非马尔可夫扩散的大偏差和路径相关的Eikonal方程

IF 1.2 2区 数学 Q2 STATISTICS & PROBABILITY Annales De L Institut Henri Poincare-probabilites Et Statistiques Pub Date : 2014-07-20 DOI:10.1214/15-AIHP678
Jingsheng Ma, Zhenjie Ren, N. Touzi, Jianfeng Zhang
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引用次数: 11

摘要

本文给出了非马尔可夫布朗运动驱动的随机系数随机微分方程的大偏差原理。与Gao & Liu[19]类似,这扩展了Freidlin & Wentzell[18]收集的相应结果。然而,我们使用了不同的观点,通过使用倒向随机微分技术,将Fleming[14]和Evans & Ishii[10]的PDE方法应用于路径依赖的情况。与马尔可夫情况类似,我们得到了作用函数作为路径依赖的Eikonal方程的唯一有界解的一个表征。最后,给出了隐含波动率曲面的短期渐近性在金融数学中的一个应用。
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Large deviations for non-Markovian diffusions and a path-dependent Eikonal equation
This paper provides a large deviation principle for Non-Markovian, Brownian motion driven stochastic differential equations with random coefficients. Similar to Gao & Liu [19], this extends the corresponding results collected in Freidlin & Wentzell [18]. However, we use a different line of argument, adapting the PDE method of Fleming [14] and Evans & Ishii [10] to the pathdependent case, by using backward stochastic differential techniques. Similar to the Markovian case, we obtain a characterization of the action function as the unique bounded solution of a path-dependent version of the Eikonal equation. Finally, we provide an application to the short maturity asymptotics of the implied volatility surface in financial mathematics.
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来源期刊
CiteScore
2.70
自引率
0.00%
发文量
85
审稿时长
6-12 weeks
期刊介绍: The Probability and Statistics section of the Annales de l’Institut Henri Poincaré is an international journal which publishes high quality research papers. The journal deals with all aspects of modern probability theory and mathematical statistics, as well as with their applications.
期刊最新文献
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