Value at risk linear exponent (VARLINEX) forecasts

The VARLINEX (value at risk linear exponent) forecasting procedure is presented in this paper, which explicitly adjusts the forecasts when the loss functions of the forecaster are asymmetric. The theory of order statistics is applied to derive the VARLINEX forecasts and their corresponding confidence intervals, which are distribution-free. An empirical study based on our method is carried out for the S&P 500 returns and compared with the RiskMetrics™ and the EVT method. It is found that our method can perform very well in relation to EVT and always performs much better than RiskMetrics™.