Spec. Vol. 48A), eds Glynn, P., Mikosch, T. and Rolski, T., Applied Probability Trust, Sheffield, pp. 217234.Google Scholar
Breiman, L. (1965). On some limit theorems similar to the arc-sin law. Theory Prob. Appl. 10, 323331.Google Scholar
Brockwell, P. J. and Lindner, A. (2009). Existence and uniqueness of stationary Lévy-driven CARMA processes. Stoch. Process. Appl. 119, 26602681.Google Scholar
Carmona, P., Petit, F. and Yor, M. (1997). On the distribution and asymptotic results for exponential Functionals of Lévy processes. In Exponential Functionals and Principle Values Related to Brownian motion, ed. Yor, M., Revista Matematica Iberoamericana, Madrid, pp. 73130.Google Scholar
Collamore, J. F. (2009). Random recurrence equations and ruin in a Markov-dependent stochastic economic environment. Ann. Appl. Prob. 19, 14041458.Google Scholar
Diaconis, P. and Freedman, D. (1999). Iterated random functions. SIAM Rev. 41, 4576.Google Scholar
Dufresne, D. (1990). The distribution of a perpetuity, with applications to risk theory and pension funding. Scand. Actuarial J. 1990, 3979.Google Scholar
Dufresne, D. (1996). On the stochastic equation L(X)=LB(X+C)] and a property of gamma distributions. Bernoulli 2, 287291.Google Scholar
Dufresne, D. (1998). Algebraic properties of beta and gamma distributions, and applications. Adv. Appl. Math. 20, 285299.Google Scholar
Dufresne, D. (2010). G distributions and the beta-gamma algebra. Electron. J. Prob. 15, 21632199.CrossRefGoogle Scholar
Dumas, V., Guillemin, F. and Robert, P. (2002). A Markovian analysis of additive-increase multiplicative-decrease algorithms. Adv. Appl. Prob. 34, 85111.Google Scholar
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50, 9871007.Google Scholar
Enriquez, N., Sabot, C. and Zindy, O. (2009). A probabilistic representation of constants in Kesten's renewal theorem. Prob. Theory Relat. Fields 144, 581613.Google Scholar
Gjessing, H. K. and Paulsen, J. (1997). Present value distributions with applications to ruin theory and stochastic equations. Stoch. Process. Appl. 71, 123144.Google Scholar
Goldie, C. M. (1991). Implicit renewal theory and tails of solutions of random equations. Ann. Appl. Prob. 1, 126166.Google Scholar
Goldie, C. M. and Grübel, R. (1996). Perpetuities with thin tails. Adv. Appl. Prob. 28, 463480.Google Scholar
Guillemin, F., Robert, P. and Zwart, B. (2004). AIMD algorithms and exponential functionals. Ann. Appl. Prob. 14, 90117.Google Scholar
Hirsch, F. and Yor, M. (2013). On the Mellin transforms of the perpetuity and the remainder variables associated to a subordinator. Bernoulli 19, 13501377.Google Scholar
Kesten, H. (1973). Random difference equations and renewal theory for products of random matrices. Acta Math. 131, 207248.Google Scholar
Klebanov, L. B., Maniya, G. M. and Melamed, I. A. (1985). A problem of Zolotarev and analogs of infinitely divisible and stable distributions in a scheme for summing a random number of random variables. Theory Prob. Appl. 29, 791794.Google Scholar
Kozubowski, T. J. (2000). Exponential mixture representation of geometric stable distributions. Ann. Inst. Statist. Math. 52, 231238.Google Scholar
Löpker, A. H. and van Leeuwaarden, J. S. H. (2008). Transient moments of the TCP window size process. J. Appl. Prob. 45, 163175.Google Scholar
Maulik, K. and Zwart, B. (2006). Tail asymptotics for exponential functionals of Lévy processes. Stoch. Process. Appl. 116, 156177.Google Scholar
Pitman, J. and Yor, M. (2003). Infinitely divisible laws associated with hyperbolic functions. Canad. J. Math. 55, 292330.Google Scholar
Rachev, S. T. (1991). Probability Metrics and the Stability of Stochastic Models. John Wiley, Chichester.Google Scholar
Samorodnitsky, G. and Taqqu, M. (1994). Stable Non-Gaussian Random Processes. Chapman & Hall, New York.Google Scholar
Vervaat, W. (1979). On a stochastic difference equation and a representation of nonnegative infinitely divisible random variables. Adv. Appl. Prob. 11, 750783.Google Scholar
Zolotarev, V. M. (1986). One-Dimensional Stable Distributions (Trans. Math. Monogr. 65). American Mathematical Society, Providence, RI.Google Scholar