Empirical analysis of asymmetric long memory volatility models in value-at-risk estimation

2010 ◽  
Vol 13 (1) ◽  
pp. 55-128 ◽  
Author(s):  
Zouheir Mighri ◽  
Khaled Mokni ◽  
Faysal Mansouri
Keyword(s):  
At Risk ◽  
Empirical Analysis ◽  
Long Memory ◽  
Value At Risk ◽  
Risk Estimation ◽  
Volatility Models

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.

Value-at-Risk estimation of energy commodities: A long-memory GARCH–EVT approach

2015 ◽  
Vol 51 ◽  
pp. 99-110 ◽  
Author(s):  
Manel Youssef ◽  
Lotfi Belkacem ◽  
Khaled Mokni
Keyword(s):  
At Risk ◽  
Long Memory ◽  
Value At Risk ◽  
Risk Estimation
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