Risk analysis
Rasool Roozegar; Samane Arkia
Abstract
Purpose: We have introduced the two-sided Lomax-GARCH (TSLx-GARCH) model. We have used this model to create a more realistic value-at-risk value index than other distributions for all confidence levels. We find this index for applied data.Methodology: In this study, a new flexible distribution for GARCH ...
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Purpose: We have introduced the two-sided Lomax-GARCH (TSLx-GARCH) model. We have used this model to create a more realistic value-at-risk value index than other distributions for all confidence levels. We find this index for applied data.Methodology: In this study, a new flexible distribution for GARCH models in predicting the value at risk is presented. Accurate modeling of financial returns requires proper innovation distribution.Findings: Experimental results show that the GJR-GARCH model, with its innovative TSLx distribution, generates realistic value index predictions, realistic normal distribution, t-student and generalized error distributions for all levels of confidence. The proposed distribution flexibility opens up an opportunity to increase the accuracy of financial return modeling in GARCH models.Originality/Value: We have used the TSLx-GARCH in data modeling and simulation and find both skewness and excess elongation in the financial return series and confidence levels for all levels.