Exchange-rate risk and exports: evidence from a set of transition economies

This article investigates the hypothesis that exchange-rate risk may have an effect on exports for a set of transition countries, namely, Belarus, Kazakhstan, Russia, and Ukraine. To assess this effect, although a two-step estimation approach has earned an extensive empirical record in the literature, a number of studies in this context do not appear to support this approach due to a potential generated regressor issue. This dissonance in a two-step estimation procedure seems to have been somewhat resolved by a relatively new branch of empirical approach that exploits a multivariate version of generalized autoregressive conditional heteroskedasticity in-mean models. The findings suggest that the effect of exchange-rate risk seems to be detrimental in Belarus and Ukraine. However, for Russia and Kazakhstan, which are heavily dependent on crude oil exports, the effect has been found to be indeterminate

Forecasting volatility in the biofuel feedstock markets in the presence of structural breaks: a comparison of alternative distribution functions

The need for research on commodity volatility has grown considerably due to the important role and financialization of commodities in global asset markets. This paper examines the volatility forecasting performance of a wide variety of GARCH-based models in the context of biofuel feedstock markets in the presence of structural breaks. Our sample is also extended to several non-renewable energy commodities to evaluate comparatively the volatility forecasting performance across various commodity markets. The model specifications allow for different conditional distribution functions in the rolling window estimations. A break detection algorithm finds significant evidence of structural breaks in the unconditional variance of all commodity returns under study. The out-of-sample analysis, which is based on an up-to-date model comparison testing procedure, reveals that volatility models accommodating structural breaks in the data provide the best volatility forecasts for most cases. Regarding the relevance of distribution functions, the skewed normal distribution dominates in the model confidence sets. Nevertheless, the complex distribution functions do not always outperform simpler ones, although true return distribution is asymmetric and heavy-tailed