A Note on Portfolio Optimization and the Diagonal Covariance Matrix

2009 ◽  
Author(s):  
David J. Disatnik
Keyword(s):  
Portfolio Optimization ◽  
Covariance Matrix ◽  
Diagonal Covariance Matrix

scholarly journals Object recognition using hybrid boosting method

2016 ◽  
Author(s):  
Osama Ashfaq
Keyword(s):  
Object Recognition ◽  
Covariance Matrix ◽  
Local Region ◽  
Computational Time ◽  
Local Descriptor ◽  
Alternative Approach ◽  
Different Types ◽  
Boosting Method ◽  
Mixture Of Gaussian ◽  
Diagonal Covariance Matrix

Li (ICCV, 2005) proposed a novel generative/discriminative way to combine features with different types and use them to learn labels in the images. However, the mixture of Gaussian used in Li’s paper suffers greatly from the curse of dimensionality. Here I propose an alternative approach to generate local region descriptor. I treat GMM with diagonal covariance matrix and PCA as separate features, and combine them as the local descriptor. In this way, we could reduce the computational time for mixture model greatly while score greater 90% accuracies for caltech-4 image sets.

scholarly journals Evaluating Various Portfolio Optimization Strategies using High-Dimensional Covariance Matrix Estimators

2020 ◽  
Vol 9 (6) ◽  
pp. 64-67
Keyword(s):  
Portfolio Optimization ◽  
Covariance Matrix ◽  
Global Minimum ◽  
Evaluation Studies ◽  
Minimum Variance ◽  
Portfolio Allocation ◽  
New Strategy ◽  
Rotationally Invariant ◽  
Diversified Portfolio ◽  
Allocation Strategies

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.

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

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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