Rare event simulation for processes generated via stochastic fixed point equations
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
Rare event simulation for processes generated via stochastic fixed point equations. / Collamore, Jeffrey F.; Diao, Guoqing; Vidyashankar, Anand N.
I: Annals of Applied Probability, Bind 24, Nr. 5, 2014, s. 2143-2175.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Rare event simulation for processes generated via stochastic fixed point equations
AU - Collamore, Jeffrey F.
AU - Diao, Guoqing
AU - Vidyashankar, Anand N.
PY - 2014
Y1 - 2014
N2 - In a number of applications, particularly in financial and actuarial mathematics, it is of interest to characterize the tail distribution of a random variable V satisfying the distributional equation V=_D f(V), for some random function f. This paper is concerned with computational methods for evaluating these tail probabilities. We introduce a novel importance sampling algorithm, involving an exponential shift over a random time interval, for estimating these rare event probabilities. We prove that the proposed estimator is: (i) consistent, (ii) strongly efficient and (iii) optimal within a wide class of dynamic importance sampling estimators. Moreover, using extensions of ideas from nonlinear renewal theory, we provide a precise description of the running time of the algorithm. To establish these results, we develop new techniques concerning the convergence of moments of stopped perpetuity sequences, and the first entrance and least exit time of associated Markov chains on R. We illustrate our methods with a variety of numerical examples which demonstrate the ease and scope of the implementation.
AB - In a number of applications, particularly in financial and actuarial mathematics, it is of interest to characterize the tail distribution of a random variable V satisfying the distributional equation V=_D f(V), for some random function f. This paper is concerned with computational methods for evaluating these tail probabilities. We introduce a novel importance sampling algorithm, involving an exponential shift over a random time interval, for estimating these rare event probabilities. We prove that the proposed estimator is: (i) consistent, (ii) strongly efficient and (iii) optimal within a wide class of dynamic importance sampling estimators. Moreover, using extensions of ideas from nonlinear renewal theory, we provide a precise description of the running time of the algorithm. To establish these results, we develop new techniques concerning the convergence of moments of stopped perpetuity sequences, and the first entrance and least exit time of associated Markov chains on R. We illustrate our methods with a variety of numerical examples which demonstrate the ease and scope of the implementation.
U2 - 10.1214/13-AAP974
DO - 10.1214/13-AAP974
M3 - Journal article
VL - 24
SP - 2143
EP - 2175
JO - Annals of Applied Probability
JF - Annals of Applied Probability
SN - 1050-5164
IS - 5
ER -
ID: 118284151