Hyunsok Kim

In this paper, we use a logit model to predict the probability of default for Korean shipping companies. We explore numerous financial ratios to find predictors of a shipping firm’s failure and construct four default prediction models. The results suggest that a model with industry specific indicators outperforms other models in predictive ability. This finding indicates that utilizing information about unique financial characteristics of the shipping industry may enhance the performance of default prediction models. Given the importance of the shipping industry in the Korean economy, this study can benefit both policymakers and market participants.

The breakdown of the Bretton Woods system and the adoption of generalized floating exchange rates ushered in a new era of exchange rate volatility and uncertainty. This increased volatility leads economists to search for economic models able to describe observed exchange rate behavior. In the present paper, we propose more general STAR transition functions that encompass both threshold nonlinearity and asymmetric effects. Our framework allows for a gradual adjustment from one regime to another and considers threshold effects by encompassing other existing models, such as TAR models. We apply our methodology to three different exchange rate data sets: one for developing countries and official nominal exchange rates, the second for emerging market economies using black market exchange rates, and the third for OECD economies.