scholarly journals Quantile-based optimal portfolio selection

2021 ◽  
Author(s):  
Taras Bodnar ◽  
Mathias Lindholm ◽  
Erik Thorsén ◽  
Joanna Tyrcha
Keyword(s):  
Portfolio Selection ◽  
Value At Risk ◽  
Risk Measure ◽  
Optimal Portfolio ◽  
Conditional Value At Risk ◽  
Closed Form Expression ◽  
Selection Strategy ◽  
Optimal Portfolio Selection ◽  
Sufficient And Necessary Conditions ◽  
Elliptically Contoured

AbstractIn this paper the concept of quantile-based optimal portfolio selection is introduced and a specific portfolio connected to it, the conditional value-of-return (CVoR) portfolio, is proposed. The CVoR is defined as the mean excess return or the conditional value-at-risk (CVaR) of the return distribution. The portfolio selection consists solely of quantile-based risk and return measures. Financial institutions that work in the context of Basel 4 use CVaR as a risk measure. In this regulatory framework sufficient and necessary conditions for optimality of the CVoR portfolio are provided under a general distributional assumption. Moreover, it is shown that the CVoR portfolio is mean-variance efficient when the returns are assumed to follow an elliptically contoured distribution. Under this assumption the closed-form expression for the weights and characteristics of the CVoR portfolio are obtained. Finally, the introduced methods are illustrated in an empirical study based on monthly data of returns on stocks included in the S&P index. It is shown that the new portfolio selection strategy outperforms several alternatives in terms of the final investor wealth.

scholarly journals Simulation-Based Optimal Portfolio Selection Strategy—Evidence from Asian Markets

2018 ◽  
Vol 5 (5) ◽  
pp. 1
Author(s):  
Longqing Li
Keyword(s):  
At Risk ◽  
Portfolio Optimization ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Optimal Portfolio ◽  
Conditional Value At Risk ◽  
Selection Strategy ◽  
Optimal Portfolio Selection ◽  
T Distribution ◽  
Skewed T Distribution

Recently portfolio optimization has become widely popular in risk management, and the common practice is to use mean-variance or Value-at-Risk (VaR), despite the VaR being incoherent risk measure because of the lack of subadditivity. This has led to the emergence of the conditional value-at-risk (CVaR) approach, consequently, a gradual development of mean-CVaR portfolio optimization. To seek an optimal portfolio selection strategy and increase the robustness of the result, the paper studies the performance of portfolio optimization in Asian markets using a Monte-Carlo simulation tool, creates a variety of randomly selected portfolios that consists of Asian ADRs listed in NYSE from 2011 to 2016, and applies both optimization frameworks with different skewed fat-tailed distributions, including the Generalized Hyperbolic (GH) and skewed-T distribution. The main result shows that the Generalized Hyperbolic distribution produces the lowest risk under a given rate of return, while the skewed-T distribution creates a diversification allocation outcome similar to that of historical simulation.

scholarly journals Simulation-Based Optimal Portfolio Selection Strategy—Evidence from Asian Markets

2018 ◽  
Vol 5 (5) ◽  
pp. 1
Author(s):  
Longqing Li
Keyword(s):  
At Risk ◽  
Portfolio Optimization ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Optimal Portfolio ◽  
Conditional Value At Risk ◽  
Selection Strategy ◽  
Optimal Portfolio Selection ◽  
T Distribution ◽  
Skewed T Distribution

Recently portfolio optimization has become widely popular in risk management, and the common practice is to use mean-variance or Value-at-Risk (VaR), despite the VaR being incoherent risk measure because of the lack of subadditivity. This has led to the emergence of the conditional value-at-risk (CVaR) approach, consequently, a gradual development of mean-CVaR portfolio optimization. To seek an optimal portfolio selection strategy and increase the robustness of the result, the paper studies the performance of portfolio optimization in Asian markets using a Monte-Carlo simulation tool, creates a variety of randomly selected portfolios that consists of Asian ADRs listed in NYSE from 2011 to 2016, and applies both optimization frameworks with different skewed fat-tailed distributions, including the Generalized Hyperbolic (GH) and skewed-T distribution. The main result shows that the Generalized Hyperbolic distribution produces the lowest risk under a given rate of return, while the skewed-T distribution creates a diversification allocation outcome similar to that of historical simulation.

Optimal portfolio selection in a Value-at-Risk framework

2001 ◽  
Vol 25 (9) ◽  
pp. 1789-1804 ◽  
Author(s):  
Rachel Campbell ◽  
Ronald Huisman ◽  
Kees Koedijk
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Optimal Portfolio ◽  
A Value ◽  
Risk Framework ◽  
Optimal Portfolio Selection

scholarly journals Problem-driven scenario generation: an analytical approach for stochastic programs with tail risk measure

2019 ◽  
Author(s):  
Jamie Fairbrother ◽  
Amanda Turner ◽  
Stein W. Wallace
Keyword(s):  
Portfolio Selection ◽  
Random Vector ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Monte Carlo Sampling ◽  
Conditional Value At Risk ◽  
Scenario Generation ◽  
Tail Risk ◽  
Stochastic Programs

AbstractScenario generation is the construction of a discrete random vector to represent parameters of uncertain values in a stochastic program. Most approaches to scenario generation are distribution-driven, that is, they attempt to construct a random vector which captures well in a probabilistic sense the uncertainty. On the other hand, a problem-driven approach may be able to exploit the structure of a problem to provide a more concise representation of the uncertainty. In this paper we propose an analytic approach to problem-driven scenario generation. This approach applies to stochastic programs where a tail risk measure, such as conditional value-at-risk, is applied to a loss function. Since tail risk measures only depend on the upper tail of a distribution, standard methods of scenario generation, which typically spread their scenarios evenly across the support of the random vector, struggle to adequately represent tail risk. Our scenario generation approach works by targeting the construction of scenarios in areas of the distribution corresponding to the tails of the loss distributions. We provide conditions under which our approach is consistent with sampling, and as proof-of-concept demonstrate how our approach could be applied to two classes of problem, namely network design and portfolio selection. Numerical tests on the portfolio selection problem demonstrate that our approach yields better and more stable solutions compared to standard Monte Carlo sampling.

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