scholarly journals Stable Portfolio Selection Strategy for Mean-Variance-CVaR Model under High-Dimensional Scenarios

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yu Shi ◽  
Xia Zhao ◽  
Fengwei Jiang ◽  
Yipin Zhu
Keyword(s):  
Optimization Problems ◽  
Short Selling ◽  
Computational Method ◽  
High Dimensional ◽  
Selection Strategy ◽  
Covariance Estimator ◽  
Out Of Sample ◽  
Sparse Portfolio ◽  
Mean Variance ◽  
Progressive Optimization

This paper aims to study stable portfolios with mean-variance-CVaR criteria for high-dimensional data. Combining different estimators of covariance matrix, computational methods of CVaR, and regularization methods, we construct five progressive optimization problems with short selling allowed. The impacts of different methods on out-of-sample performance of portfolios are compared. Results show that the optimization model with well-conditioned and sparse covariance estimator, quantile regression computational method for CVaR, and reweighted L1 norm performs best, which serves for stabilizing the out-of-sample performance of the solution and also encourages a sparse portfolio.

scholarly journals Técnicas Quantitativas de Otimização de Carteiras Aplicadas ao Mercado de Ações Brasileiro

2012 ◽  
Vol 10 (3) ◽  
pp. 369
Author(s):  
André Alves Portela Santos ◽  
Cristina Tessari
Keyword(s):  
Covariance Matrix ◽  
Minimum Variance ◽  
Short Selling ◽  
Optimization Techniques ◽  
Market Portfolio ◽  
Sample Covariance ◽  
Out Of Sample ◽  
Weighted Portfolio ◽  
Performance Statistics ◽  
Mean Variance

In this paper we assess the out-of-sample performance of two alternative quantitative portfolio optimization techniques - mean-variance and minimum variance optimization – and compare their performance with respect to a naive 1/N (or equally-weighted) portfolio and also to the market portfolio given by the Ibovespa. We focus on short selling-constrained portfolios and consider alternative estimators for the covariance matrices: sample covariance matrix, RiskMetrics, and three covariance estimators proposed by Ledoit and Wolf (2003), Ledoit and Wolf (2004a) and Ledoit and Wolf (2004b). Taking into account alternative portfolio re-balancing frequencies, we compute out-of-sample performance statistics which indicate that the quantitative approaches delivered improved results in terms of lower portfolio volatility and better risk-adjusted returns. Moreover, the use of more sophisticated estimators for the covariance matrix generated optimal portfolios with lower turnover over time.

CONIC PORTFOLIO THEORY

2016 ◽  
Vol 19 (03) ◽  
pp. 1650019 ◽  
Author(s):  
DILIP B. MADAN
Keyword(s):  
Optimization Problems ◽  
Risk Measures ◽  
Market Value ◽  
Acceptable Risks ◽  
Out Of Sample ◽  
Bid Price ◽  
Distorted Expectation ◽  
Risk Acceptability ◽  
Two Price Economy ◽  
Mean Variance

Portfolios are designed to maximize a conservative market value or bid price for the portfolio. Theoretically this bid price is modeled as reflecting a convex cone of acceptable risks supporting an arbitrage free equilibrium of a two price economy. When risk acceptability is completely defined by the risk distribution function and bid prices are additive for comonotone risks, then these prices may be evaluated by a distorted expectation. The concavity of the distortion calibrates market risk attitudes. Procedures are outlined for observing the economic magnitudes for diversification benefits reflected in conservative valuation schemes. Optimal portfolios are formed for long only, long short and volatility constrained portfolios. Comparison with mean variance portfolios reflects lower concentration in conic portfolios that have comparable out of sample upside performance coupled with higher downside outcomes. Additionally the optimization problems are robust, employing directionally sensitive risk measures that are in the same units as the rewards. A further contribution is the ability to construct volatility constrained portfolios that attractively combine other dimensions of risk with rewards.

Do Commodities Add Economic Value in Asset Allocation? New Evidence from Time-Varying Moments

2018 ◽  
Vol 53 (1) ◽  
pp. 365-393 ◽  
Author(s):  
Xin Gao ◽  
Federico Nardari
Keyword(s):  
Asset Allocation ◽  
Economic Value ◽  
Short Selling ◽  
Investment Strategies ◽  
Time Varying ◽  
Moderate Risk ◽  
Asset Return ◽  
Out Of Sample ◽  
New Evidence ◽  
Mean Variance

