THESIS
2022
1 online resource (ix, 49 pages) : illustrations (chiefly color)
Abstract
This thesis proposes a new estimation method for the high-dimensional mean-variance portfolio of risky assets, allowing factor investing. Our estimator mainly
consists of two parts: (1) a consistent estimator of the weight vector on factors,
and (2) an estimator of the high-dimensional weight vector on idiosyncratic components based on linear constrained LASSO. The foundation of applying the linear
constrained LASSO is an equlivalent constrained regression representation of the
weight vector on the idiosyncratic component. Under a mild sparsity assumption,
the new estimator enjoys the mean-variance efficiency asymptotically. In simulation and empirical study on the S&P 500 index components and Fama-French three
factor investment universe, the new estimator can control the risks and gain...[
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This thesis proposes a new estimation method for the high-dimensional mean-variance portfolio of risky assets, allowing factor investing. Our estimator mainly
consists of two parts: (1) a consistent estimator of the weight vector on factors,
and (2) an estimator of the high-dimensional weight vector on idiosyncratic components based on linear constrained LASSO. The foundation of applying the linear
constrained LASSO is an equlivalent constrained regression representation of the
weight vector on the idiosyncratic component. Under a mild sparsity assumption,
the new estimator enjoys the mean-variance efficiency asymptotically. In simulation and empirical study on the S&P 500 index components and Fama-French three
factor investment universe, the new estimator can control the risks and gains high
portfolio returns simultaneously at different risk levels.
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