A MARTINGALE APPROACH FOR FRACTIONAL BROWNIAN MOTIONS AND RELATED PATH DEPENDENT PDES

• 成果类型: Article
• 署名作者: Viens, Frederi; Zhang, Jianfeng
• 署名单位: Michigan State University; University of Southern California
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/19-AAP1486
• 发表日期: 2019
• 页码: 3489-3540
• 关键词: viscosity solutions; stochastic volatility; long-memory; EQUATIONS; calculus

摘要

In this paper, we study dynamic backward problems, with the computation of conditional expectations as a special objective, in a framework where the (forward) state process satisfies a Volterra type SDE, with fractional Brownian motion as a typical example. Such processes are neither Markov processes nor semimartingales, and most notably, they feature a certain time inconsistency which makes any direct application of Markovian ideas, such as flow properties, impossible without passing to a path-dependent framework. Our main result is a functional Ito formula, extending the seminal work of Dupire (Quant. Finance 19 (2019) 721-729) to our more general framework. In particular, unlike in (Quant. Finance 19 (2019) 721-729) where one needs only to consider the stopped paths, here we need to concatenate the observed path up to the current time with a certain smooth observable curve derived from the distribution of the future paths. This new feature is due to the time inconsistency involved in this paper. We then derive the path dependent PDEs for the backward problems. Finally, an application to option pricing and hedging in a financial market with rough volatility is presented.