School of Banking and Finance
UNSW Business School
University of New South Wales
2015 - 2016 Yonsei University School of Business, Seoul, South Korea
2007 - 2012 University of Illinois at Urbana-Champaign, Illinois, USA
2006 - 2007 Korea Advanced Institute of Science and Technology (KAIST), Business School, Seoul, South Korea
1999 - 2005 Korea Advanced Institute of Science and Technology (KAIST), Daejon, South Korea
On the Systematic Volatility of Unpriced Earnings Shocks, with Timothy Johnson (JFE) (SSRN) (internet appendix)
Some important puzzles in macro finance can be resolved in a model featuring systematically varying volatility of unpriced shocks to firms' earnings. In the data, the correlation between corporate debt and stock market valuations is low. The model accounts for this via the opposing effect of unpriced earnings risk on levered debt and equity prices. The model also explains the low (or nonexistent) risk-reward relation for the market portfolio of levered equity via the opposing effects of unpriced and priced uncertainty (both components of stock volatility) on the levered equity risk premium. Versions of the model calibrated to empirical measures of both types of fundamental risk can quantitatively substantiate these explanations. Variation in residual earning dispersion accounts for a significant fraction of observed disagreement between debt and equity valuations, and of realized stock volatility. The implication that the two components of risk should forecast the levered equity risk premium with opposite signs is also supported in the data. The results are a notable advance for risk-based asset pricing.
Risk Premium Information from Treasury Bill Yields (paper)
This paper finds that short-maturity Treasury-bill yields have unique information about risk premiums that is not spanned by long-maturity Treasury-bond yields. I estimate two components of risk premiums: one is for long-term and the other is for short-term. The long-term component steepens the slope of yield curves and has forecastability horizon of longer than one year. In contrast, the short-term component affects Treasury-bill yields but almost invisible from Treasury bonds, has forecastability horizon of less than one quarter, and is related to bond liquidity premiums.
Dynamic Capital Structure Model with Investment (paper)
This paper develops a dynamic equilibrium model of capital structure and investment decisions. It explains the difference between the intra- and inter-firm variations in Q ratio. Its intra-firm variations are determined by a firm's own productivity process, while the inter-firm variations are determined by the cross-sectional heterogeneity in firms' production technologies. Understanding this cross-sectional heterogeneity can explain why leverage and profitability are important determinants of investment. The model also successfully matches the moments of aggregate market returns as well as cross-sectional stock return anomalies such as value, profitability, and investment premiums.
Liquidity might be categorized into two types: asset liquidity and funding liquidity. This paper presents a new approach to measure funding liquidity and demonstrates that the estimated funding liquidity can predict future stock market returns. The key idea is that, as capital constraints become more binding, speculators withdraw first from small stocks and then from large stocks. Given that asset liquidity is provided by speculators, the asset liquidity of large and small stocks would covary differently with shocks to speculators' capital depending on their participation in the markets. Based on this intuition, funding liquidity is measured as the difference of rolling correlations of stock market returns with large and small stocks' asset liquidity. The estimated funding liquidity appears positively correlated to aggregate hedge fund leverage ratios, stock market sentiments, and the total number of M&A activities, and negatively to bond liquidity premiums, Moody's Baa-Aaa corporate bond spreads, and the relative prevalence of liquidity mergers. The funding liquidity is able to predict future stock market returns, and its forecasting power is significant in both in-sample and out-of-sample tests. Its forecastability is robust to various equity premium predictors as well as subsample periods.
Jun - Aug 2009 Barclays Capital, New York, USA
Position: Summer Quant Associate within Fixed Income Research
Built a model to estimate monetary policy interest rate expectation and forward risk premium
Implemented a numerical computation library in C# to calibrate parameters of Svensson Curve
2003 - 2005 NHN Corp., Seoul, Korea
Position: Programmer (Online Game Server)
Designed the server structure and the communication protocol between servers and clients