Evaluating Various Portfolio Optimization Strategies using High-Dimensional Covariance Matrix Estimators
Ruchir Sharma

Ruchir Sharma*, Department of Computer Science Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India.
Manuscript received on July 02, 2020. | Revised Manuscript received on July 10, 2020. | Manuscript published on August 30, 2020. | PP: 64-67 | Volume-9 Issue-6, August 2020. | Retrieval Number:  F1230089620/2020©BEIESP | DOI: 10.35940/ijeat.F1230.089620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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. 
Keywords: Covariance Matrix Estimators, Sample Based Estimator (SCE), Ledoit-Wolf Estimator (LWE), Rotationally Invariant Estimators (RIE), Global Minimum Variance (GMVP), Most-Diversified Portfolio (MDP), Most-Diversified Portfolio (MMDP)