We study herd behavior in a laboratory financial market with financial market professionals. We compare two treatments, one in which the price adjusts to the order flow so that herding should never occur, and one in which event uncertainty makes herding possible. In the first treatment, subjects...

Using monthly data for a set of variables, we examine the out-of-sample performance of various variance/covariance models and find that no model has consistently outperformed the others. We also show that it is possible to increase the probability mass toward the tails and to match reasonably...

Following record low interest rates and fast depreciating U.S. dollar, crude oil prices became under rising pressure and seemed boundless. Oil price process parameters changed drastically in 2003M5-2007M10 toward consistently rising prices. Short-term forecasting would imply persistence of...

A stochastic volatility model where volatility was driven solely by a latent variable called news was estimated for three stock indices. A Markov chain Monte Carlo algorithm was used for estimating Bayesian parameters and filtering volatilities. Volatility persistence being close to one was...

Unlike conventional fiscal sustainability assessments, the Value-at-Risk approach developed in this paper explicitly captures the contribution of key risk variables to public sector vulnerability. In an illustrative application to Ecuador, the VaR approach confirms a significant risk of...

We develop a new methodology to estimate the importance of herd behavior in financial markets: we build a structural model of informational herding that can be estimated with financial transaction data. In the model, rational herding arises because of information-event uncertainty. We estimate...