Estimation of Value at Risk by Using GJR-GARCH Copula Based on Block Maxima


  • Hasna Afifah Rusyda Actuarial Science, Department of Statistics, Padjadjaran University, Indonesia
  • Fajar Indrayatna Actuarial Science, Department of Statistics, Universitas Padjadjaran, Indonesia
  • Lienda Noviyanti Actuarial Science, Department of Statistics, Universitas Padjadjaran, Indonesia



copula-based asymmetric, GJR-GARCH, GEV distributed, value at risk


This paper will discuss the risk estimation of a portfolio based on value at risk (VaR) using a copula-based asymmetric Glosten – Jagannathan – Runkle - Generalized Autoregressive Conditional Heteroskedasticity (GJR-GARCH). There is non-linear correlation for dependent model structure among the variables that lead to the inaccurate VaR estimation so that we use copula functions to model the joint probability of large market movements. Data is GEV distributed. Therefore, we use Block Maxima consisting of fitting an extreme value distribution as a tail distribution to count VaR. The results show VaR can estimate the risk of portfolio return reasonably because the model has captured the data properties. Data volatility can be accommodated by GJR-GARCH, Copula can capture dependence between stocks, and Block maxima can accommodate extreme tail behavior of the data.


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How to Cite

Rusyda, H. A. ., Indrayatna, F. ., & Noviyanti, L. . (2021). Estimation of Value at Risk by Using GJR-GARCH Copula Based on Block Maxima. Indonesian Journal of Statistics and Its Applications, 5(2), 405–414.