Main areas of teaching: Econometrics (ECON 4570), Data Analysis in Economics and Finance (ECON 4580/6030), Advanced Data Analytics and Policy Evaluation (ECON 4590/6040)

My research interest lies in the analysis of nonstationary time series data that can be applied in many areas in finance, including financial markets prediction, portfolio investment, and risk management. Specifically, my research has been focused on developing statistical methods that can be used to deal with nonstationary issues when conducting statistical inference or making predictions. My work in this context is directed towards:

1. Identification of informative economic and financial predictors of market returns. A new inference approach, MBB-IVXQR, is proposed in one of my recent papers to test for the significance of both nonstationary and stationary predictors. Different from the widely used filtration methods such as first-differencing, the IVX-filtration can maintain the maximum amount of information in a nonstationary data stream during the process of filtration. In the simulated experiments, it has been shown that the proposed method can effectively reduce more than 50% of the inference errors of the conventional methods.

2. Prediction of rare events in the financial markets. My research also focuses on time series quantile regression and its application in finance. Quantile regression (QR) is a powerful statistical method that can be used to identify heterogeneous relationships between economic variables. In finance, it can be applied to identify useful information to predict the bad/good time in the market. With an understanding of the properties of the financial time series data, I have proposed several statistical methods to apply the quantile regression method to analyze the rare events in the financial markets.

3. Applying machine learning techniques to the analysis of high-dimensional, nonstationary time series data. Some of my recent work focuses on dealing with high-dimensional time series data when some time series are stationary, some are nonstationary, and some are cointegrated. I propose to use the adaptive lasso method for selecting mixed-root predictors in an increasing-dimension framework, and prove its validity and efficiency. I also provide sufficient evidence from the analysis of financial markets to show that this method can greatly reduce the forecasting errors relative to conventional statistical methods.