文章基本信息

标题：A conditional heteroscedastic VaR approach with alternative distributions
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作者：Ramona Serrano Bautista ; Leovardo Mata Mata
期刊名称：EconoQuantum
印刷版ISSN：1870-6622
出版年度：2020
卷号：17
期号：2
页码：81-98
DOI：10.18381/eq.v17i2.7125
出版社：Universidad de Guadalajara
摘要：Objective: The purpose of this paper is to explore different distributions in conditional Value at Risk (VaR) modeling as an option in the Mexican market. Methodology: We estimate a GARCH model under the Gaussian, Normal Inverse Gaussian, Skew Generalized t and the Stable distribution assumption, then we implement the model in predicting one-day ahead VaR and finally we examine the performance among the four VaR models during a period of high volatility.Results: The backtesting result confirms that the stable-VaR approach outperforms the other models in the VaR’s prediction at 99% confidence level.Limitations: Although the VaR is a widely used risk measure is not a coherent risk measure, for this reason, a natural extension of our work should be to estimate the expected shortfall and this may produce different insights.Conclusions: Our findings reveal that models that consider some empirical characteristic of financial returns such as leptokurtic, volatility clustering and asymmetry improve the VaR predicting capacity. This finding is important in the search more robust approaches for VaR estimates.
关键词：VaR; GARCH; Stable distribution; Generalized Skew t distribution; Normal Inverse Gaussian distribution; G17; C22; C53.
其他关键词：VaR; GARCH; distribución estable; distribución t-student sesgada generalizada; distribución normal inversa gaussiana; G17; C22; C53.