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Value-at-risk estimation with new skew extension of generalized normal distribution

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  • Emrah Altun
  • Huseyin Tatlidil
  • Gamze Ozel

Abstract

In this paper, we introduce a new distribution, called the alpha-skew generalized normal (ASGN), for GARCH models in modeling daily Value-at-Risk (VaR). Basic structural properties of the proposed distribution are derived including probability and cumulative density functions, moments and stochastic representation. The real data application based on ISE-100 index is given to show the performance of GARCH model specified under ASGN innovation distribution with respect to normal, Student’s-t, skew normal and generalized normal models in terms of the VaR accuracy. The empirical results show that GARCH model with ASGN innovation distribution generates the most accurate VaR forecasts for all confidence levels.

Suggested Citation

  • Emrah Altun & Huseyin Tatlidil & Gamze Ozel, 2019. "Value-at-risk estimation with new skew extension of generalized normal distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(14), pages 3663-3681, July.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:14:p:3663-3681
    DOI: 10.1080/03610926.2018.1481970
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    Cited by:

    1. Kim, SungBum & Kim, Hyoung-Moon, 2022. "Series form of the characteristic functions of scale mixtures of multivariate skew-normal distributions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 198(C), pages 172-187.

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