Optimal importance sampling for simulation of Lévy processes

Guangxin Jiang; Michael C. Fu; Chenglong Xu

doi:10.1109/WSC.2015.7408538

Optimal importance sampling for simulation of Lévy processes

Guangxin Jiang
, Michael C. Fu
, Chenglong Xu

City University of Hong Kong
University of Maryland, College Park
Tongji University

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

2 Scopus citations

Abstract

This paper provides an efficient algorithm using Newton's method under sample average approximation (SAA) to solve the parametric optimization problem associated with the optimal importance sampling change of measure in simulating Lévy processes. Numerical experiments on variance gamma (VG), geometric Brownian motion (GBM), and normal inverse Gaussian (NIG) examples illustrate the computational advantages of the SAA-Newton algorithm over stochastic approximation (SA) based algorithms.

Original language	English
Title of host publication	2015 Winter Simulation Conference, WSC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3813-3824
Number of pages	12
ISBN (Electronic)	9781467397438
DOIs	https://doi.org/10.1109/WSC.2015.7408538
State	Published - 16 Feb 2016
Externally published	Yes
Event	Winter Simulation Conference, WSC 2015 - Huntington Beach, United States Duration: 6 Dec 2015 → 9 Dec 2015

Publication series

Name	Proceedings - Winter Simulation Conference
Volume	2016-February
ISSN (Print)	0891-7736

Conference

Conference	Winter Simulation Conference, WSC 2015
Country/Territory	United States
City	Huntington Beach
Period	6/12/15 → 9/12/15

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10.1109/WSC.2015.7408538

Cite this

@inproceedings{3bd8381f1e8246998f80c5c972a901e1,

title = "Optimal importance sampling for simulation of L{\'e}vy processes",

abstract = "This paper provides an efficient algorithm using Newton's method under sample average approximation (SAA) to solve the parametric optimization problem associated with the optimal importance sampling change of measure in simulating L{\'e}vy processes. Numerical experiments on variance gamma (VG), geometric Brownian motion (GBM), and normal inverse Gaussian (NIG) examples illustrate the computational advantages of the SAA-Newton algorithm over stochastic approximation (SA) based algorithms.",

author = "Guangxin Jiang and Fu, \{Michael C.\} and Chenglong Xu",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; Winter Simulation Conference, WSC 2015 ; Conference date: 06-12-2015 Through 09-12-2015",

year = "2016",

month = feb,

day = "16",

doi = "10.1109/WSC.2015.7408538",

language = "英语",

series = "Proceedings - Winter Simulation Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3813--3824",

booktitle = "2015 Winter Simulation Conference, WSC 2015",

address = "美国",

}

Jiang, G, Fu, MC & Xu, C 2016, Optimal importance sampling for simulation of Lévy processes. in 2015 Winter Simulation Conference, WSC 2015., 7408538, Proceedings - Winter Simulation Conference, vol. 2016-February, Institute of Electrical and Electronics Engineers Inc., pp. 3813-3824, Winter Simulation Conference, WSC 2015, Huntington Beach, United States, 6/12/15. https://doi.org/10.1109/WSC.2015.7408538

Optimal importance sampling for simulation of Lévy processes. / Jiang, Guangxin; Fu, Michael C.; Xu, Chenglong.
2015 Winter Simulation Conference, WSC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 3813-3824 7408538 (Proceedings - Winter Simulation Conference; Vol. 2016-February).

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

TY - GEN

T1 - Optimal importance sampling for simulation of Lévy processes

AU - Jiang, Guangxin

AU - Fu, Michael C.

AU - Xu, Chenglong

PY - 2016/2/16

Y1 - 2016/2/16

N2 - This paper provides an efficient algorithm using Newton's method under sample average approximation (SAA) to solve the parametric optimization problem associated with the optimal importance sampling change of measure in simulating Lévy processes. Numerical experiments on variance gamma (VG), geometric Brownian motion (GBM), and normal inverse Gaussian (NIG) examples illustrate the computational advantages of the SAA-Newton algorithm over stochastic approximation (SA) based algorithms.

AB - This paper provides an efficient algorithm using Newton's method under sample average approximation (SAA) to solve the parametric optimization problem associated with the optimal importance sampling change of measure in simulating Lévy processes. Numerical experiments on variance gamma (VG), geometric Brownian motion (GBM), and normal inverse Gaussian (NIG) examples illustrate the computational advantages of the SAA-Newton algorithm over stochastic approximation (SA) based algorithms.

UR - https://www.scopus.com/pages/publications/84962885868

U2 - 10.1109/WSC.2015.7408538

DO - 10.1109/WSC.2015.7408538

M3 - 会议稿件

AN - SCOPUS:84962885868

T3 - Proceedings - Winter Simulation Conference

SP - 3813

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BT - 2015 Winter Simulation Conference, WSC 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - Winter Simulation Conference, WSC 2015

Y2 - 6 December 2015 through 9 December 2015

ER -

Optimal importance sampling for simulation of Lévy processes

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