Journal ArticleDOI
A cost-effective approach to portfolio construction with range-based risk measures
Chi Seng Pun,Lei Wang +1 more
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This paper introduces a new class of risk measures and the corresponding risk minimizing portfolio optimization problem and shows that for some cases of the proposed range-based risk measures, the corresponding portfolio optimization can be recast as a support vector regression problem.Abstract:
In this paper, we introduce a new class of risk measures and the corresponding risk minimizing portfolio optimization problem. Instead of measuring the expected deviation of a daily return from a s...read more
Citations
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Journal ArticleDOI
Resolution of Degeneracy in Merton's Portfolio Problem
Chi Seng Pun,Hoi Ying Wong +1 more
TL;DR: This study proposes a constrained $\ell_1$-minimization approach to resolve the degeneracy in the high-dimensionalSetting and stabilize the performance in the low-dimensional setting and proves the consistency of the framework that the estimate of the optimal control tends to be the optimal value.
Journal ArticleDOI
A self-calibrated direct approach to precision matrix estimation and linear discriminant analysis in high dimensions
TL;DR: With the theoretical and empirical evidence, it is shown that the proposed estimator is better than its competitors in statistical accuracy and has clear computational advantages.
Journal ArticleDOI
Optimal dynamic mean–variance portfolio subject to proportional transaction costs and no-shorting constraint
TL;DR: In this paper , a semi-closed form solution of the optimal dynamic investment policy with the boundaries of buying, no-transaction, selling, and liquidation regions was derived by adopting dynamic programming, duality theory, and a comparison approach.
Journal ArticleDOI
Optimal dynamic mean–variance portfolio subject to proportional transaction costs and no-shorting constraint
TL;DR: In this article, a semi-closed form solution of the optimal dynamic investment policy with the boundaries of buying, no-transaction, selling, and liquidation regions was derived by adopting dynamic programming, duality theory, and a comparison approach.
Journal ArticleDOI
A Sparse Learning Approach to Relative-Volatility-Managed Portfolio Selection
TL;DR: In this paper, a self-calibrated sparse learning approach for estimating a sparse target vector, which is a product of a precision matrix and a vector, is proposed, and investigated its application to fina...
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
Proceedings Article
Support Vector Regression Machines
TL;DR: This work compares support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space and expects that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input space.
Journal ArticleDOI
Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?
TL;DR: In this article, the authors evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1-N portfolio.
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