Entropy Approach to Incorporate Fat Tailed Constraints in Financial Models

In the existing financial literature, entropy based ideas have been proposed in portfolio optimization and in model calibration for options pricing. The abstracted problem corresponds to finding a probability measure that minimizes Kullbach-Leibler (KL) distance with respect to a known measure while it satisfies certain moment constraints on functions of underlying assets. In this paper, we show that under KL distance, the optimal solution may not exist when constraints involve fat tailed distributions ubiquitous in financial practice. We note that this drawback may be corrected if 'polynomial-divergence' entropy distance is used. We discuss existence and uniqueness issues related to this new optimization problem as well as the nature of the optimal solution under different objectives. We also identify the optimal solution structure under KL distance as well as polynomial divergence when the associated constraints include those on marginal distribution of functions of underlying assets. These results are applied to a simple problem of model calibration to options prices as well as to portfolio modeling in Markowitz framework, where we note that a reasonable view that a particular portfolio of assets has heavy tailed losses may lead to fatter and more reasonable tail distributions of all assets