Open AccessEstimation of value at risk by using gjr-garch copula based on block maxima
Author(s) -
Hasna Afifah Rusyda,
Fajar Indrayatna,
Lienda Noviyanti
Publication year - 2021
Publication title -
indonesian journal of statistics and applications
Language(s) - English
Resource type - Journals
ISSN - 2599-0802
DOI - 10.29244/ijsa.v5i2p405-414
Subject(s) - copula (linguistics) , autoregressive conditional heteroskedasticity , value at risk , econometrics , autoregressive model , extreme value theory , maxima , volatility (finance) , portfolio , heteroscedasticity , joint probability distribution , mathematics , marginal distribution , economics , statistics , financial economics , risk management , random variable , finance , art , performance art , art history
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.