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Importance sampling of heavy-tailed iterated random functions

Part of: Probabilistic methods, simulation and stochastic differential equations Markov processes

Published online by Cambridge University Press:  16 November 2018

Bohan Chen ,
Chang-Han Rhee  and
Bert Zwart
Bohan Chen*
Affiliation:
Centrum Wiskunde & Informatica
Chang-Han Rhee*
Affiliation:
Centrum Wiskunde & Informatica
Bert Zwart*
Affiliation:
Centrum Wiskunde & Informatica
*
* Postal address: Stochastics Group, Centrum Wiskunde & Informatica, Science Park 123, 1098 XG, Amsterdam, The Netherlands.
*** Current address: Industrial Engineering and Management Sciences, 2145 Sheridan Road, Tech C150, Evanston, Illinois, IL 60208-3109, USA. Email address: [email protected]
* Postal address: Stochastics Group, Centrum Wiskunde & Informatica, Science Park 123, 1098 XG, Amsterdam, The Netherlands.
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Abstract

We consider the stationary solution Z of the Markov chain {Zn}n∈ℕ defined by Zn+1=ψn+1(Zn), where {ψn}n∈ℕ is a sequence of independent and identically distributed random Lipschitz functions. We estimate the probability of the event {Z>x} when x is large, and develop a state-dependent importance sampling estimator under a set of assumptions on ψn such that, for large x, the event {Z>x} is governed by a single large jump. Under natural conditions, we show that our estimator is strongly efficient. Special attention is paid to a class of perpetuities with heavy tails.

Keywords

State-dependent importance samplingheavy-tailed distributioniterated random functionperpetuities

MSC classification

Primary: 65C05: Monte Carlo methods
Secondary: 60J05: Discrete-time Markov processes on general state spaces
Type
Original Article
Information
Advances in Applied Probability , Volume 50 , Issue 3 , September 2018 , pp. 805 - 832
Copyright
Copyright © Applied Probability Trust 2018 

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