Research Statement


I am a job market candidate in Economics at Duke University. My fields of interest are financial economics and applied econometrics. My research concentrates on applying econometric tools to solve problems in finance, based on high frequency data. In particular, my dissertation topic is market microstructure and information processing in financial markets.

Download the full version of Research Statement here.

Research Experiences

“Investor Sentiment and Volume-Volatility Relationship” (Job Market Paper)[Download]

This paper shows the effect investor sentiment on information processing in the nancial market. We investigate how disagreement among investors affects the relationship between trading intensity and price volatility around macroeconomic announcements during high and low sentiment periods. By incorporating into the Kandel and Pearson (1995) model a one factor structure with heterogeneous beliefs in the idiosyncratic components, we explicitly derive the volume-volatility elasticity for individual stocks around systematic information release.
Our empirical results are based on intraday transaction data of S&P 500 ETF and Dow Jones 30 components, as well as high frequency econometric tools for the multi-dimensional setting. Consistent with the model predictions, our estimates of elasticity decrease signicantly with the ratio of idiosyncratic variance. Disagreement measures only cast signicantly negative effect in high sentiment periods for both the market portfolio and individual stocks, which is in line with changes in investors’ condence level when sentiment regime shifts.

“Volume, Volatility and Public News Announcements”, with Tim Bollerslev and Jia Li, revise and resubmit to Review of Economic Studies [Download]

We provide new empirical evidence for the way in which financial markets process information. Our results are based on high-frequency intraday data along with new econometric techniques for making inference on the relationship between trading intensity and spot volatility around public news announcements. Consistent with the predictions derived from a theoretical model in which investors agree to disagree, our estimates for the intraday volume-volatility elasticity around the most important news announcements are systematically below unity. Our elasticity estimates also decrease significantly with measures of disagreements in beliefs, economic uncertainty, and textual-based sentiment, further highlighting the key role played by differences-of-opinion.

“Efficient Estimation of Integrated Volatility with Irregular Observation Times”, work in progress