Combinations of High Order Gaussian Kernel Estimator with Simulation Historical of Value at Risk (VaR) Return Portfolio Measurement

Abstract

Financial 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. 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 i.e. 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.