Browsing by Subject "Bayesian regularization"

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    Degree of mispricing with the black-scholes model and nonparametric cures
    (Peking University Press, 2003) Gençay, R.; Salih, A.
    The Black-Scholes pricing errors are larger in the deeper out-of-the-money options relative to the near out-of-the-money options, and mispricing worsens with increased volatility. Our results indicate that the Black-Scholes model is not the proper pricing tool in high volatility situations especially for very deep out-of-the-money options. Feedforward networks provide more accurate pricing estimates for the deeper out-of-the money options and handles pricing during high volatility with considerably lower errors for out-of-the-money call and put options. This could be invaluable information for practitioners as option pricing is a major challenge during high volatility periods.