During the recent decade it has become commonly agreeable that existing financial models cannot explain many observable phenomena in the financial world (e.g. the recent mortgage crisis and its worldwide effects, equity premium puzzle and home bias in investing). One flaw of the existing models is that they do not capture the entire picture of uncertainty. This research generalizes models of asset pricing to accommodate additional tier of uncertainty, called ambiguity. Namely, we model financial decision making in a more realistic manner by dropping the standard and restrictive assumption that economic agents know the precise odds of possible events and their accurate monetary outcomes. We formulate financial decision making under multi-dimensional uncertainty using the novel mathematical framework of phantom probability, introduced by Izhakian and Izhakian (2009). This proposal addresses a series of fundamental questions about the nature of ambiguity: How optimal portfolio composition is affected by ambiguity? Whether ambiguity leads to greater savings compared to the standard risk formulations. When do ambiguity and risk reinforce (counteract) each other? How can the level of ambiguity be measured? Can ambiguity be eliminated or reduced by diversification in a manner similar to idiosyncratic risk in the CAPM framework? The effect of ambiguity in an intertemporal framework is highly related to implication of the term structure of interest rate and option pricing. Accommodating ambiguity to option and term structure models can explain some deviations from Black-Scholes pricing formula (e.g. the option “smile”, under pricing and risk-free rate puzzle) and might also produce more realistic term structure model. This research project is supervised by Prof. Thomas Sargent, Prof. Simon Benninga and Prof. Itzhak Gilboa. The outgoing phase will take place at New York University and the return phase will take place at Tel Aviv University.