Large and moderate deviations for importance sampling in the Heston model

被引：0

作者：

Geha, Marc ^{[1
]}

Jacquier, Antoine ^{[2
,3
]}

Zuric, Zan ^{[2
]}

机构：

[1] Princeton Univ, ORFE Dept, Princeton, NJ USA

[2] Imperial Coll London, Dept Math, London, England

[3] Alan Turing Inst, London, England

来源：

ANNALS OF OPERATIONS RESEARCH | 2024年 / 336卷 / 1-2期

基金：

英国工程与自然科学研究理事会;

关键词：

Heston; Volatility; Importance sampling; Large deviations; Moderate deviations; STOCHASTIC VOLATILITY; OPTIMIZATION; PRINCIPLE; FORMULAS; DUALITY;

D O I：

10.1007/s10479-023-05424-0

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

We provide a detailed importance sampling analysis for variance reduction in stochastic volatility models. The optimal change of measure is obtained using a variety of results from large and moderate deviations: small-time, large-time, small-noise. Specialising the results to the Heston model, we derive many closed-form solutions, making the whole approach easy to implement. We support our theoretical results with a detailed numerical analysis of the variance reduction gains.

引用

页码：47 / 92

页数：46

共 42 条

[1]

[Anonymous], 2004, APPL MATH

[2] General Freidlin-Wentzell Large Deviations and positive diffusions [J].