Value-at-Risk Estimation of Carbon Spot Market Based on the Combined GARCH-EVT-VaR Model

2014 ◽  
Vol 1065-1069 ◽  
pp. 3250-3253 ◽  
Author(s):  
Jing Jing Jiang ◽  
Bin Ye
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Model ◽  
Risk Estimation ◽  
Emission Trading ◽  
Price Volatility ◽  
Spot Market ◽  
Value Theory ◽  
Var Model ◽  
The European Union

Based on the analysis of the dynamics of carbon price volatility, this article proposes to develop a combined extreme value theory and conditional variance based Value-at-Risk model (GARCH-EVT-VaR) for short-term risk measurement and estimation of the carbon spot market under the European Union Emission Trading Scheme (EU ETS). The model is implied to the EUA spot market and compared with the traditional GARCH-VaR model, the empirical results show that the GARCH based model underestimates market risks by overlooking the great price shocks, but the GARCH-EVT based model has the ability to take those extreme jumps into its risk estimations.

scholarly journals Dynamic value at risk estimation for BELEX15

2011 ◽  
Vol 8 (1) ◽  
Author(s):  
Emilija Nikolić-Đorić ◽  
Dragan Đorić
Keyword(s):  
At Risk ◽  
Stock Returns ◽  
Long Memory ◽  
Value At Risk ◽  
Stock Exchange ◽  
Risk Estimation ◽  
Value Theory ◽  
Garch Models ◽  
Measurement Models ◽  
Standardized Residuals

This paper uses RiskMetrics, GARCH and IGARCH models to calculate daily VaR for Belgrade Stock Exchange index BELEX15 returns based on the normal and Student t innovation distribution. In the case of GARCH and IGARCH models VaR values are obtained applying Extreme Value Theory on the standardized residuals. The Kupiec's LR statistics was used to test the accuracy of risk measurement models. The main conclusions are: (1) when modelling value-at-risk it is very important to have a good model for volatility of stock returns; (2) both stationary and integrated GARCH models outperform RiskMetrics in estimating VaR; (3) although long memory volatility is present in the BELEX15 index, IGARCH models cannot outperform GARCH type models in VaR evaluations for this index.

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 Return distribution and value at risk estimation for BELEX15

2011 ◽  
Vol 21 (1) ◽  
pp. 103-118 ◽  
Author(s):  
Dragan Djoric ◽  
Emilija Nikolic-Djoric
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Risk Estimation ◽  
Var Model ◽  
Ratio Test ◽  
Sample Period ◽  
Return Distribution ◽  
Model Accuracy ◽  
Confidence Levels

The aim of this paper is to find distributions that adequately describe returns of the Belgrade Stock Exchange index BELEX15. The sample period covers 1067 trading days from 4 October 2005 to 25 December 2009. The obtained models were considered in estimating Value at Risk ( VaR ) at various confidence levels. Evaluation of VaR model accuracy was based on Kupiec likelihood ratio test.

scholarly journals Methods for Computing Numerical Standard Errors: Review and Application to Value-at-Risk Estimation

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
David Ardia ◽  
Keven Bluteau ◽  
Lennart F. Hoogerheide
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Simulation Experiment ◽  
Risk Model ◽  
Risk Measure ◽  
Risk Estimation ◽  
Standard Errors ◽  
Monte Carlo Experiments ◽  
Simulation Based ◽  
Underlying Risk

Abstract Numerical standard error (NSE) is an estimate of the standard deviation of a simulation result if the simulation experiment were to be repeated many times. We review standard methods for computing NSE and perform a Monte Carlo experiments to compare their performance in the case of high/extreme autocorrelation. In particular, we propose an application to risk management where we assess the precision of the value-at-risk measure when the underlying risk model is estimated by simulation-based methods. Overall, heteroscedasticity and autocorrelation estimators with prewhitening perform best in the presence of large/extreme autocorrelation.

scholarly journals The role of distribution and volatility specification in value at risk estimation: Evidence from the Johannesburg Stock Exchange

2012 ◽  
Vol 5 (2) ◽  
pp. 515-526
Author(s):  
John M. Mwamba ◽  
Kruger Pretorius
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Risk Estimation ◽  
Value Theory ◽  
Johannesburg Stock Exchange ◽  
Volatility Models ◽  
Student’S T ◽  
T Distribution

Given the volatile nature of global financial markets, managing as well as predicting financial risk plays an increasingly important role in banking and finance. The Value at Risk (VaR) measure has emerged as the most prominent measure of downside market risk. It is measured as the alpha quantile of the profit and loss distribution. Recently a number of distributions have been proposed to model VaR: these include the extreme value theory distributions (EVT), Generalized Error Distribution (GED), Student’s t, and normal distribution. Furthermore, asymmetric as well as symmetric volatility models are combined with these distributions for out-sample VaR forecasts. This paper assesses the role of the distribution assumption and volatility specification in the accuracy of VaR estimates using daily closing prices of the Johannesburg Stock Exchange All Share Index (JSE ALSI). It is found that Student’s t distribution combined with asymmetric volatility models produces VaR estimates in out-sample periods that outperform those from models stemming from normal, EVT/symmetric volatility specification.

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