I have three different option pricing models, for which I computed the in-sample and out-of-sample pricing errors.
Now I want to test the pricing performance of these three option pricing models against the Black-Scholes obtained prices. What is the most convenient way to do so?
When comparing the empirical predictive performance of various models, one usually uses the Diebold-Mariano test statistic. Would this be a valid approach for comparing the performance of the option models? I am concerned about the fact that the Diebold-Marino (DM) test-statistic is not following a standard normal distribution when using the pricing errors of the option models as input.
One paper (Andreou, Charalambous and Martzoukos, 2005) uses the matched-pair t-tests concerning the squared differences to compare the performance statistically. Here I am again concerned about the distribution of the t-statistic, as the inferences made are only valid in case of a standard normal distribution.
Or is bootstrapping the DM-statistic a solution? Let me know your thoughts...