Abstract
The Black-Scholes option pricing model assumes that (instantaneous) common stock returns are normally distributed. However, the observed distribution exhibits deviations from normality; in particular skewness and kurtosis. We attribute these deviations to gross data errors. Using options' transactions data, we establish that the sample standard deviation, sample skewness, and sample kurtosis contribute to the Black-Scholes model's observed mispricing of a sample from the Berkeley Options Data Base of 2323 call options written on 88 common stocks paying no dividends during the options'life. Following Huber's statement that the primary case for robust statistics is when the shape of the observed distribution deviates slightly from the assumed distribution (usually the Gaussian), we show that robust volatility estimators eliminate the mispricing with respect to sample skewness and sample kurtosis, and significantly improve the Black-Scholes model's pricing performance with respect to estimated volatility.
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We would like to thank Richard Roll, Mark Rubinstein, and seminar participants at Stanford University for helpful comments. Brian Betker and Kwan-Ho Kim provided excellent research assistance.
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Geske, R., Torous, W. Skewness, kurtosis, and black-scholes option mispricing. Statistical Papers 32, 299–309 (1991). https://doi.org/10.1007/BF02925505
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DOI: https://doi.org/10.1007/BF02925505