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

Altun E., Tatlidil H., Ozel G.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, vol.48, no.14, pp.3663-3681, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 48 Issue: 14
  • Publication Date: 2019
  • Doi Number: 10.1080/03610926.2018.1481970
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.3663-3681
  • Keywords: GARCH model, alpha-skew generalized normal, value-at-risk, volatility, VARIANCE, KURTOSIS, MODELS
  • Hacettepe University Affiliated: Yes


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.