Robustness and sensitivity analyses for rough Volterra stochastic volatility models

TL;DR

This paper evaluates the robustness and sensitivity of various rough Volterra stochastic volatility models, including empirical tests on real market data, to understand their stability and calibration reliability.

Contribution

It introduces a comprehensive sensitivity analysis framework for rough Volterra models and compares their robustness to classical models using real market data.

Findings

RFSV, rBergomi, and αRFSV models show different sensitivity profiles.

Empirical results highlight the calibration stability of certain rough models.

Classical models like Heston and Bates exhibit distinct sensitivity characteristics.

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.