We conduct a comprehensive out-of-sample assessment of the economic value adding of commodities in multiasset investment strategies that exploit the predictability of asset return moments. We find that predictability makes the inclusion of commodities profitable even when short selling and high leverage are not permitted. For instance, a mean-variance (non-mean-variance) investor with moderate risk aversion and leverage, rebalancing quarterly, would be willing to pay up to 108 (155) basis points per year after transaction cost for adding commodities to her stock, bond, and cash portfolio. Previous research had reached mixed or even opposite conclusions, especially in an out-of-sample context.

scholarly journals Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 146
Author(s):  
Aleksei Vakhnin ◽  
Evgenii Sopov
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithms ◽  
Numerical Experiments ◽  
Large Scale ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fixed Number ◽  
High Dimensional ◽  
Cooperative Coevolution ◽  
Large Scale Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

scholarly journals A Bayesian approach for quantile optimization problems with high-dimensional uncertainty sources

2021 ◽  
Vol 376 ◽  
pp. 113632
Author(s):  
Christian Sabater ◽  
Olivier Le Maître ◽  
Pietro Marco Congedo ◽  
Stefan Görtz
Keyword(s):  
Bayesian Approach ◽  
Optimization Problems ◽  
High Dimensional ◽  
Uncertainty Sources

Optimization of Cold-Formed Channel Sections Using Imperialist Competitive Algorithm

2012 ◽  
Vol 166-169 ◽  
pp. 493-496
Author(s):  
Roya Kohandel ◽  
Behzad Abdi ◽  
Poi Ngian Shek ◽  
M.Md. Tahir ◽  
Ahmad Beng Hong Kueh
Keyword(s):  
Sequential Quadratic Programming ◽  
Optimization Problems ◽  
Imperialist Competitive Algorithm ◽  
Computational Method ◽  
Compression Force ◽  
Sqp Method ◽  
Competitive Algorithm ◽  
Different Types ◽  
Formed Channel ◽  
Good Agreement

The Imperialist Competitive Algorithm (ICA) is a novel computational method based on the concept of socio-political motivated strategy, which is usually used to solve different types of optimization problems. This paper presents the optimization of cold-formed channel section subjected to axial compression force utilizing the ICA method. The results are then compared to the Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) algorithm for validation purpose. The results obtained from the ICA method is in good agreement with the GA and SQP method in terms of weight but slightly different in the geometry shape.

FAST METHODS FOR LARGE-SCALE NON-ELLIPTICAL PORTFOLIO OPTIMIZATION

2014 ◽  
Vol 09 (02) ◽  
pp. 1440001 ◽  
Author(s):  
MARC S. PAOLELLA
Keyword(s):  
Portfolio Optimization ◽  
Large Scale ◽  
Asset Returns ◽  
Short Selling ◽  
Sharpe Ratio ◽  
Mixture Of Normals ◽  
Optimization Via Simulation ◽  
Fast Methods ◽  
Out Of Sample ◽  
Weighted Portfolio

Simple, fast methods for modeling the portfolio distribution corresponding to a non-elliptical, leptokurtic, asymmetric, and conditionally heteroskedastic set of asset returns are entertained. Portfolio optimization via simulation is demonstrated, and its benefits are discussed. An augmented mixture of normals model is shown to be superior to both standard (no short selling) Markowitz and the equally weighted portfolio in terms of out of sample returns and Sharpe ratio performance.

Improving Mean Variance Optimization through Sparse Hedging Restrictions

2015 ◽  
Vol 50 (6) ◽  
pp. 1415-1441 ◽  
Author(s):  
Shingo Goto ◽  
Yan Xu
Keyword(s):  
Covariance Matrix ◽  
Factor Model ◽  
Risk Minimization ◽  
Portfolio Risk ◽  
Finite Samples ◽  
Inverse Covariance Matrix ◽  
Out Of Sample ◽  
Nonnegativity Constraints ◽  
Mean Variance ◽  
Equal Weighting

AbstractIn portfolio risk minimization, the inverse covariance matrix prescribes the hedge trades in which a stock is hedged by all the other stocks in the portfolio. In practice with finite samples, however, multicollinearity makes the hedge trades too unstable and unreliable. By shrinking trade sizes and reducing the number of stocks in each hedge trade, we propose a “sparse” estimator of the inverse covariance matrix. Comparing favorably with other methods (equal weighting, shrunk covariance matrix, industry factor model, nonnegativity constraints), a portfolio formed on the proposed estimator achieves significant out-of-sample risk reduction and improves certainty equivalent returns after transaction costs.

Preservation of Additive Convexity and Its Applications in Stochastic Optimization Problems

2021 ◽  
Author(s):  
Xiting Gong ◽  
Tong Wang
Keyword(s):  
Stochastic Optimization ◽  
Optimization Problems ◽  
High Dimensional ◽  
Value Functions

Preservation Results for Proving Additively Convex Value Functions for High-Dimensional Stochastic Optimization Problems

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