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

Author

Listed:
  • Jan Pospíšil

    (University of West Bohemia)

  • Tomáš Sobotka

    (University of West Bohemia)

  • Philipp Ziegler

    (University of Rostock)

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 (FSV). 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 a data set of Apple Inc. equity options traded in four different days in April and May 2015. In particular, the results for Heston, Bates and approximative FSV models are provided.

Suggested Citation

  • Jan Pospíšil & Tomáš Sobotka & Philipp Ziegler, 2019. "Robustness and sensitivity analyses for stochastic volatility models under uncertain data structure," Empirical Economics, Springer, vol. 57(6), pages 1935-1958, December.
    • Handle: RePEc:spr:empeco:v:57:y:2019:i:6:d:10.1007_s00181-018-1535-3
    DOI: 10.1007/s00181-018-1535-3
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    References listed on IDEAS

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    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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