KOMBINASI ESTIMATOR KERNEL GAUSSIAN ORDER TINGGI DENGAN SIMULASI HISTORIKAL TERHADAP PENGUKURAN VALUE AT RISK (VaR) RETURN PORTFOLIO - - Zulfikar; Muhyiddin Zainul Abidin; Ali Priyono; Ali Mudlofar (KH. A. Wahab Hasbullah University, 2014)
Experts assume that measures the risk of financial asset returns generally have a normal distribution. Reality often shows asset returns are not normally distributed, so that the constraints and make it difficult to estimate the risk of taking the measurements. For it is necessary to develop methods of risk measurement, VaR on asset returns regardless of the form of distribution as a form of financial risk estimation. In this research the size of the financial risk VaR calculation that will be developed in the form of High-order kernel estimator of VaR with historical simulation method approach. This method implements the VaR measurement and VaR sensitivity of the asset return data are first estimated using a combination of historical simulations and high-order kernel estimators. The data used is the return data obtained from the calculation of the closing price (closing price) daily stock of PT Astra International Tbk (ASII) and PT. Telekomunikasi Indonesia Tbk. (TKLM) trading for one year ie from January 2, 2012 until December 28, 2012 are taken from the Indonesia Stock Exchange (IDX). Return assumed to be independent for each time period. Test results obtained Portfolio Return value estimate VaR with Historical Simulation estimation methods and the combination of high order kernels increase with increasing order kernel estimates and tend to be larger than the Historical Simulation estimation methods. Statistical properties indicates that the value of symmetry (Skewness) data distribution is generally obtained values close to zero ie between values of 0.06 and 1.06, which means the portfolio return data distribution approximates the shape of a symmetrical distribution. Moderate slope values (the kurtosis) showed the highest value of -1.53, which means the value of the distribution of the portfolio return data are within the scope of normal distribution in which the kurtosis value for the normal distribution is 3. Test sensitivity of VaR portfolio return data shows that the assumption of 99% for a confidence level and a one-year time horizon, VaR at 4,396% a year means 252 days of hope in the risk by 11 days on market movements. Keywords: Return of assets, Value at Risk, High-order kernel estimator, Historical Simulation