Cheolwoo Han
Sungkyunkwan University · 経済学
研究室紹介
Professor Cheolwoo Han's research lab specializes in financial engineering, credit risk modeling, and quantitative investment strategies. The lab focuses on developing advanced statistical and machine learning models to address market inefficiencies, such as overreaction and momentum anomalies, while enhancing risk management through innovative credit risk frameworks. Key research directions include dynamic market integration, PD-LGD dependency modeling, and robust portfolio optimization under uncertainty. The lab emphasizes practical applications, combining theoretical rigor with empirical validation in equity, derivative, and credit markets.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15We investigated the overreaction of the Korean market in response to shocks in the US stock market, and analysed the dynamic relationship between these two markets since 1996. We found that the KOSPI 200 index futures overreacted to the S&P 500 index returns during the period from 2000 to 2009 when the Korean market was in its growth stage. As the Korean market matured and the KOSPI 200 overnight futures were introduced in 2009, the overreaction disappeared. When investors employed the Kelly mod
The independent sector assumption in the CreditRisk+ model has been a major bstacle to its implementation. Attempts to overcome this limitation have not met with much success. This paper proposes an extension of the original model that accommodates a wide range of sector covariance structures. Existing numerical algorithms designed for the original model can be reused with little modification. Case studies demonstrate that our model outperforms other CreditRisk+ variants that allow sector depend
This paper documents the bimodality of momentum stocks: both high- and low-momentum stocks have nontrivial probabilities for both high and low returns. The bimodality makes the momentum strategy fundamentally risky and can cause a large loss. To alleviate the bimodality and improve return predictability, this paper develops a novel cross-sectional prediction model via machine learning. By reclassifying stocks based on their predicted financial performance, the model significantly outperforms off
In this article, we investigate the impacts of futures and options markets on the volatility of the underlying market. Unlike earlier studies, the focus is on their persistence over time. Tests on the Hang Seng index yield several interesting results that often contrast with previous findings. Empirical results suggest that the quality of new information generated by derivative trading determines the impacts on the spot market volatility. The futures market provides new, material information red
In this article, a generic severity risk framework in which loss given default (LGD) is dependent upon probability of default (PD) in an intuitive manner is developed. By modeling the conditional mean of LGD as a function of PD, which also varies with systemic risk factors, this model allows an arbitrary functional relationship between PD and LGD. Based on this framework, several specifications of stochastic LGD are proposed with detailed calibration methods. By combining these models with an ex
Abstract This paper develops a portfolio model that penalizes the deviation from a reference portfolio. The proposed model renders a robust portfolio that performs superior under parameter uncertainty. Penalizing the deviation also improves the performance of existing shrinkage portfolio models that are suboptimal due to model parameter uncertainty. The equal‐weight portfolio turns out to be a better reference portfolio than the currently holding portfolio even in the presence of transaction cos
In this article, we propose a new framework for addressing multivariate time-varying volatilities. By employing methods of differential geometry, our model respects the geometric structure of the covariance space, i.e., symmetry and positive definiteness, in a way that is independent of any local coordinate parametrization. Its parsimonious specification makes it particularly suitable for large dimensional systems. Simulation studies suggest that our model embraces much of the nonlinear behaviou
Abstract We propose an extension of the existing information criterion‐based structural break identification approaches. The extended approach helps identify both pure structural change (break) and partial structural change (break) . A pure structural change refers to the case when breaks occur simultaneously in all parameters of regression equation, whereas a partial structural change happens when breaks occur in some parameters only. Our approach consistently outperforms other well‐known appro
Abstract In this article, we develop a bankruptcy prediction model for Korean firms that utilize logit regression. We find that not only financial accounting ratios but equity market inputs and macro-economic variables are also important predictors of bankruptcy. However, unlike the findings of Campbell et al. (2008), using market value of equity in computing total assets did not improve the model. We compare the model with a Merton-type structural model and find that our model demonstrates a hi