多因素Volterra型随机波动率模型的渐近性

IF 0.8 4区数学 Q3 MATHEMATICS, APPLIED

Stochastic Analysis and Applications Pub Date : 2021-09-20 DOI:10.1080/07362994.2022.2120012

Giulia Catalini, B. Pacchiarotti

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引用次数: 3

摘要

．我们研究了波动过程是连续多维Volterra过程的一个正连续函数的多维随机波动模型，该模型可以是不自相似的。本文得到的主要结果是对一维情况下Cellupica和Pacchiarotti [M。Cellupica和B. Pacchiarotti (2021) Volterra型随机波动模型的路径渐近性。理论概率论学报，34(2):682-727。我们陈述了尺度对数价格的一些(路径和有限维)大偏差原理，以及一些(路径和有限维)短时间大偏差原理。

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Asymptotics for multifactor Volterra type stochastic volatility models

. We study multidimensional stochastic volatility models in which the volatility process is a positive continuous function of a continuous multidimensional Volterra process that can be not self-similar. The main results obtained in this paper are a generalization of the results due, in the one-dimensional case, to Cellupica and Pacchiarotti [M. Cellupica and B. Pacchiarotti (2021) Pathwise Asymptotics for Volterra Type Stochastic Volatility Models. Journal of Theoretical Probability , 34(2):682–727]. We state some (pathwise and ﬁnite-dimensional) large deviation principles for the scaled log-price and as a consequence some (pathwise and ﬁnite-dimensional) short-time large deviation principles.

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来源期刊

Stochastic Analysis and Applications 数学-统计学与概率论

CiteScore

2.70

自引率

7.70%

发文量

审稿时长

6-12 weeks

期刊介绍： Stochastic Analysis and Applications presents the latest innovations in the field of stochastic theory and its practical applications, as well as the full range of related approaches to analyzing systems under random excitation. In addition, it is the only publication that offers the broad, detailed coverage necessary for the interfield and intrafield fertilization of new concepts and ideas, providing the scientific community with a unique and highly useful service.