Change point models using hierarchical priors share in the information of each regime when estimating the parameter values of a regime. Because of this sharing, hierarchical priors have been very successful when estimating the parameter values of short-lived regimes and predicting the...

Using a modified DCC-MIDAS specification that allows the long-term correlation component to be a function of multiple explanatory variables, we show that the stock-bond correlation in the US, the UK, Germany, France, and Italy is mainly driven by inflation and interest rate expectations as well...

This paper contributes to model the industry interconnecting structure in a network context. General predictive model (Rapach et al. 2016) is extended to quantile LASSO regression so as to incorporate tail risks in the construction of industry interdependency networks. Empirical results show a...

A large set of macroeconomic variables have been suggested as equity risk premium predictors in the literature. This paper proposes a forecasting approach for the equity risk premium with two novel features. First, individual month-ahead forecasts are obtained from parsimonious threshold...

The literature on stock return predictability has identified macroeconomic and technical predictors that when combined, leads to out-of-sample outperformance relative to the historical mean null. This paper investigates a new method for aggregating information beyond using forecast combination...

We evaluate stock return predictability using a fully flexible Bayesian framework, which explicitly allows for different degrees of time-variation in coefficients and in forecasting models. We believe that asset return predictability can evolve quickly or slowly, based upon market conditions,...

We generalize the Ferreira and Santa-Clara (2011) sum-of-the-parts method for forecasting stock market returns. Rather than summing the parts of stock returns, we suggest summing some of the frequency-decomposed parts. The proposed method signi cantly improves upon the original sum-of-the-parts...

In this study, we propose a set of covariates that exploit information content of hedge funds' relative size, performance, growth, tail risk, and past liquidation rate, in predicting their liquidation. Empirical results show that our proposed covariates exhibit significant predictive power for...

The CCC-GARCH model, and its dynamic correlation extensions, form the most important model class for multivariate asset returns. For multivariate density and portfolio risk forecasting, a drawback of these models is the underlying assumption of Gaussianity. This paper considers the so-called...