Time-varying Minimum Variance Portfolio

2021 ◽  
Author(s):  
Qingliang Fan ◽  
Ruike Wu ◽  
Yanrong Yang ◽  
Wei Zhong
Keyword(s):  
Minimum Variance ◽  
Time Varying ◽  
Minimum Variance Portfolio

Properties of the beta coefficient of the global minimum variance portfolio

2020 ◽  
Vol 8 (1) ◽  
pp. 11-21
Author(s):  
S. M. Yaroshko ◽  
◽  
M. V. Zabolotskyy ◽  
T. M. Zabolotskyy ◽  
◽  
...  
Keyword(s):  
Confidence Interval ◽  
Asymptotic Distribution ◽  
Global Minimum ◽  
Asset Returns ◽  
Minimum Variance ◽  
Benchmark Portfolio ◽  
Beta Coefficient ◽  
Minimum Variance Portfolio ◽  
Global Minimum Variance Portfolio ◽  
Daily Returns

The paper is devoted to the investigation of statistical properties of the sample estimator of the beta coefficient in the case when the weights of benchmark portfolio are constant and for the target portfolio, the global minimum variance portfolio is taken. We provide the asymptotic distribution of the sample estimator of the beta coefficient assuming that the asset returns are multivariate normally distributed. Based on the asymptotic distribution we construct the confidence interval for the beta coefficient. We use the daily returns on the assets included in the DAX index for the period from 01.01.2018 to 30.09.2019 to compare empirical and asymptotic means, variances and densities of the standardized estimator for the beta coefficient. We obtain that the bias of the sample estimator converges to zero very slowly for a large number of assets in the portfolio. We present the adjusted estimator of the beta coefficient for which convergence of the empirical variances to the asymptotic ones is not significantly slower than for a sample estimator but the bias of the adjusted estimator is significantly smaller.

Minimum Variance Portfolio Composition

2010 ◽  
Author(s):  
Roger Clarke ◽  
Harindra de Silva ◽  
Steven Thorley
Keyword(s):  
Minimum Variance ◽  
Portfolio Composition ◽  
Minimum Variance Portfolio

scholarly journals Time-Varying Wind Load Identification Based on Minimum-Variance Unbiased Estimation

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Huili Xue ◽  
Kun Lin ◽  
Yin Luo ◽  
Hongjun Liu
Keyword(s):  
Damping Ratio ◽  
Estimation Method ◽  
Minimum Variance ◽  
Wind Load ◽  
Ar Model ◽  
Unbiased Estimation ◽  
Recursive Identification ◽  
Time Varying ◽  
Building Structures ◽  
Shear Building

A minimum-variance unbiased estimation method is developed to identify the time-varying wind load from measured responses. The formula derivation of recursive identification equations is obtained in state space. The new approach can simultaneously estimate the entire wind load and the unknown structural responses only with limited measurement of structural acceleration response. The fluctuating wind speed process is investigated by the autoregressive (AR) model method in time series analysis. The accuracy and feasibility of the inverse approach are numerically investigated by identifying the wind load on a twenty-story shear building structure. The influences of the number and location of accelerometers are examined and discussed. In order to study the stability of the proposed method, the effects of the errors in crucial factors such as natural frequency and damping ratio are discussed through detailed parametric analysis. It can be found from the identification results that the proposed method can identify the wind load from limited measurement of acceleration responses with good accuracy and stability, indicating that it is an effective approach for estimating wind load on building structures.

Impact of ESG Rating of Companies on the Portfolio Performance

2021 ◽  
Vol 8 (4) ◽  
pp. 34-42
Author(s):  
Ramkumar Samyukth
Keyword(s):  
Fund Performance ◽  
Minimum Variance ◽  
Socially Responsible Investing ◽  
Sharpe Ratio ◽  
Portfolio Performance ◽  
Socially Responsible ◽  
Responsible Investing ◽  
Service Companies ◽  
Minimum Variance Portfolio ◽  
Mean Variance Portfolio

Socially responsible investing is becoming more popular among people because people are becoming more concerned about the environment and society. Socially responsible investors screen the company by considering the ESG factors. The question raced is whether socially responsible investing improves the portfolio performance and how the funds perform during uncertain times like the Covid-19 pandemic. Since many critics of ESG funds say that the ESG funds’ performance highly depends on Software and Service company stocks, so the relevance of Software and Service companies in the fund has been analyzed in this research. The portfolios have been formed by using the Markowitz mean-variance portfolio model, and the performance of the minimum variance portfolio has been studied. The fund performance has been analyzed using the Sharpe ratio, and the result concludes that the ESG fund performance with minimum variance has an abnormally high Sharpe Ratio of 10.8. A similar type of performance was identified during the Covid-19 pandemic. The abnormally high Sharpe ratio will encourage investors to move towards socially responsible investing.

scholarly journals A Closer Look at the Minimum-Variance Portfolio Optimization Model

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Zhifeng Dai
Keyword(s):  
Objective Function ◽  
Minimum Variance ◽  
Lagrangian Functions ◽  
Portfolio Strategies ◽  
Empirical Performance ◽  
Minimum Variance Portfolio ◽  
Real Market ◽  
Portfolio Optimization Model ◽  
Norm Constraint ◽  
Relationship Of

Recently, by imposing the regularization term to objective function or additional norm constraint to portfolio weights, a number of alternative portfolio strategies have been proposed to improve the empirical performance of the minimum-variance portfolio. In this paper, we firstly examine the relation between the weight norm-constrained method and the objective function regularization method in minimum-variance problems by analyzing the Karush–Kuhn–Tucker conditions of their Lagrangian functions. We give the range of parameters for the two models and the corresponding relationship of parameters. Given the range and manner of parameter selection, it will help researchers and practitioners better understand and apply the relevant portfolio models. We apply these models to construct optimal portfolios and test the proposed propositions by employing real market data.

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