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Title: Option hedging and parameter estimation of pricing models Authors:  Lo-Bin Chang - Ohio State University (United States) [presenting]
Abstract: Typical parameter estimation methods such as the maximum likelihood estimation and implied parameter estimation rely upon the validity of model assumptions. Given the well-known fact that no model is perfect, the estimation criterion should be selected to adapt to the practical usage of the model. Focusing on option pricing and hedging, a novel criterion for estimating parameters is proposed, which is based on the magnitude of cumulative hedging error over the option lifetime. The theoretical property of this criterion for the Black-Scholes model is provided. Back-testing experiments of delta hedging with real stock data show that for both Black-Scholes and Merton's jump-diffusion models, the proposed hedging-optimization estimation results in better hedging performance over the maximum likelihood estimation and implied parameter estimation.