Putting the squeeze on the sample covariance matrix for portfolio construction

Munro, Brian; Bradfield, D. J.

doi:10.1080/10293523.2015.1125061

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…They showed that the shrinkage estimator reduces the tracking error relative to the benchmark index. Clarke et al On the basis of the findings of Munro & Bradfield [36], the Ledoit & Wolf [26] Bayesian shrinkage estimator was adopted to estimate the covariance matrix in the ensuing empirical study. The Ledoit & Wolf [26] Bayesian shrinkage technique involves finding a compromise between the sample covariance matrix S and a shrinkage target (highly structured estimator F ).…”

Section: Covariance Matrix Estimationmentioning

confidence: 99%

Low volatility sector-based portfolios: a South African case

Oladele

Bradfield²

2016

ORiON

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Portfolios and indices that have been specifically constructed to have low risk attributes have received increasing interest in the recent international literature. It has been found that portfolios constructed by targeting low risk assets have predominantly outperformed portfolios constructed to have higher risks. This anomaly has led to renewed interest in constructing low volatility portfolios by practitioners. This study analyses a variety of low volatility portfolio construction methodologies using sectors as building blocks in the South African environment. The empirical results from back-testing these portfolios show significant promise in the South African setting when compared with a market capitalization-weighted benchmark. In the empirical analysis in the South African environment two techniques stand out as being superior low volatility construction techniques amongst the seven techniques assessed. Furthermore, the low volatility portfolios are blended with typical general equity portfolios (using the Shareholder-Weighted Index (SWIX) as a proxy). It was found that these blended portfolios have useful features which lead to enhanced performance and therefore can serve as effective portfolio strategies.

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Section: Covariance Matrix Estimationmentioning

confidence: 99%

Low volatility sector-based portfolios: a South African case

Oladele

Bradfield²

2016

ORiON

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Research on portfolio optimization under asymmetric power-law distribution of return tail

Feng

Zhang

et al. 2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

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An effective portfolio selection model is constructed on the premise of measuring accurately the risk and return on assets. According to the reality that asset returns obey the asymmetric power-law distribution, this paper first builds two fractal statistical measures, fractal expectation and fractal variance to measure the asset returns and risks, inspired by the method of measuring the curve length in the fractal theory. Then, by incorporating the fractal statistical measure into the return–risk criterion, a portfolio selection model based on the fractal statistical measure is established, namely, the fractal portfolio selection model, and the closed-form solution of the model is given. Finally, through empirical analysis, it is found that under the constraints of typical factual characteristics that the asset returns obey the asymmetric power-law distribution, the fractal portfolio is better than the traditional portfolio as a whole, which not only can improve the investment performance but also has better robustness. The validity of the fractal investment portfolio is experimentally tested.

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An Empirical Assessment of Low Volatilty Portfolio Construction Techniques in the South African Environment

Oladele¹,

Bradfield

2018

Studies in Economics and Econometrics

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Putting the squeeze on the sample covariance matrix for portfolio construction

Cited by 3 publications

References 15 publications

Low volatility sector-based portfolios: a South African case

Low volatility sector-based portfolios: a South African case

Research on portfolio optimization under asymmetric power-law distribution of return tail

An Empirical Assessment of Low Volatilty Portfolio Construction Techniques in the South African Environment

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