ESTIMASI VALUE AT RISK (VAR) PORTOFOLIO DENGAN METODE GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (GARCH)-VINE COPULA (Studi Kasus : Saham – Saham yang Tergabung dalam Jakarta Islamic Index Periode 1 Januari 2016–30 Juni 2020)

Investing in the financial sector is an investment that is in great demand by investors, one of which is stock investment. In investing, of course, there is a risk, where the investor doesn’t know whether the investment made gains or losses. To reduce the level of risk that will be accepted, an investment is made in the form of a portfolio. One of the measuring tools used to calculate portfolio risk is Value at Risk (VaR). The VaR calculation method assumes a return normally distributed. But in reality, financial data is rarely found in the normal distribution and dependence between stocks is often not linear. Therefore, in this study the GARCH-Vine Copula method was used to estimate VaR. Vine Copula is a multivariate distribution function that combines the univariate marginal return distribution in the portfolio, as well as describing the structure of its non-linear dependence. This study used Copula from the Archimedean family. Financial data usually tends to have high volatility or it is said that data contains elements of heteroscedasticity. To overcome the heteroscedasticity element, the GARCH model is used. If the results of GARCH modeling are not normally distributed, then modeling with Vine Copula will be continued. In this study, estimation of portfolio VaR using the GARCH - Vine Copula method was carried out on 4 stocks incorporated in the Jakarta Islamic Index for the period January 1, 2016 - June 30, 2020, namely ICBP, INCO, INDF, and UNVR stocks. Results of the calculations in the study is derived models Gumbel C-Vine Copula is the best model to model data. Obtained VaR estimates of 1.76%, 2.39%, 4.1% of investment funds at the confidence level 90%, 95%, 99%