CONFRONTING MODEL MISSPECIFICATION IN FINANCE: TRACTABLE COLLECTIONS OF SCENARIO PROBABILITY MEASURES FOR ROBUST FINANCIAL OPTIMIZATION PROBLEMS

Despite the widespread realization that financial models for contingent claim pricing, asset allocation and risk management depend critically on their underlying assumptions, the vast majority of financial models are based on single probability measures. In such models, asset prices are assumed to be random, but asset price probabilities are assumed to be known with certainty, an obviously false assumption.

We explore practical methods to specify collections of probability measures for an assortment of important financial problems; we provide practical methods to solve the robust financial optimization problems that arise and, in the process, discover "dangerous" measures.