This paper shows that rare events are important in explaining the cross section of asset returns because of their role in shaping agents' expectations. I reconsider the "bad beta, good beta" ICAPM proposed by Campbell and Vuolteenaho and I point out that the explanatory power of the model relies...

Over the last two decades, alternative expected return proxies have been proposed with substantially lower variation than realized returns. This helped to reduce parameter uncertainty and to identify many seemingly robust relations between expected returns and variables of interest, which would...

We complement the conditional CAPM by introducing unobservable long-run changes in risk factor loadings. In this environment, investors rationally `learn' the long-level of factor loadings from the observation of realized returns. As a direct consequence of this assumption, conditional betas are...

In addition to giving a detailed description and explanation of the Black–Litterman (BL) model, this paper deals with estimation of the parameter tau. This parameter is the most mysterious one in the BL model, as the literature does not provide specific guidance on its calibration....

This paper uses a predictive regression framework to examine the out-of-sample predictability of South Africa’s equity premium, using a host of financial and macroeconomic variables. Past studies tend to suggest that the predictors on their own fail to deliver consistent out-of-sample forecast...

We develop a finite-sample procedure to test for mean-variance efficiency and spanning without imposing any parametric assumptions on the distribution of model disturbances. In so doing, we provide an exact distribution-free method to test uniform linear restrictions in multivariate linear...

We develop a finite-sample procedure to test for mean-variance efficiency and spanning without imposing any parametric assumptions on the distribution of model disturbances. In so doing, we provide an exact distribution-free method to test uniform linear restrictions in multivariate linear...

We develop a finite-sample procedure to test for mean-variance efficiency and spanning without imposing any parametric assumptions on the distribution of model disturbances. In so doing, we provide an exact distribution-free method to test uniform linear restrictions in multivariate linear...

We propose a generalized method of moment (GMM) estimator of the number of latent factors in linear factor models. The method is appropriate for panels a large (small) number of cross-section observations and a small (large) number of time-series observations. It is robust to heteroskedasticity...