Idiosyncratic volatility and stock returns: a cross country analysis

Empirical evidences regarding the association of idiosyncratic volatility and stock returns are inconsistent with the Capital Asset Pricing Model (CAPM), which implies that idiosyncratic risk should not be priced because it would be fully eliminated through diversification. Using Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) estimated conditional idiosyncratic volatility of individual stocks across 36 countries from 1973 to 2007, we find that idiosyncratic risk is priced on a significantly positive risk premium for stock returns. The evidence is statistically and economically significant. It overwhelmingly supports the prediction of existing theories that idiosyncratic risk is positively related to expected returns.

Portfolio selection with higher moments

We propose a method for optimal portfolio selection using a Bayesian decision theoretic framework that addresses two major shortcomings of the traditional Markowitz approach: the ability to handle higher moments and parameter uncertainty. We employ the skew normal distribution which has many attractive features for modeling multivariate returns. Our results suggest that it is important to incorporate higher order moments in portfolio selection. Further, our comparison to other methods where parameter uncertainty is either ignored or accommodated in an ad hoc way, shows that our approach leads to higher expected utility than competing methods, such as the resampling methods that are common in the practice of finance.Bayesian decision problem, Multivariate skewness, Parameter uncertainty, Optimal portfolios, Utility function maximization,