A multiobjective conditional Value-at-Risk model in time interval for loan portfolios

2008 ◽  
Vol 3 (4) ◽  
pp. 253-265 ◽  
Author(s):  
Min Jiang ◽  
Zhiqing Meng ◽  
Chuangyin Dang
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Model ◽  
Conditional Value At Risk ◽  
Time Interval ◽  
Loan Portfolios

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.

scholarly journals A conditional value-at-risk model for insurance products with a guarantee

2009 ◽  
Vol 11 (1/2) ◽  
pp. 122 ◽  
Author(s):  
Andrea Consiglio ◽  
Antonio Pecorella ◽  
Stavros A. Zenios
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Model ◽  
Conditional Value At Risk

A risk model to compute the volatility and the need for collateral margins in energy futures contracts in Brazil

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pedro Argento ◽  
Marcelo Cabus Klotzle ◽  
Antonio Carlos Figueiredo Pinto ◽  
Leonardo Lima Gomes
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Model ◽  
Extreme Values ◽  
Safety Margin ◽  
Conditional Value At Risk ◽  
Futures Contracts ◽  
Content Type ◽  
Energy Futures ◽  
Risk Dimensions

Purpose Brazil is characterized by the inexistence of a more robust system of guarantees and rules to minimize risks and protect agents in energy futures contracts. In this sense, this study aims to answer the question of how a centralized clearing agent can compute safety margin requirements to help reduce the systemic risk of the energy futures contracts market in Brazil. Design/methodology/approach The intermediate steps and specific objectives are to analyze the volatility behavior, identify the autoregressive conditional heteroscedasticity effects and model the variance of the return series. Based on this, the authors calculate the value-at-risk and conditional value-at-risk metrics for the energy futures contracts. As a robustness test, the authors added a peak over threshold methodology from extreme values theory. Findings In general, monthly products require margins because of their higher variance. With the asymmetrical distribution of returns, the authors needed to consider different maintenance margins for the long and short positions. It was also shown that two guarantee margins were required to secure the contracts as follows: the initial margin and the maintenance margin. The three factors that defined the size of the maintenance margin the volatility, skewness and kurtosis of the return series. Originality/value The contribution of this study lies in promoting the understanding of the risk dimensions of the energy derivatives market in Brazil and it offers concrete recommendations for how to mitigate this risk through market mechanisms and structures. Similar arrangements can be applied to other emerging markets.

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