Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/239937 
Year of Publication: 
2019
Citation: 
[Journal:] Administrative Sciences [ISSN:] 2076-3387 [Volume:] 9 [Issue:] 2 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-17
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
conditional value at risk
extreme value theory
GARCH models
backtesting models
maximum likelihood method
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
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