[We examine how local and global political risks affect industry return volatility. Our central premise is that some industries are more sensitive to political events than others. We find that industries that are more dependent on trade, contract enforcement, and labor exhibit greater return volatility when local political risks are higher. Political uncertainty in countries of trading partners of trade-dependent industries similarly results in greater volatility. Volatility decomposition results indicate that while systematic volatility is associated with domestic political uncertainty, global political risks translate into larger idiosyncratic volatility.]

We examine how local and global political risks affect industry return volatility. Our central premise is that some industries are more sensitive to political events than others. We find that industries that are more dependent on trade, contract enforcement, and labor exhibit greater return volatility when local political risks are higher. Political uncertainty in countries of trading partners of trade-dependent industries similarly results in greater volatility. Volatility decomposition results indicate that while systematic volatility is associated with domestic political uncertainty, global political risks translate into larger idiosyncratic volatility. (JEL G10, G15) On September 29, 2008, the U.S. House of Representatives voted down the bailout bill proposed by the Treasury and the Federal Reserve in order to provide extra liquidity to the troubled U.S. financial markets. Within two hours the Chicago Board Options Exchange Volatility Index increased by 17%, while in one day the Dow Jones Industrial Average Index dropped 778 points. Global stock markets reacted in a similar fashion.1 Clearly, the uncertainty about the outcome of a critical vote was reflected by both domestic and global stock

This paper analyzes determinants of country default risk in emerging markets, reflected by sovereign yield spreads. The results reported so far in the literature are heterogeneous with respect to significant explanatory variables. This could indicate a high degree of uncertainty about the “true” regression model. We use Bayesian Model Averaging as the model selection method in order to find the variables which are most likely to determine credit risk. We document that total debt, history of recent default, currency depreciation, and growth rate of foreign currency reserves as well as market sentiments are the key drivers of yield spreads.