论文标题
通过G-期望衡量的财务时间序列的分配不确定性
Distributional uncertainty of the financial time series measured by G-expectation
论文作者
论文摘要
根据非线性期望下的大量和中央限制定理定律，我们引入了一种使用G-正常分布来衡量财务风险的新方法。应用最大均值估计器和小型Windows方法，我们建立了自回归模型来确定G-正常分布的参数，即时间序列的返回，最大和最小波动率。利用在G正常分布下的风险（VAR）预测模型的值（VAR）的值，我们表明G-VAR模型在预测基准数据集的VAR方面具有出色的性能，与许多众所周知的VAR预测变量相比。
Based on law of large numbers and central limit theorem under nonlinear expectation, we introduce a new method of using G-normal distribution to measure financial risks. Applying max-mean estimators and small windows method, we establish autoregressive models to determine the parameters of G-normal distribution, i.e., the return, maximal and minimal volatilities of the time series. Utilizing the value at risk (VaR) predictor model under G-normal distribution, we show that the G-VaR model gives an excellent performance in predicting the VaR for a benchmark dataset comparing to many well-known VaR predictors.
