Econometric Theory

Professor Sung Hoon Choi to be published in Econometric Theory

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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.

Professor Jungbin Hwang to be Published in Econometric Theory

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Professor Jungbin Hwang and his co-author Yixiao Sun have had their paper, “Simple, Robust, and Accurate F and t Tests in Cointegrated Systems,” accepted by Econometric Theory  as a lead article in a future issue.

In this paper, they propose new, simple, and more accurate statistical tests in a cointegrated system that allows for endogenous regressors and serially dependent errors. The approach involves first transforming the time series using orthonormal basis functions in L-2 space, which have energy concentrated at low frequencies, and then running an augmented regression based on the transformed data and constructing the test statistics in the usual way. The F and t tests developed in this article, are extremely simple to implement have more accurate size in finite samples than existing tests such as the asymptotic chi-squared and normal tests based on the fully modified OLS estimator of Phillips and Hansen (1990) and can be made as powerful as the latter test.