Mean-conditional value at risk model for the stochastic project scheduling problem

2020 ◽  
Vol 142 ◽  
pp. 106356 ◽  
Author(s):  
Fatemeh Rezaei ◽  
Amir Abbas Najafi ◽  
Reza Ramezanian
Keyword(s):  
At Risk ◽  
Project Scheduling ◽  
Value At Risk ◽  
Risk Model ◽  
Conditional Value At Risk ◽  
Scheduling Problem ◽  
Project Scheduling Problem ◽  
Stochastic Project Scheduling

scholarly journals Relative Average Deviation as Measure of Robustness in the Stochastic Project Scheduling Problem

2019 ◽  
Vol 28 (52) ◽  
pp. 77-97
Author(s):  
Néstor Raúl Ortiz-Pimiento ◽  
Francisco Javier Díaz-Serna
Keyword(s):  
Project Scheduling ◽  
Scheduling Problem ◽  
Average Deviation ◽  
Traditional Concept ◽  
The Real ◽  
Simulation Process ◽  
Solution Robustness ◽  
Project Scheduling Problem ◽  
Stochastic Project Scheduling

In the Project Scheduling Problem (PSP), the solution robustness can be understood as the capacity that a baseline has to support the disruptions generated by unplanned events (risks). A robust baseline of the project can be obtained from redundancy based methods, which are considered proactive methods to solve the stochastic project scheduling problem.  In this research, three redundancy based methods are evaluated and their performance is compared in terms of robustness. These methods add extra time to the original activities duration in order to face the eventualities that may appear during the project execution. In this article a new indicator to analyze the solution robustness to the Project Scheduling Problem with random duration of activities is proposed. This indicator called Relative Average Deviation (RAD) is defined as the margin of deviation of the activities’ start times in relation to their durations. The RAD is based in a traditional concept that seeks to minimize the value of the differences between the planned start times and the real executed start times. The planned start times were obtained from the project baseline generated by each redundancy based method and the real executed start times were obtained from a simulation process based on Monte Carlo technique. The new indicator was used to evaluate the robustness of three baselines generated by different methods but applied to the same case study. Finally, the results suggest that the Relative Average Deviation (RAD) facilitates the interpretation of the robustness concept because it focuses on analyzing the deviation margin associated with an activity.

USING EXTREME VALUE THEORY TO EVALUATE CONDITIONAL VAR FOR RISK MANAGEMENT IN ELECTRICITY MARKETS

2016 ◽  
Vol 78 (10) ◽  
Author(s):  
M. T. Askari ◽  
Z. Afzalipor ◽  
A. Amoozadeh
Keyword(s):  
At Risk ◽  
Electricity Markets ◽  
Extreme Value Theory ◽  
Value At Risk ◽  
Risk Model ◽  
Test System ◽  
Value Theory ◽  
Extreme Value ◽  
Conditional Value At Risk ◽  
Bidding Strategy

In a deregulated power market, generation companies attempt to maximize their profits and minimize their risks. This paper proposes a risk model for bidding strategy of generation companies based on EVT-CVaR method. Extreme Value Theory can overcome shortcomings of traditional methods in computing financial risk based on value-at-risk and conditional value-at-risk method. Also, generalized Pareto distribution is suggested to model tail of an unknown distribution and parameters of the GPD are estimated by likelihood moment method. Numerical results for risk assessment using the proposed approach are presented for IEEE 30-bus test system. According to the findings, this method can be used as a robust technique to calculate the risk for bidding strategy of generation companies.

scholarly journals Risk Analysis through the Half-Normal Distribution

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2080
Author(s):  
Maria-Teresa Bosch-Badia ◽  
Joan Montllor-Serrats ◽  
Maria-Antonia Tarrazon-Rodon
Keyword(s):  
At Risk ◽  
Risk Analysis ◽  
Normal Distribution ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Model ◽  
Risk Measures ◽  
Risk Indicators ◽  
Conditional Value At Risk ◽  
Risk Matrices

