Research
Research
with Mengmeng Ao, Yingying Li, and Xinghua Zheng. Revised and Resubmission for Journal of Econometrics.
Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5197964
Presentation: The 16th Society of Financial Econometrics (SoFiE) Annual Conference (Rio de Janeiro, 2024)
Abstract: We propose MAXSER-H, a novel method for estimating large mean-variance efficient portfolios when dealing with asset returns that exhibit heteroscedasticity and heavy-tailedness. The approach integrates robust regressions and a self-normalization technique with the MAXSER method proposed in Ao, Li, and Zheng (2019). We prove that as the number of assets and sample size grow, the MAXSER-H portfolio asymptotically maximizes expected return while adhering to the specified risk constraint. A conditional method, MAXSER-H-TV, is further developed to account for time-varying parameters, improving upon the original unconditional method. Extensive simulations and empirical studies confirm the superior performance of both MAXSER-H and MAXSER-H-TV.
with Yingying Li and Xinghua Zheng. Submitted to Journal of Financial Economics.
Presentation: CityU Workshop in Econometrics and Statistics (Hong Kong, 2026), The 2025 Annual Meeting of the Greater Bay Econometrics Study Group (Hong Kong, 2025), The 2nd HKUST IAS-SBM Joint Workshop on Financial Econometrics in the Big Data Era (Hong Kong, 2025), The 2025 Random Matrix Theory and Applications Summer Workshop (Yunnan, 2025), The 19th International Symposium on Econometric Theory and Applications (Macau, 2025), The 2024 First Macau International Conference on Business Intelligence and Analytics (Macau, 2024)
Abstract: This paper introduces CORE (COnstrained sparse Regression for Efficient portfolios), a novel method for estimating the efficient portfolios from an investment universe composed exclusively of risky assets. We establish the asymptotic mean-variance efficiency of the CORE portfolio as both the number of assets and the sample size proportionally approach infinity. In extensive simulations and empirical studies on S&P 500 Index constituents, the CORE portfolio meets the specified risk levels, delivers superior Sharpe ratios, and outperforms various benchmarks both before and after transaction costs.