Nonparametric portfolio estimation and asset allocation

This thesis comprises two essays that apply nonparametric methods to the estimation of portfolio allocations. In the first essay, I test the significance to investor welfare of (i) adding additional assets to the portfolio choice set and (ii) conditioning on predictor variables. I estimate unconditional and conditional optimal allocations of a constant relative risk aversion investor by maximizing a nonparametric approximation of the expected utility integral. Investors can improve their expected utility significantly over that of an equities and cash investor by adding portfolios based on the value or momentum premiums into their asset allocation decision. In contrast, neither a size premium portfolio nor a long-term bond portfolio improves expected utility. The significance of predictability is increased by simultaneously conditioning on the two strongest predictors (of eight) studied: the term spread and the gold industry trend. In the second essay, I formulate a nonparametric estimator that permits combining historical data with a qualitative prior. I investigate the impact of an investor belief, motivated by asset-pricing theory, that optimal allocations are positive. In the estimator construction, I use a Bayesian approach to perturb the probabilities associated with each data point in the empirical distribution to reflect qualitative prior beliefs. In a simulation study and in out-of-sample tests, I find that portfolio estimates conditioned on a belief in the positivity of portfolio weights are significantly more stable than those estimated by an uninformed investor, and that the model performs better in out-of-sample tests than a number of plug-in models. However, the out-of-sample performance lags that of the minimum-variance and 1/N policies.Business, Sauder School ofGraduat