Portfolio Optimization Using Modified Herfindahl Constraint

Hitaj, Asmerilda; Zambruno, Giovanni

doi:10.1007/978-3-319-61320-8_10

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…The authors in [26] propose the shrinkage estimator toward the constant correlation, while in [31] this approach has been extended to higher moments such as skewness and kurtosis. Empirical analyses have shown that the use of shrinkage estimators for the mean-variance parameters often improves the out-of-sample performance (see [18,19]).…”

Section: Introductionmentioning

confidence: 99%

Smart network based portfolios

Clemente,

Grassi,

Hitaj

2019

Preprint

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In this article we deal with the problem of portfolio allocation by enhancing network theory tools. We use the dependence structure of the correlations network in constructing some well-known risk-based models in which the estimation of correlation matrix is a building block in the portfolio optimization. We formulate and solve all these portfolio allocation problems using both the standard approach and the network-based approach. Moreover, in constructing the network-based portfolios we propose the use of two different estimators for the covariance matrix: the sample estimator and the shrinkage toward constant correlation one. All the strategies under analysis are implemented on two high-dimensional portfolios having different characteristics, covering the period from January 2001 to December 2017. We find that the network-based portfolio consistently better performs and has lower risk compared to the corresponding standard portfolio in an out-of-sample perspective.

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Section: Introductionmentioning

confidence: 99%

Smart network based portfolios

Clemente,

Grassi,

Hitaj

2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Smart network based portfolios

2022

Self Cite

View full text Add to dashboard Cite

In this article we deal with the problem of portfolio allocation by enhancing network theory tools. We propose the use of the correlation network dependence structure in constructing some well-known risk-based models in which the estimation of the correlation matrix is a building block in the portfolio optimization. We formulate and solve all these portfolio allocation problems using both the standard approach and the network-based approach. Moreover, in constructing the network-based portfolios we propose the use of three different estimators for the covariance matrix: the sample, the shrinkage toward constant correlation and the depth-based estimators . All the strategies under analysis are implemented on three high-dimensional portfolios having different characteristics. We find that the network-based portfolio consistently performs better and has lower risk compared to the corresponding standard portfolio in an out-of-sample perspective.

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Design of Optimum Portfolio Scheme Based on Improved NSGA-II Algorithm

Zhou

Chen

Lin

2022

Computational Intelligence and Neuroscience

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In the financial industry, it is of great significance to study the multiobjective portfolio optimization for obtaining a reasonable investment strategy. This paper designs the financial portfolio scheme based on the multiobjective optimization algorithm that is based on the framework of the NSGA-II algorithm. In order to introduce convergence information, aiming at the actual problem of the portfolio, the mixed individual coding mechanism with asset information expands the application of the multiobjective evolutionary algorithm in portfolio optimization. The portfolio scheme obtained is effective, which is helpful to improve the decision-making efficiency of financial investors and enriches the application of modern financial theory.

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Portfolio Optimization Using Modified Herfindahl Constraint

Cited by 4 publications

References 26 publications

Smart network based portfolios

Smart network based portfolios

Smart network based portfolios

Design of Optimum Portfolio Scheme Based on Improved NSGA-II Algorithm

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