The assumption of normality in calculating value at risk
DOI:
https://doi.org/10.29201/peipn.v1i1.233Keywords:
Value at risk, Normal distribution, Skewness, KurtosisAbstract
This paper shows that it is necessary to use different methods from the one proposed by RiskMetrics™ to calculate VaR because mexican stock market returns are not normally distributed. Firstly original distributions got transformed following the RiskMetrics™ suggestion. The EWMA model gives a better approximation to the normal distribution than the constant volatility model does. In addition, some models such as mixed normal distributions, the hyperbolic model, and the estimating functions model are tested to calculate VaR. The last one is the best model to measure extreme values in the stock distributions. Finally, a comparison among the models is presented in a box.
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