Portfolio Optimization Using a Block Structure for the Covariance Matrix

We compare the performance of multiple covariance matrix estimators for the purpose of portfolio optimization. This evaluation studies the ability of estimators like Sample Based Estimator (SCE), Ledoit-Wolf Estimator (LWE), and Rotationally Invariant Estimators (RIE) to estimate covariance matrix and their competency in fulfilling the objectives of various portfolio allocation strategies. In this paper, we have captured the effectiveness of strategies such as Global Minimum Variance (GMVP) and Most-Diversified Portfolio (MDP) to produce optimal portfolios. Additionally, we also propose a new strategy inspired from MDP: Most-Diversified Portfolio (MMDP), that enables diversification upon minimizing risk. Empirical evaluations show that by and large, MMDP furnishes the maximum returns. LWE are relatively more robust than SCE and RIE but RIE performs better under certain conditions.

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ROBUST INDEX-TRACKING AND ENHANCED INDEX-TRACKING IN PORTFOLIO OPTIMIZATION

Studies of Applied Economics ◽

10.25115/eae.v38i1.2955 ◽

2020 ◽

Vol 38 (1) ◽

Author(s):

Nazanin Ansari Khoshabar ◽

Maziar Salahi ◽

Somayyeh Lotfi ◽

Abdelouahed Hamdi

Keyword(s):

Portfolio Optimization ◽

Covariance Matrix ◽

Sharpe Ratio ◽

Second Order ◽

Interval Uncertainty ◽

Index Tracking ◽

Second Order Cone

We study index-tracking and enhanced index-tracking problems in portfolio optimization under interval uncertainty for returns and covariance matrix. The proposed robust counterparts for both models are in the form of second order cone programs. Finally, we test the models on EUROSTOXX 50 dataset. We compare the solutions of the robust models with nominal models to show the effect of uncertainty, and compare the performance of different strategies in terms of Sharpe ratio.

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When do improved covariance matrix estimators enhance portfolio optimization? An empirical comparative study of nine estimators

Quantitative Finance ◽

10.1080/14697688.2010.534813 ◽

2011 ◽

Vol 11 (7) ◽

pp. 1067-1080 ◽

Cited By ~ 28

Author(s):

Ester Pantaleo ◽

Michele Tumminello ◽

Fabrizio Lillo ◽

Rosario N. Mantegna

Keyword(s):

Comparative Study ◽

Portfolio Optimization ◽

Covariance Matrix

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Portfolio Optimization of the Mean-Absolute Deviation Model of Some Stocks using the Singular Covariance Matrix

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6363.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 7818-7822

Keyword(s):

Portfolio Optimization ◽

Covariance Matrix ◽

Stock Exchange ◽

Optimal Portfolio ◽

Risk Level ◽

Mean Absolute Deviation ◽

Investment Portfolio ◽

Absolute Deviation ◽

Singular Covariance Matrix ◽

The Mean

Investing in the stock sector, investors often face risk problems. Usually, forming an investment portfolio is done to minimize risk. In this research, investment portfolio optimization is discussed. The data analyzed are 8 shares traded on the capital market in Indonesia through the Indonesia Stock Exchange (IDX). Optimization is performed using the Mean-Absolute Deviation model with the singular covariance matrix to determine the optimal weights. The results of portfolio optimization Mean-Absolute Deviation model with singular covariance matrix method, was obtained optimal portfolio weights that is of 17.22% for BBCA shares; 26.64% for TKIM shares; 9.96% for BBRI shares; 9.96% for BBNI shares; 8.70% for BMRI shares; 3.75% for ADRO shares; 6.52% for GGRM shares; and 17.25% for UNTR shares. Where the optimal portfolio composition is obtained the expected rate of return (expected return) of 0.18% with a portfolio risk level (standard deviation) of 0.07%.

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