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Risk neutral model selection. In general, if your model has too few parameters, it doesn't capture some features, and if it has too many, it overfits. In statistics, the balance between fit and model parsimony is achieved by information criteria, like Akaike. There is nothing similar for risk neutral models. How do you choose between a one factor Vasicek model and a 10 factor Cheyette with stochastic-local volatility and time-dependent mean-reversions? Currently it's much closer to an art than to a science.



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