We propose using a Bayesian time-varying coefficient model estimated with Markov chain-Monte Carlo methods to measure contagion empirically. The proposed measure works in the joint presence of heteroskedasticity and omitted variables and does not require knowledge of the timing of the crisis. It...

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...

This paper reviews recent advances in the specification and estimation of Bayesian Vector Autoregressive models (BVARs). After describing the Bayesian principle of estimation, we first present the methodology originally developed by Litterman (1986) and Doan et al. (1984) and review alternative...

We use daily data on bank reserves and overnight interest rates to document a striking pattern in the high-frequency behavior of the U.S. market for federal funds: depository institutions tend to hold more reserves during the last few days of each “reserve maintenance period,” when the...

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...

We propose a model of the interbank money market with an explicit role for central bank intervention and periodic reserve requirements, and study the interaction of profit-maximizing banks with a central bank targeting interest rates at high frequency. The model yields predictions on biweekly...