Professor Sung Hoon Choi’s recent article titled “Large Global Volatility Matrix Analysis Based on Observation Structural Information” has been accepted for publication in Econometric Theory, one of the leading scholarly journals in theoretical econometrics.
Abstract
In this paper, we develop a novel large volatility matrix estimation procedure for analyzing global financial markets. Practitioners often use lower-frequency data, such as weekly or monthly returns, to address the issue of different trading hours in the international financial market. However, this approach can lead to inefficiency due to information loss. To mitigate this problem, our proposed method, called Structured Principal Orthogonal complEment Thresholding (Structured-POET), incorporates observation structural information for both global and national factor models. We establish the asymptotic properties of the Structured-POET estimator, and also demonstrate the drawbacks of conventional covariance matrix estimation procedures when using lower-frequency data. Finally, we apply the Structured-POET estimator to an out-of-sample portfolio allocation study using international stock market data.





Huari’s research interests are in asset pricing, financial econometrics, macro finance, and machine learning. At Sewanee, she teaches the courses, Investment Finance, Derivatives and Fixed Income Securities, Financial Modeling, and Financial Engineering.
Current UConn PhD students, do reach out to Huari and Tao for advice on building a successful academic career at a liberal arts college.
The University of Connecticut’s Center for Career Development is excited to 

