Volume 5, Issue 3

We examine the behavior of the real exchange rates of nine transition economies during the 1990s. We propose an empirical model rationalized on the basis of standard economic models in the tradition of Mundell-Fleming-Dornbusch and Harrod-Balassa-Samuelson, allowing explicitly for real interest rate differentials and (implicitly) for productivity differentials to have an impact on real exchange rate equilibrium and employing nonlinear modeling techniques that are consistent with recently developed economic theories and observed regularities. Using a nonlinear multivariate generalization of the Beveridge-Nelson decomposition applied to our models, we also identify the permanent and temporary components of these real exchange rates implied by our estimates. The results have a natural interpretation and clear policy implications.

This paper assesses empirically two competing unemployment theories. It identifies one structural break in U.K. and German unemployment around 1980 that is more severe in both absolute and relative terms than that for the United States in 1973. This offers support for the structuralist theory. A nonlinear (TAR) model is used to capture fast-up, slow-down unemployment dynamics. Impulse response functions suggest that the half-lives of shocks are longer in the postbreak subsamples, especially in Europe, which places the persistence theory closer to the mark. We conclude that elements from both theories are needed for an adequate account of unemployment dynamics.

This paper examines the dynamic linkages between oil prices and the stock market. Prior work argues that daily oil futures price changes and the S&P 500 stock index movements are not related. This conclusion could be due to the fact that only linear linkages have been examined. Relying on nonlinear causality tests, this study provides evidence that oil shocks affect stock index returns, which is consistent with the documented influence of oil on economic output. Moreover, the study finds that the linkage between oil prices and the stock market was stronger in the 1990s.

From the practitioners' point of view, one of the most interesting questions that tail studies can answer is what are the extreme movements that can be expected in financial markets? Have we already seen the largest ones or are we going to experience even larger movements? Are there theoretical processes that can model the type of fat tails that come out of our empirical analysis? Answers to such questions are essential for sound risk management of financial exposures. It turns out that we can answer these questions within the framework of the extreme value theory. This paper provides a step-by-step guideline for extreme value analysis in the MATLAB environment with several examples.