Distributional uncertainty of the financial time series measured by G-expectation

Shige Peng and Shuzhen Yang

Papers from arXiv.org

Abstract: 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.

Date: 2020-11, Revised 2021-07
New Economics Papers: this item is included in nep-ets, nep-ore and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2011.09226

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