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Vast Portfolio Selection with Gross-exposure Constraints().
Jianqing Fan1, Jingjin Zhang, Ke Yu
1Frederick L. Moore Professor, Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08540, USA and honorary professor, Department of Statistics, Shanghai University of Economics and Finance, Shanghai, China.
Journal of the American Statistical Association
|January 8, 2013
Summary
Gross-exposure constraints improve large portfolio selection accuracy. This method ensures empirical portfolios match theoretical performance, avoiding estimation errors in covariance matrices for better investment strategies.
Area of Science:
- Quantitative Finance
- Investment Management
- Econometrics
Background:
- Traditional portfolio selection often faces challenges with large datasets and estimation errors.
- The impact of gross-exposure constraints on portfolio performance and estimation error is not fully understood.
- Existing methods may not fully leverage the information in vast covariance matrices.
Purpose of the Study:
- To introduce and analyze a large portfolio selection method using gross-exposure constraints.
- To provide theoretical justification for empirical findings on portfolio optimization.
- To explore improvements over no-short-sale portfolios and address applications in tracking and enhancement.
Main Methods:
- Developing a theoretical framework for portfolio selection with gross-exposure constraints.
- Analyzing the performance of empirically selected portfolios against theoretical optima.
- Conducting simulation studies and empirical analyses using Fama-French and Russell 3000 data.
Main Results:
- Portfolios selected with gross-exposure constraints demonstrate performance comparable to theoretical optimal portfolios.
- The proposed method mitigates the error accumulation effect from estimating large covariance matrices.
- Allowing limited short positions can enhance the performance of no-short-sale portfolios.
Conclusions:
- Gross-exposure constraints offer a robust approach to large portfolio selection, aligning empirical results with theory.
- The method provides a theoretical basis for observed empirical performance and addresses estimation challenges.
- This framework offers practical improvements for portfolio selection, tracking, and investment strategies.
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