Assistant Professor of Finance
John and Anne Oros Professor (2023 – present)
Wisconsin School of Business
University of Wisconsin–Madison

5253 Grainger Hall
975 University Avenue
Madison, WI 53706-1324

E-mail: sang.seo@wisc.edu | Phone: (608) 262-9777
CV | SSRN | Google Scholar

EDUCATION

The Wharton School, University of Pennsylvania
Ph.D. & M.A. in Finance, 2010 – 2015

KAIST (Korea Advanced Institute of Science and Technology)
B.S. in Management Engineering & Mathematics, 2003 – 2007

PUBLISHED AND ACCEPTED PAPERS

Do Rare Events Explain CDX Tranche Spreads?, with Jessica A. Wachter

Option Prices in a Model with Stochastic Disaster Risk, with Jessica A. Wachter

Learning, Slowly Unfolding Disasters, and Asset Prices, with Mohammad Ghaderi and Mete Kilic

Characterizing the Variance Risk Premium: The Role of the Leverage Effect, with Guanglian Hu and Kris Jacobs

Synthetic Options and Implied Volatility for the Corporate Bond Market, with Steven Chen and Hitesh Doshi

Why Do Rational Investors Like Variance at the Peak of a Crisis? A Learning-Based Explanation, with Mohammad Ghaderi and Mete Kilic
The Risk and Return of Equity and Credit Index Options, with Hitesh Doshi, Jan Ericsson, and Mathieu Fournier

WORKING PAPERS

Is There a Macro-Announcement Premium?, with Mohammad Ghaderi
Abstract: The conditional return volatility barely drops at macro-announcements. This is at odds with the notion that high announcement returns are a manifestation of a large announcement premium. We show that models with an announcement premium cannot explain the joint patterns of return and volatility over announcement days. Surprisingly, traditional models, which do not feature such a premium, can. Our estimation results based on a statistical setup indicate that the average announcement return is mostly attributable to the monetary policy surprise and pure small-sample components, which do not average out in-sample; the announcement premium is estimated to be small, if any.

Options on Interbank Rates and Implied Disaster Risk, with Hitesh Doshi and Hyung Joo Kim
Abstract: The identification of disaster risk has remained a significant challenge due to the rarity of macroeconomic disasters. We show that the interbank market can help characterize the time variation in disaster risk. We propose a risk-based model in which macroeconomic disasters are likely to coincide with interbank market failure. Using interbank rates and their options, we estimate our model via MLE and filter out the short-run and long-run components of disaster risk. Our estimation results are independent of the stock market and serve as an external validity test of rare disaster models, which are typically calibrated to match stock moments.

Learning, Subjective Beliefs, and Time-Varying Preferences for Different Inflation Ranges, with Mohammad Ghaderi and Ivan ShaliastovichAbstract: We identify desirable/undesirable inflation outcomes under subjective beliefs by comparing survey-based and risk-adjusted distributions of inflation. Intuitively, investors dislike inflation at both extremes, preferring a range in the middle. This "good inflation" region, which investors associate with lower-than-average marginal utility, substantially varies over time in position and width, revealing time-varying preferences across inflation ranges. Different inflation ranges contribute to the inflation risk premium with varying signs, offsetting each other and often masking important insights into the pricing of inflation risk. We rationalize empirical patterns using a model where investors learn and update beliefs about hidden deflationary and inflationary recession states.

Pricing of Corporate Bonds: Evidence From a Century-Long Cross-Section, with Mohammad Ghaderi, Sebastien Plante, and Nikolai RoussanovAbstract: We construct a new historical corporate bond database spanning 128 calendar years to address longstanding data limitations in corporate bond research. By hand-collecting monthly corporate bond quotes from three archival print sources, we complement existing datasets and create an extensive database dating back to 1895, comprising nearly 110,000 unique bonds and 8 million observations. Leveraging this expanded sample, we find that the lack of priced risks in corporate bonds documented by recent studies stems from their reliance on short samples. With greater statistical power, we show that prominent bond and stock factors as well as nontraded macroeconomic factors are significantly priced with theoretically consistent signs. Our database, covering major economic episodes like the Great Depression, not only validates previous empirical findings like the GZ spread's predictive content but also aims to serve as a "CRSP" for corporate bonds.