We embed a news shock, a noisy indicator of the future state, in a two-state Markovswitching growth model. Our framework, combined with parameter learning, features rich history-dependent uncertainty dynamics. We show that bad news that arrives during a prolonged economic boom can trigger a...

This paper examines the relationship between idiosyncratic risk and stock returns in BRICS (Brazil, Russia, India, China, and South Africa) countries by applying parametric and nonparametric approaches. It also explores the idiosyncratic risk puzzle by dividing firms into groups based on...

These days it's become convention (reinforced by the media's treatment of wealth) to assess our net worth by tallying up the market value of our financial assets, even though it's more natural and useful to think of our wealth as a stream of dollars over time given the nature of our income and...

We develop methods to solve general equilibrium models in which forward-looking agents are subject to waves of pessimism, optimism, and uncertainty that turn out to critically affect macroeconomic outcomes. Agents in the model are fully rational, conduct Bayesian learning, and they know that...

We examine the pricing of tail risk in international stock markets. We find that the tail risk of different countries is highly integrated. Introducing a new World Fear index, we find that local and global aggregate market returns are mainly driven by global tail risk rather than local tail...

In this paper, we study the skewness risk and its return predictability in the energy market. Skewness risk is often used to measure the possibility of market crash. We study both physical skewness (market skewness and cross-sectional average realized skewness) estimated from underlying stock...

Private real estate markets have experienced signi ficant in inflows of institutional capital over the last couple of decades. In this paper we seek to understand what are the implications of this recent development. Employing a generalized Hamiltonian Monte Carlo Bayesian procedure we find...

Employing a generalized Hamiltonian Monte Carlo Bayesian procedure we develop a new measure of real estate uncertainty that explicitly encapsulates conditional stochastic volatility and noise. When applied to commercial real estate (CRE) markets, results of Vector Autoregressive (VAR) modeling...

Several recent finance articles employ the Omega measure, proposed by Keating and Shadwick (2002) - defined as a ratio of potential gains out of possible losses - for gauging the performance of funds or active strategies (e.g. Eling and Schuhmacher, 2007; Farinelli and Tibiletti, 2008; Annaert et al., 2009;...