junkyung

Thank you for your interest in my research. Here are selected papers. We examine credit supply leading up to and during a repo run. Lenders initially relax lending terms after a bad signal about the borrower's creditworthiness. Lenders with greater exposure to the borrower, a closer relationship, or a larger vested interest in the collateral asset class intervene faster and sustain forbearance longer. Lenders also prioritize larger, shorter-term loans and loans against illiquid or low-quality assets. These findings are consistent with the predictions of a parsimonious coordination model in which lenders trade off collateral safety against borrower survival. The model produces a cooperative forbearance equilibrium and a tipping point beyond which haircuts become strategic complements and a run begins. Empirically, we find that other loan terms—price and maturity—remain stable before and during the run, underscoring the central role of collateral in dynamically managing risk exposure. We investigate corporate bond defaults from 1995 to 2020 using hand-collected data from hard-copy publications in Korea. Using an under-sampling method, we construct default prediction models based on machine learning models as well as a logistic model. The empirical results show that the random forest model outperforms the others. However, regardless of the models used, model performance in financial crisis periods is significantly worse than in non-crisis periods. This finding suggests the need for additional information to improve model performance during crises when the default prediction is the most relevant. Furthermore, the dominant predictor of defaults before the Global Financial Crisis was the debt ratio, while subsequently, the coverage ratio has become the most important predicto