An importance sampling algorithm for estimating extremes of perpetuity sequences

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Standard

An importance sampling algorithm for estimating extremes of perpetuity sequences. / Collamore, Jeffrey F.

AIP Conference Proceedings. Vol. 1479 American Institute of Physics, 2012. p. 1966-1969.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Collamore, JF 2012, An importance sampling algorithm for estimating extremes of perpetuity sequences. in AIP Conference Proceedings. vol. 1479, American Institute of Physics, pp. 1966-1969. https://doi.org/10.1063/1.4756571

APA

Collamore, J. F. (2012). An importance sampling algorithm for estimating extremes of perpetuity sequences. In AIP Conference Proceedings (Vol. 1479, pp. 1966-1969). American Institute of Physics. https://doi.org/10.1063/1.4756571

Vancouver

Collamore JF. An importance sampling algorithm for estimating extremes of perpetuity sequences. In AIP Conference Proceedings. Vol. 1479. American Institute of Physics. 2012. p. 1966-1969 https://doi.org/10.1063/1.4756571

Author

Collamore, Jeffrey F. / An importance sampling algorithm for estimating extremes of perpetuity sequences. AIP Conference Proceedings. Vol. 1479 American Institute of Physics, 2012. pp. 1966-1969

Bibtex

@inproceedings{35c967b47e4b4cf6b1b77f0486df5cd7,
title = "An importance sampling algorithm for estimating extremes of perpetuity sequences",
abstract = "In a wide class of problems in insurance and financial mathematics, it is of interest to study the extremal events of a perpetuity sequence.This paper addresses the problem of numerically evaluating these rare event probabilities. Specifically, an importance sampling algorithm is described whichis efficient in the sense that it exhibits bounded relative error, and which is optimal in an appropriate asymptotic sense. Themain idea of the algorithm is to use a ``dual{"}change of measure, which is employed to an associated Markov chain over a randomly-stopped time interval.The algorithm also makes use of the so-called forward sequences generated to the given stochastic recursion,together with elements of Markov chain theory. ",
author = "Collamore, {Jeffrey F.}",
note = "NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. - 19–25 September 2012, Kos, Greece",
year = "2012",
doi = "10.1063/1.4756571",
language = "English",
isbn = "8-0-7354-1091-6",
volume = "1479",
pages = "1966--1969",
booktitle = "AIP Conference Proceedings",
publisher = "American Institute of Physics",
address = "United States",

}

RIS

TY - GEN

T1 - An importance sampling algorithm for estimating extremes of perpetuity sequences

AU - Collamore, Jeffrey F.

N1 - NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. - 19–25 September 2012, Kos, Greece

PY - 2012

Y1 - 2012

N2 - In a wide class of problems in insurance and financial mathematics, it is of interest to study the extremal events of a perpetuity sequence.This paper addresses the problem of numerically evaluating these rare event probabilities. Specifically, an importance sampling algorithm is described whichis efficient in the sense that it exhibits bounded relative error, and which is optimal in an appropriate asymptotic sense. Themain idea of the algorithm is to use a ``dual"change of measure, which is employed to an associated Markov chain over a randomly-stopped time interval.The algorithm also makes use of the so-called forward sequences generated to the given stochastic recursion,together with elements of Markov chain theory.

AB - In a wide class of problems in insurance and financial mathematics, it is of interest to study the extremal events of a perpetuity sequence.This paper addresses the problem of numerically evaluating these rare event probabilities. Specifically, an importance sampling algorithm is described whichis efficient in the sense that it exhibits bounded relative error, and which is optimal in an appropriate asymptotic sense. Themain idea of the algorithm is to use a ``dual"change of measure, which is employed to an associated Markov chain over a randomly-stopped time interval.The algorithm also makes use of the so-called forward sequences generated to the given stochastic recursion,together with elements of Markov chain theory.

U2 - 10.1063/1.4756571

DO - 10.1063/1.4756571

M3 - Article in proceedings

SN - 8-0-7354-1091-6

VL - 1479

SP - 1966

EP - 1969

BT - AIP Conference Proceedings

PB - American Institute of Physics

ER -

ID: 45682569