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Robustness and sensitivity analyses for stochastic volatility models under uncertain data structure

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  • Jan Posp'iv{s}il
  • Tom'av{s} Sobotka
  • Philipp Ziegler

Abstract

In this paper we perform robustness and sensitivity analysis of several continuous-time stochastic volatility (SV) models with respect to the process of market calibration. The analyses should validate the hypothesis on importance of the jump part in the underlying model dynamics. Also an impact of the long memory parameter is measured for the approximative fractional SV model. For the first time, the robustness of calibrated models is measured using bootstrapping methods on market data and Monte-Carlo filtering techniques. In contrast to several other sensitivity analysis approaches for SV models, the newly proposed methodology does not require independence of calibrated parameters - an assumption that is typically not satisfied in practice. Empirical study is performed on data sets of Apple Inc. equity options traded in April and May 2015.

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  • Jan Posp'iv{s}il & Tom'av{s} Sobotka & Philipp Ziegler, 2019. "Robustness and sensitivity analyses for stochastic volatility models under uncertain data structure," Papers 1912.06709, arXiv.org.
    • Handle: RePEc:arx:papers:1912.06709
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    References listed on IDEAS

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    Cited by:

    1. Jan Matas & Jan Posp'iv{s}il, 2021. "Robustness and sensitivity analyses for rough Volterra stochastic volatility models," Papers 2107.12462, arXiv.org, revised Jun 2023.

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