Abstract

Any investment in the stock market will earn returns accompanied by risks. Return and risk has a mutual correlation that equilibrium. The formation of a portfolio is intended to provide a lower risk or with the same risk but provide a higher return. Value at Risk (VaR) is a instrument to analyze risk management. Time series model used in stock return data that it has not normal distribution and heteroscedastisicity is Generalized Autoregressive Conditional Heteroscedasticity (GARCH). GARCH-Copula is a combined method of GARCH and Copula. The Copula method is used in joint distribution modeling because it does not require the assumption of normality of the data and can capture tail dependence between each variable. This research uses return data from stock closing prices of Unilever Indonesia and Kimia Farma period January 1, 2013 until December 31, 2016. Copula model is selected based on the highest likelihood log value is Copula Clayton. Value at Risk estimates of Unilever Indonesia and Kimia Farma's stock portfolio on the same weight were performed using Monte Carlo simulation with backtesting of 30 days period data at 95% confidence level. Keywords : Stock, Risk, Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Copula, Value at Risk

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