On Conditional Value at Risk (CoVaR) for tail-dependent copulas

Abstract The paper deals with Conditional Value at Risk (CoVaR) for copulas with nontrivial tail dependence. We show that both in the standard and the modified settings, the tail dependence function determines the limiting properties of CoVaR as the conditioning event becomes more extreme. The results are illustrated with examples using the extreme value, conic and truncation invariant families of bivariate tail-dependent copulas.

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

Jurnal Teknologi ◽

10.11113/jt.v78.5744 ◽

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.

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Estimating Conditional Value at Risk in the Tehran Stock Exchange Based on the Extreme Value Theory Using GARCH Models

Administrative Sciences ◽

10.3390/admsci9020040 ◽

2019 ◽

Vol 9 (2) ◽

pp. 40 ◽

Cited By ~ 5

Author(s):

Hamed Tabasi ◽

Vahidreza Yousefi ◽

Jolanta Tamošaitienė ◽

Foroogh Ghasemi

Keyword(s):

At Risk ◽

Distribution Function ◽

Extreme Value Theory ◽

Value At Risk ◽

Stock Exchange ◽

Value Theory ◽

Extreme Value ◽

Likelihood Method ◽

Conditional Value At Risk ◽

Garch Models

This paper attempted to calculate the market risk in the Tehran Stock Exchange by estimating the Conditional Value at Risk. Since the Conditional Value at Risk is a tail-related measure, Extreme Value Theory has been utilized to estimate the risk more accurately. Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models were used to model the volatility-clustering feature, and to estimate the parameters of the model, the Maximum Likelihood method was applied. The results of the study showed that in the estimation of model parameters, assuming T-student distribution function gave better results than the Normal distribution function. The Monte Carlo simulation method was used for backtesting the Conditional Value at Risk model, and in the end, the performance of different models, in the estimation of this measure, was compared.

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