  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.
