We use factor augmented vector autoregressive models with time-varying coe¢ cients to construct a …nancial conditions index. The time-variation in the parameters allows for the weights attached to each …nancial variable in the index to evolve over time. Furthermore, we develop methods for...

In this paper we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also...

This paper examines both the in-sample and out-of-sample performance of three monetary fundamental models of exchange rates and compares their out-of-sample performance to that of a simple Random Walk model. Using a data-set consisting of five currencies at monthly frequency over the period...

This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require...

This paper examines the effect that heterogeneous customer orders flows have on exchange rates by using a new propreitary dataset of weekly net order flow segmented by customer type across nine of the most liquid currency pairs. We make three contributions. First, we investigate the extent to...

In this paper we propose a novel empirical extension of the standard market microstructure order flow model. The main idea is that heterogeneity of beliefs in the foreign exchange market can cause model instability and such instability has not been fully accounted for in the existing empirical...