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

Shige Peng; Shuzhen Yang

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arXiv · 2011.09226

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

Shige Peng·Shuzhen Yang

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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.

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Shige Peng, Shuzhen Yang. 2020-11-18. Distributional uncertainty of the financial time series measured by G-expectation. https://arxiv.org/abs/2011.09226

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