In this paper, we analyze the robustness and sensitivity of various continuous-time rough Volterra stochastic volatility models in relation to the process of market calibration. Model robustness is examined from two perspectives: the sensitivity of option price estimates and the sensitivity of parameter estimates to changes in the option data structure. The following sensitivity analysis consists of statistical tests to determine whether a given studied model is sensitive to changes in the option data structure based on the distribution of parameter estimates. Empirical study is performed on a data set consisting of Apple Inc. equity options traded on four different days in April and May 2015. In particular, the results for RFSV, rBergomi and $\alpha$RFSV models are provided and compared to the results for Heston, Bates, and AFSVJD models.

翻译：本文分析了多种连续时间粗糙Volterra随机波动率模型在市场校准过程中的鲁棒性与敏感性。模型鲁棒性从两个视角进行检验：期权价格估计对数据变动的敏感性，以及参数估计对期权数据结构变化的敏感性。后续的敏感性分析包含一系列统计检验，旨在基于参数估计的分布特征判断特定研究模型是否对期权数据结构变化敏感。实证研究采用的数据集包含苹果公司在2015年4月至5月四个交易日交易的股票期权。特别地，本文提供了RFSV、rBergomi和$\alpha$RFSV模型的结果，并与Heston、Bates和AFSVJD模型的结果进行了比较。