Empirical assessment and model selection in option pricing

The majority of empirical option pricing studies consider the distance from the market option prices as the performance metric. Though, this kind of assessment is limited to the objectives of proper hedging of options and fair pricing of OTC contracts. Options can also be used in market efficiency tests. Efficiency tests require positions in options and other assets (e.g. underlying security) with the objective to yield risk-adjusted profits. If a model can generate excess profit consistently, then it might claim that markets are not efficient. This study consists of four main parts. Different empirical option pricing studies are investigated in terms of assessment methodology. It is shown that error aggregation with data mining algorithms is a more robust way to assess model performance of a model. New model error metrics based on efficiency tests are introduced. Finally, all the new findings are used in the introduction of a model selection framework. Model selection uses the price and hedge estimates of individual pricing models (e.g. Black-Scholes) to come up with better price and hedge structure according to the performance metric. Main motivation is to avoid overfitting of complex models, by switching between simpler but effective models according to performance shifts. Experiments with SPX and NDX contracts between 2009 and 2013 indicate that a model selection method with only a data mining algorithm to group errors and a simple selection rule, yields consistently better results than the individual models it uses.