Testing the Unbiased Forward Exchange Rate Hypothesis Using a Markov Switching Model and Instrumental Variables

This paper develops a model for the forward and spot exchange rate which allows for the presence of a Markov switching risk premium in the forward market and considers the issue of testing for the unbiased forward exchange rate (UFER) hypothesis. Using US/UK data, it is shown that the UFER hypothesis cannot be rejected provided that instrumental variables are used to account for within-regime correlation between explanatory variables and disturbances in the Markov switching model on which the test is based

The Chair of the U.S. Federal Reserve and the Macroeconomic Causality Regimes

We investigate regime-dependent Granger causality between real output, in ation and monetary indicators and map with U.S. Fed Chairperson's tenure since 1965. While all monetary indicators have causal predictive content in certain time periods, we report that the Federal Funds rate (FFR) and Domestic Money (DM) are substitutes in their role as lead or feedback variables to explain variations in real output and in ation. We provide a comprehensive account of evolution of causal relationships associated with all US Fed Chairpersons we consider

A Hidden Markov Model Approach to Classify and Predict the Sign of Financial Local Trends

In the field of financial time series analysis it is widely accepted that the returns (price variations) are unpredictable in the long period [1]; nevertheless, this unappealing constraint could be somehow relaxed if sufficiently short time intervals are considered. In this paper this alternative scenario is investigated with a novel methodology, aimed at analyzing short (local) financial trends for predicting their sign (increase or decrease). This peculiar problem needs specific models – different from standard techniques used for estimating the volatility or the returns – able to capture the asymmetries between increase and decrease periods in the short time. This is achieved by modeling directly the signs of the local trends using two separate Hidden Markov models, one for positive and one for negative trends. The approach has been tested with different financial indexes, with encouraging results also in comparison with standard methods