We build an equilibrium model to explain why stock return predictability concentrates in bad times. The key feature is that investors use different forecasting models, and hence assess uncertainty differently. As economic conditions deteriorate, uncertainty rises and investors' opinions...

In this paper we investigate the predictive power of cross-sectional volatility, skewness and kurtosis for future stock returns. Adding to the work of Maio (2016), who finds cross-sectional volatility to forecast a decline in the equity premium with high predictive power in-sample as well as...

A generalization of the hyperbolic secant distribution which allows both for skewness and for leptokurtosis was given by Morris (1982). Recently, Vaughan (2002) proposed another flexible generalization of the hyperbolic secant distribution which has a lot of nice properties but is not able to...

We explore the issue of estimating a simple agent-based model of price formation in an asset market using the approach of Alfarano et al. (2008) as an example. Since we are able to derive various moment conditions for this model, we can apply generalized method of moments (GMM) estimation. We...

We study the well-known multiplicative Lognormal cascade process in which the multiplication of Gaussian and Lognormally distributed random variables yields time series with intermittent bursts of activity. Due to the non-stationarity of this process and the combinatorial nature of such a...

We develop a novel machine learning method to estimate large dimensional time-varying GMM models via our newly designed ridge fusion regularization scheme. Our method is a one-step procedure and allows for abrupt, smooth and dual type time variation with a fast rate of convergence. It...

This paper develops a Monte-Carlo backtesting procedure for risk premia strategies and employs it to study Time-Series Momentum (TSM). Relying on time-series models, empirical residual distributions and copulas we overcome two key drawbacks of conventional backtesting procedures. We create...