Efficient rare-event simulation for perpetuities

We consider perpetuities of the form D=B1exp(Y1)+B2exp(Y1+Y2)+⋯, where the Yj’s and Bj’s might be i.i.d. or jointly driven by a suitable Markov chain. We assume that the Yj’s satisfy the so-called Cramer condition with associated root θ∗∈(0,∞) and that the tails of the Bj’s are appropriately behaved so that D is regularly varying with index θ∗. We illustrate by means of an example that the natural state-independent importance sampling estimator obtained by exponentially tilting the Yj’s according to θ∗ fails to provide an efficient estimator (in the sense of appropriately controlling the relative mean squared error as the tail probability of interest gets smaller). Then, we construct estimators based on state-dependent importance sampling that are rigorously shown to be efficient.

[1]  Paul Dupuis,et al.  Subsolutions of an Isaacs Equation and Efficient Schemes for Importance Sampling , 2005, Math. Oper. Res..

[2]  C. Goldie IMPLICIT RENEWAL THEORY AND TAILS OF SOLUTIONS OF RANDOM EQUATIONS , 1991 .

[3]  C. Klüppelberg,et al.  Modelling Extremal Events , 1997 .

[4]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[5]  JEFFREY F. COLLAMORE Random recurrence equations and ruin in a Markov-dependent stochastic economic environment , 2009 .

[6]  A probabilistic representation of constants in Kesten’s renewal theorem , 2007, math/0703648.

[7]  J. Mount Importance Sampling , 2005 .

[8]  Persi Diaconis,et al.  Iterated Random Functions , 1999, SIAM Rev..

[9]  Jeffrey F. Collamore,et al.  Rare event simulation for processes generated via stochastic fixed point equations , 2011, 1107.3284.

[10]  Peter W. Glynn,et al.  Fluid heuristics, Lyapunov bounds and efficient importance sampling for a heavy-tailed G/G/1 queue , 2007, Queueing Syst. Theory Appl..

[11]  D. Siegmund,et al.  A diffusion process and its applications to detecting a change in the drift of Brownian motion , 1984 .

[12]  Søren Asmussen,et al.  Ruin probabilities , 2001, Advanced series on statistical science and applied probability.

[13]  Jose H. Blanchet,et al.  Efficient Monte Carlo for high excursions of Gaussian random fields , 2010, 1005.0812.

[14]  S. Asmussen,et al.  Ruin probabilities via local adjustment coefficients , 1995 .

[15]  Peter W. Glynn,et al.  Stochastic Simulation: Algorithms and Analysis , 2007 .

[16]  P. Glynn,et al.  Efficient rare-event simulation for the maximum of heavy-tailed random walks , 2008, 0808.2731.

[17]  Sلأren Asmussen,et al.  Applied Probability and Queues , 1989 .

[18]  Søren Asmussen,et al.  Applied probability and queues, Second Edition , 2003, Applications of mathematics.

[19]  Jürgen Potthoff,et al.  Fast simulation of Gaussian random fields , 2011, Monte Carlo Methods Appl..

[20]  H. Nyrhinen,et al.  Finite and infinite time ruin probabilities in a stochastic economic environment , 2001 .

[21]  Tim Hesterberg,et al.  Monte Carlo Strategies in Scientific Computing , 2002, Technometrics.

[22]  J. Corcoran Modelling Extremal Events for Insurance and Finance , 2002 .

[23]  J. Blanchet,et al.  On exact sampling of stochastic perpetuities , 2011, Journal of Applied Probability.

[24]  Benoite de Saporta,et al.  Tail of the stationary solution of the stochastic equation Y(n+1)=a(n)Y(n)+b(n) with Markovian coefficients , 2002 .

[25]  T. Mikosch,et al.  Limit theory for the sample autocorrelations and extremes of a GARCH (1,1) process , 2000 .

[26]  P. Dupuis,et al.  Importance Sampling, Large Deviations, and Differential Games , 2004 .

[27]  Jeffrey F. Collamore Importance Sampling Techniques for the Multidimensional Ruin Problem for General Markov Additive Sequences of Random Vectors , 2002 .

[28]  Benoîte de Saporta,et al.  Tail of the stationary solution of the stochastic equation Yn+1=anYn+bn with Markovian coefficients , 2004 .

[29]  D. Dufresne The Distribution of a Perpetuity, with Applications to Risk Theory and Pension Funding , 1990 .

[30]  H. Kesten Random difference equations and Renewal theory for products of random matrices , 1973 .

[31]  Bert Zwart,et al.  Importance sampling of compounding processes , 2007, 2007 Winter Simulation Conference.

[32]  Jun S. Liu,et al.  Monte Carlo strategies in scientific computing , 2001 .

[33]  Paul Glasserman,et al.  Analysis of an importance sampling estimator for tandem queues , 1995, TOMC.