Option pricing and trading with artificial neural networks and advanced parametric models with implied parameters

We combine parametric models and feedforward artificial neural networks to price and trade European SP (ii) the Corrado and Su that additionally allows for excess skewness and kurtosis via a Gram-Charlier series expansion; (iii) analytic models that extend the GBM by incorporating multiple sources of Poisson distributed jumps; and (vi) stochastic volatility and jump models. Daily average implied parameters of these models are estimated with options transaction data via an unconstraint process optimized by the non-linear least squares Levenberg-Marquardt algorithm. This structural average implied parameters are used to validate the out-of sample pricing and trading (with transaction costs) ability of all models developed.