Financial Probabilities from Fisher Information

Abstract

We present a novel synthesis of Fisher information and asset pricing theory that yields a practical method for reconstructing the probability density implicit in security prices. The Fisher information approach to these inverse problems transforms the search for a probability density into the solution of a differential equation for which a substantial collection of numerical methods exist. We illustrate the potential of this approach by calculating the probability density implicit in both bond and option prices. Comparing the results of this approach with those obtained using maximum entropy we find that Fisher information usually results in probability densities that are smoother than those obtained using maximum entropy.