Project portfolio selection and scheduling optimization based on risk measure: a conditional value at risk approach

2019 ◽  
Vol 285 (1-2) ◽  
pp. 9-33 ◽  
Author(s):  
Vijaya Dixit ◽  
Manoj Kumar Tiwari
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Risk Measure ◽  
Conditional Value At Risk ◽  
Project Portfolio ◽  
Scheduling Optimization ◽  
Project Portfolio Selection ◽  
Risk Approach

scholarly journals Multi-stage stochastic model in portfolio selection problem

Filomat ◽  
2018 ◽  
Vol 32 (3) ◽  
pp. 991-1001
Author(s):  
Shokoofeh Banihashemi ◽  
Ali Azarpour ◽  
Marziye Kaveh
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Selection Problem ◽  
Conditional Value At Risk ◽  
Expected Return ◽  
Portfolio Selection Problem ◽  
Multi Stage

This paper is a novel work of portfolio-selection problem solving using multi objective model considering four parameters, Expected return, downside beta coefficient, semivariance and conditional value at risk at a specified confidence level. Multi-period models can be defined as stochastic models. Early studies on portfolio selection developed using variance as a risk measure; although, theories and practices revealed that variance, considering its downsides, is not a desirable risk measure. To increase accuracy and overcoming negative aspects of variance, downside risk measures like semivarinace, downside beta covariance, value at risk and conditional value at risk was other risk measures that replaced in models. These risk measures all have advantages over variance and previous works using these parameters have shown improvements in the best portfolio selection. Purposed models are solved using genetic algorithm and for the topic completion, numerical example and plots to measure the performance of model in four dimensions are provided.

scholarly journals ESTIMASI NILAI CONDITIONAL VALUE AT RISK MENGGUNAKAN FUNGSI GAUSSIAN COPULA

2015 ◽  
Vol 4 (4) ◽  
pp. 188
Author(s):  
HERLINA HIDAYATI ◽  
KOMANG DHARMAWAN ◽  
I WAYAN SUMARJAYA
Keyword(s):  
At Risk ◽  
Confidence Level ◽  
Value At Risk ◽  
Risk Measure ◽  
Copula Function ◽  
Gaussian Copula ◽  
Nonlinear Dependence ◽  
Conditional Value At Risk ◽  
Dependence Structure ◽  
Measurement Tools

Copula is already widely used in financial assets, especially in risk management. It is due to the ability of copula, to capture the nonlinear dependence structure on multivariate assets. In addition, using copula function doesn’t require the assumption of normal distribution. There fore it is suitable to be applied to financial data. To manage a risk the necessary measurement tools can help mitigate the risks. One measure that can be used to measure risk is Value at Risk (VaR). Although VaR is very popular, it has several weaknesses. To overcome the weakness in VaR, an alternative risk measure called CVaR can be used. The porpose of this study is to estimate CVaR using Gaussian copula. The data we used are the closing price of Facebook and Twitter stocks. The results from the calculation using 90%  confidence level showed that the risk that may be experienced is at 4,7%, for 95% confidence level it is at 6,1%, and for 99% confidence level it is at 10,6%.

scholarly journals ESTIMASI NILAI CONDITIONAL VALUE AT RISK (CVaR) PORTOFOLIO MENGGUNAKAN METODE EVT-GJR-VINE COPULA

2019 ◽  
Vol 8 (1) ◽  
pp. 15
Author(s):  
NI WAYAN UCHI YUSHI ARI SUDINA ◽  
KOMANG DHARMAWAN ◽  
I WAYAN SUMARJAYA
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measure ◽  
Conditional Value At Risk ◽  
Risk Level ◽  
Copula Method ◽  
Vine Copula

Conditional value at risk (CVaR) is widely used in risk measure that takes into account losses exceeding the value at risk level. The aim of this research is to compare the performance of the EVT-GJR-vine copula method and EVT-GARCH-vine copula method in estimating CVaR of the portfolio using backtesting. Based on the backtesting results, it was found that the EVT-GJR-vine copula method have better performance when compared to the EVT-GARCH-vine copula method in estimating the CVaR value of the portfolio. This can be seen from the statistical values ??, and  of EVT-GJR-vine copula method which is generally smaller than the statistical values , and of the EVT-GARCH-vine copula method.

scholarly journals Convex approximations for two-stage mixed-integer mean-risk recourse models with conditional value-at-risk

2019 ◽  
Vol 181 (2) ◽  
pp. 473-507 ◽  
Author(s):  
E. Ruben van Beesten ◽  
Ward Romeijnders
Keyword(s):  
At Risk ◽  
Error Bounds ◽  
Value At Risk ◽  
Risk Measure ◽  
Mixed Integer ◽  
Conditional Value At Risk ◽  
Two Stage ◽  
Special Cases ◽  
Integer Recourse ◽  
Mean Risk

Abstract In traditional two-stage mixed-integer recourse models, the expected value of the total costs is minimized. In order to address risk-averse attitudes of decision makers, we consider a weighted mean-risk objective instead. Conditional value-at-risk is used as our risk measure. Integrality conditions on decision variables make the model non-convex and hence, hard to solve. To tackle this problem, we derive convex approximation models and corresponding error bounds, that depend on the total variations of the density functions of the random right-hand side variables in the model. We show that the error bounds converge to zero if these total variations go to zero. In addition, for the special cases of totally unimodular and simple integer recourse models we derive sharper error bounds.

scholarly journals D6.3 Report on stochastic optimisation for simple problems

2021 ◽  
Author(s):  
Q. Ayoul-Guilmard ◽  
S. Ganesh ◽  
F. Nobile ◽  
R. Rossi ◽  
C. Soriano
Keyword(s):  
At Risk ◽  
High Performance Computing ◽  
Cost Efficiency ◽  
Value At Risk ◽  
High Performance ◽  
Risk Measure ◽  
Conditional Value At Risk ◽  
Theoretical Methods ◽  
Stochastic Optimisation ◽  
Performance Computing

This report addresses the general matter of optimisation under uncertainties, following a previous report on stochastic sensitivities (deliverable 6.2). It describes several theoretical methods, as well their application into implementable algorithms. The specific case of the conditional value at risk chosen as risk measure, with its challenges, is prominently discussed. In particular, the issue of smoothness – or lack thereof – is addressed through several possible approaches. The whole report is written in the context of high-performance computing, with concern for parallelisation and cost-efficiency.

Sign in / Sign up

Export Citation Format

Share Document