We study the directional predictability of monthly excess stock market returns in the U.S. and ten other markets using univariate and bivariate binary response models. Our main interest is on the potential benefits of predicting the signs of the returns jointly, focusing on the predictive power...

In this paper, we compare the forecasting performance of univariate noncausal and conventional causal autoregressive models for a comprehensive data set consisting of 170 monthly U.S. macroeconomic and financial time series. The noncausal models consistently outperform the causal models. For a...

We develop a similarity-based structural vector autoregressive (SVAR) model using the similar clusters of data relevant for the prevailing initial macroeconomic conditions of interest. Our computationally attractive simple approach enables us to uncover time-varying effects of structural...

We develop a volatility decomposition derived from flexible local projections to quantify the relative contributions of expected discount rates and cash flows to the variation of dividend yields. Local projections enable the incorporation of large information sets, the use of monthly data along...

We propose simulation-based forecasting methods for the noncausal vector autoregressive model proposed by Lanne and Saikkonen (2012). Simulation or numerical methods are required because the prediction problem is generally nonlinear and, therefore, its analytical solution is not available. It...

We obtain new methodological and empirical perspectives on the fundamental risk-return tradeoff in stock returns by imposing economic and asset pricing motivated constraints on the equity premium. In contrast to highly ambiguous past empirical findings, these constraints result in a nonlinear...