Browsing by Subject "Option pricing"
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Item Open Access 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.Item Open Access Measures of model uncertainty and calibrated option bounds(Taylor & Francis, 2009) Pınar, M. Ç.Recently, Cont introduced a quantitative framework for measuring model uncertainty in the context of derivative pricing [Model uncertainty and its impact on the pricing of derivative instruments, Math. Finance, 16(3) (2006), pp. 519-547]. Two measures of model uncertainty were proposed: one measure based on a coherent risk measure compatible with market prices of derivatives and another measure based on convex risk measures. We show in a discrete time, finite state probability setting, that the two measures introduced by Cont are closely related to calibrated option bounds studied recently by King et al. [Calibrated option bounds, Inf. J. Ther. Appl. Financ., 8(2) (2005), pp. 141-159]. The precise relationship is established through convex programming duality. As a result, the model uncertainty measures can be computed efficiently by solving convex programming or linear programming problems after a suitable discretization. Numerical results using S&P 500 options are given.