Abstract
This chapter examines a decision-theoretic Bayesian framework for the estimation of Sharpe style portfolio weights of the Morgan Stanley Capital International (MSCI) sector returns. An appropriately defined prior density of style weights can incorporate non-negativity and other constraints. Factor-mimicking portfolios are used as proxies to global style factors such as value, growth, debt, and size. The computational approach is based on the Monte Carlo Integration (MCI) for the estimation of the posterior moments and distribution of portfolio weights. MCI provides a number of advantages, such as a flexible choice of prior distributions, improved numerical accuracy of the estimated parameters, use of inequality restrictions in prior distributions, and exact inference procedures. The empirical findings suggest that style factors explain the MSCI sector portfolio returns for the particular sample period. Further, non-negativity constraints on portfolio weights were found to be binding in all cases.
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