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EconoQuantum
On-line version ISSN 2007-9869Print version ISSN 1870-6622
Abstract
SERRANO BAUTISTA, Ramona and MATA MATA, Leovardo. A conditional heteroscedastic VaR approach with alternative distributions. EconoQuantum [online]. 2020, vol.17, n.2, pp.81-98. Epub Nov 18, 2020. ISSN 2007-9869. https://doi.org/10.18381/eq.v17i2.7125.
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.
Keywords : VaR; GARCH; Stable distribution; Generalized Skew t distribution; Normal Inverse Gaussian distribution; G17; C22; C53.