论文标题
时间不均匀的高斯随机波动率模型：较大的偏差和超粗糙度
Time-inhomogeneous Gaussian stochastic volatility models: Large deviations and super roughness
论文作者
论文摘要
我们介绍了时间不均匀的随机波动率模型，其中挥发性通过伏尔泰拉型连续高斯过程的非负功能描述，该过程可能具有非常粗糙的样品路径。本文在本文中获得的主要结果是在非常轻微的限制下，在时间固定的超级粗糙高斯模型中，对数价格过程的样本路径和小噪声偏差原理。我们使用这些结果来研究二元屏障选项的渐近行为，退出时间概率功能和呼叫选项。
We introduce time-inhomogeneous stochastic volatility models, in which the volatility is described by a nonnegative function of a Volterra type continuous Gaussian process that may have very rough sample paths. The main results obtained in the paper are sample path and small-noise large deviation principles for the log-price process in a time-inhomogeneous super rough Gaussian model under very mild restrictions. We use these results to study the asymptotic behavior of binary barrier options, exit time probability functions, and call options.
