Robustness and sensitivity analyses for rough Volterra stochastic volatility models

Jan Matas and Jan Posp\'i\v{s}il

Papers from arXiv.org

Abstract: 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.

Date: 2021-07, Revised 2023-06
New Economics Papers: this item is included in nep-isf and nep-ore
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