We study the applicability of the half-normal distribution to the probability–severity risk analysis traditionally performed through risk matrices and continuous probability–consequence diagrams (CPCDs). To this end, we develop a model that adapts the financial risk measures Value-at-Risk (VaR) and Conditional Value at Risk (CVaR) to risky scenarios that face only negative impacts. This model leads to three risk indicators: The Hazards Index-at-Risk (HIaR), the Expected Hazards Damage (EHD), and the Conditional HIaR (CHIaR). HIaR measures the expected highest hazards impact under a certain probability, while EHD consists of the expected impact that stems from truncating the half-normal distribution at the HIaR point. CHIaR, in turn, measures the expected damage in the case it exceeds the HIaR. Therefore, the Truncated Risk Model that we develop generates a measure for hazards expectations (EHD) and another measure for hazards surprises (CHIaR). Our analysis includes deduction of the mathematical functions that relate HIaR, EHD, and CHIaR to one another as well as the expected loss estimated by risk matrices. By extending the model to the generalised half-normal distribution, we incorporate a shape parameter into the model that can be interpreted as a hazard aversion coefficient.

scholarly journals Measuring Systemic Risk of Banking in Indonesia: Conditional Value at Risk Model Application

2017 ◽  
Vol 6 (2) ◽  
pp. 301-318
Author(s):  
Harjum Muharam ◽  
Erwin Erwin
Keyword(s):  
At Risk ◽  
Financial Institutions ◽  
Systemic Risk ◽  
Value At Risk ◽  
Risk Model ◽  
Financial System ◽  
Conditional Value At Risk ◽  
Spearman Correlation ◽  
Weak Correlation ◽  
Model Application

Systemic risk is a risk of collapse of the financial system that would cause the financial system is not functioning properly. Measurement of systemic risk in the financial institutions, especially banks are crucial, because banks are highly vulnerable to financial crisis. In this study, to estimate the conditional value-at-risk (CoVaR) used quantile regression. Samples in this study of 9 banks have total assets of the largest in Indonesia. Testing the correlation between VaR and ΔCoVaR in this study using Spearman correlation and Kendall's Tau. There are five banks that have a significant correlation between VaR and ΔCoVaR, meanwhile four others banks in the sample did not have a significant correlation. However, the correlation coefficient is below 0.50, which indicates that there is a weak correlation between VaR and CoVaR.DOI: 10.15408/sjie.v6i2.5296

Weather risk assessment of Indian power sector: A conditional value-at-risk approach

2018 ◽  
Vol 30 (4) ◽  
pp. 641-661
Author(s):  
Mahuya Basu ◽  
Tanupa Chakraborty
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Model ◽  
Conditional Value At Risk ◽  
Weather Data ◽  
Power Sector ◽  
Damodar Valley ◽  
Weather Risk ◽  
Risk Approach ◽  
Indian Power Sector

This paper aims to assess the weather risk exposure of Indian power sector from both generation and demand sides. The study considers two representative firms – firstly, Damodar Valley Corporation (DVC), a hydro-generator, to assess its rainfall exposure, and secondly, Calcutta Electric Supply Corporation (CESC), a retail power supplier, to assess the temperature sensitivity of power demand. The study opts for ‘Value at Risk’ approach, which combines both the sensitivity of power variables towards weather variable and the probability of weather change. The sensitivity is measured using regression analysis with autoregressive distributed lag (ARDL). Parametric distributions are fitted to weather data to assess probabilities. Due to the ‘fat-tail’ characteristic of the fitted distribution, a ‘conditional value-at-risk’ model is considered more effective. The study reveals that the hydroelectricity generation is highly exposed to monsoon rainfall fluctuation and hence the hydro-generator may experience substantial loss of revenue due to insufficient monsoon, whereas the revenue of retail power distributor is moderately exposed to fluctuation of daily surface temperature.

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