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

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  • Shige Peng
  • Shuzhen Yang

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.

Suggested Citation

  • Shige Peng & Shuzhen Yang, 2020. "Distributional uncertainty of the financial time series measured by G-expectation," Papers 2011.09226, arXiv.org, revised Jul 2021.
    • Handle: RePEc:arx:papers:2011.09226
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    References listed on IDEAS

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