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arXiv 2203.09015math.PRq-fin.MF
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多元随机波动模型与大偏差原理

Multivariate Stochastic Volatility Models and Large Deviation Principles

Archil Gulisashvili

摘要:

建立了多元非齐次对数过程随机波动率模型的样本路径大偏差原理。新LDP适用的模型的例子包括高斯模型、非高斯分数模型、混合模型、具有反射的模型、以及波动过程是Volterra型随机积分方程的解的模型。利用对数过程的LDP得到了对数过程首次离开开集时间的分布函数和多维二元障碍期权价格的大偏差式渐近公式.我们还证明了一个样本路径LDP的解决方案的Volterra型随机积分方程的可预测系数取决于辅助随机过程。

英文摘要:

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.

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