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.

Vulnerability to food insecurity in rural Punjab, Pakistan

2018 ◽  
Vol 120 (9) ◽  
pp. 2088-2101 ◽  
Author(s):  
Muhammad Khalid Bashir ◽  
Steven Schilizzi ◽  
Rohan Sadler ◽  
Ghaffar Ali
Keyword(s):  
At Risk ◽  
Food Insecurity ◽  
Value At Risk ◽  
Security Analysis ◽  
Assessment Method ◽  
Conditional Value At Risk ◽  
Primary Data ◽  
Content Type ◽  
Period Data ◽  
Food Deficiency

Purpose The purpose of this paper is to measure the vulnerability to food insecurity in rural Punjab, Pakistan. Design/methodology/approach Primary data of 1,152 households were collected. The extent of food deficiency was measured using dietary intake assessment method (seven days). Value at Risk (VaR) and conditional Value at Risk (cVaR), a method widely used for risk analysis in financial institutes, were applied to assess the vulnerability to food insecurity. Findings In total, 23 percent of the sample households were measured as food deficient. The VaR and cVaR results identified that the lowest 3 percentiles (up to 30 percent) were at risk to become food deficient without any seasonal shortages. In case of shocks, up till sixth percentiles (60 percent) will be as at risk of food deficiency. This study suggests that multi-period data, at least quarterly, are required to predict vulnerability. It is suggested that a blanket policy is not a good approach. Once the most vulnerable households are identified, a targeted approach must be opted. Originality/value Generalizing the results of one week’s calorie calculations may produce biased results that may mislead the policy process. A multi-period data collection is costly and cumbersome. The application of VaR and cVaR helps overcome this issue. Furthermore, this is one of the initial studies to apply these methods to food security analysis.

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

Examining the evidence of risk spillovers between Shanghai and London non-ferrous futures markets: a dynamic Copula-CoVaR approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hong Shen ◽  
Yue Tang ◽  
Ying Xing ◽  
Pin Ng
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Global Economy ◽  
Futures Markets ◽  
Ferrous Metal ◽  
Conditional Value At Risk ◽  
Metal Exchange ◽  
London Metal Exchange ◽  
Content Type ◽  
Dynamic Copula

PurposeThis paper aims to examine the evidence of risk spillovers between Shanghai and London non-ferrous futures markets using a dynamic Copula-CoVaR approach.Design/methodology/approachWith daily data, the marginal distributions and optimal Copula functions are determined using the kernel estimation method and squared Euclidean distance test. The conditional value-at-risk and the conditional value-at-risk spillover rate are computed from the Copula estimated parameters based on the Copula-CoVaR model. Also, the dynamic correlation coefficient between the two futures markets is investigated.FindingsThe empirical results are as follows: overall, the risk spillover effect exerted by the London Metal Exchange on the Shanghai Futures Exchange is more significant than vice versa. Moreover, the degree of risk spillovers exerted by the London Metal Exchange on the Shanghai Futures Exchange for zinc and copper are more significant when they are depressed in the London Metal Exchange. Moreover, the dynamic of the correlation between the Shanghai and London futures markets is attributed to be largely due to changes in the global economy.Research limitations/implicationsThe Copula-CoVaR model used in this paper is suitable for measuring the risk spillovers between two different markets, while the risk spillovers across multiple markets or the consideration of multiple risk factors cannot be accurately captured using this framework. Multiple state variables to capture time variation in the conditional moments of return series will be a topic in future research.Practical implicationsThe results provide theoretical support for risk management and monitoring of the non-ferrous futures markets.Originality/valueThe ability of the Copula function to accurately describe a nonlinear relationship and tail correlation is harnessed to measure the risk spillovers, explore the degree and direction of risk spillovers and identify the source of risk spillovers. The global economy is incorporated as a macro factor to explore its inner connection with the dynamic of risk spillovers in the non-ferrous metal futures market.

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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