Multivariate Stochastic Volatility Models and Large Deviation Principles

Archil Gulisashvili

Papers from arXiv.org

Abstract: We establish a comprehensive sample path large deviation principle (LDP) for log-processes associated with multivariate time-inhomogeneous stochastic volatility models. Examples of models for which the new LDP holds include Gaussian models, non-Gaussian fractional models, mixed models, models with reflection, and models in which the volatility process is a solution to a Volterra type stochastic integral equation. The LDP for log-processes is used to obtain large deviation style asymptotic formulas for the distribution function of the first exit time of a log-process from an open set and for the price of a multidimensional binary barrier option. We also prove a sample path LDP for solutions to Volterra type stochastic integral equations with predictable coefficients depending on auxiliary stochastic processes.

Date: 2022-03, Revised 2022-11
New Economics Papers: this item is included in nep-cwa, nep-ets and nep-ore
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