| Abstract: |
| In this paper, we review the value at risk (VaR) model under sublinear expectation. We first consider the classical VaR model, and then introduce the basic concepts under sublinear expectation. Based on sublinear expectation, we show the definition of the VaR under model uncertainty, which is called G-VaR. Furthermore, we present three methods for estimating the parameters of the G-VaR model. Those are the long-time average method, the first-order autoregressive method, and the adapted learning method. In the end, we use S&P500 index to verify the performance of G-VaR model. |
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