Title:	Robustness and sensitivity analyses for stochastic volatility models under uncertain data structure
Authors:	Pospíšil, Jan Sobotka, Tomáš Ziegler, Philippe
Citation:	POSPÍŠIL, J., SOBOTKA, T., ZIEGLER, P. Robustness and sensitivity analyses for stochastic volatility models under uncertain data structure. Empirical Economics, 2019, roč. 57, č. 6, s. 1935-1958. ISSN 0377-7332.
Issue Date:	2019
Publisher:	Physica-Verlag
Document type:	článek preprint postprint article preprint postprint
URI:	2-s2.0-85052079492 http://hdl.handle.net/11025/36056
ISSN:	0377-7332
Keywords in different language:	robustness analysis;sensitivity analysis;stochastic volatility models;bootstrapping;Monte Carlo filtering
Abstract in different language:	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.
Rights:	© Physica-Verlag
Appears in Collections:	Postprinty / Postprints (KMA) Preprinty / Preprints (KMA) Články / Articles (KMA) OBD

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