Does Exchange Rate Risk Affect Exports Asymmetrically? Asian Evidence

The effects of exchange rate risk have interested researchers, since the collapse of fixed exchange rates. Little consensus exists, however, regarding its effect on exports. Previous studies implicitly assume symmetry. This paper tests the hypothesis of asymmetric effects of exchange rate risk with a dynamic conditional correlation bivariate GARCH(1,1)-M model. The asymmetry means that exchange rate risk (volatility) affects exports differently during appreciations and depreciations of the exchange rate. The data include bilateral exports from eight Asian countries to the US. The empirical results show that real exchange rate risk significantly affects exports for all countries, negative or positive, in periods of depreciation or appreciation. For five of the eight countries, the effects of exchange risk are asymmetric. Thus, policy makers can consider the stability of the exchange rate in addition to its depreciation as a method of stimulating export growth.

Geographic Deregulation and Commercial Bank Performance in US State Banking Markets

This paper examines the effects of geographical deregulation on commercial bank performance across states. We reach some general conclusions. First, the process of deregulation on an intrastate and interstate basis generally improves bank profitability and performance. Second, the macroeconomic variables -- the unemployment rate and real personal income per capita -- and the average interest rate affect bank performance as much, or more, than the process of deregulation. Finally, while deregulation toward full interstate banking and branching may produce more efficient banks and a healthier banking system, we find mixed results on this issue.

Using Leading Indicators to Forecast US Home Sales in a Bayesian VAR Framework

This paper uses Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting US home sales. The benchmark Bayesian model includes home sales, the price of homes, the mortgage rate, real personal disposable income, and the unemployment rate. We evaluate the forecasting performance of six alternative leading indicators by adding each, in turn, to the benchmark model. Out-of-sample forecast performance over three periods shows that the model that includes building permits authorized consistently produces the most accurate forecasts. Thus, the intention to build in the future provides good information with which to predict home sales. Another finding suggests that leading indicators with longer leads outperform the short-leading indicators.