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

2020/11/18 by Shigē Péng, Peng, Shige, Shuzhen Yang +1 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · Engineering · #Complex Systems and Time Series Analysis #FOS: Economics and business #Fault Detection and Control Systems #Mathematical Finance (q-fin.MF) #Stock Market Forecasting Methods

paper · pdf · doi:10.48550/arxiv.2011.09226

openalex publication_date 2020/11/18 · openalex created_date 2021/12/06 · openalex updated_date 2026/08/01

Abstract

Based on law of large numbers and central limit theorem under nonlinear\nexpectation, we introduce a new method of using G-normal distribution to\nmeasure financial risks. Applying max-mean estimators and small windows method,\nwe establish autoregressive models to determine the parameters of G-normal\ndistribution, i.e., the return, maximal and minimal volatilities of the time\nseries. Utilizing the value at risk (VaR) predictor model under G-normal\ndistribution, we show that the G-VaR model gives an excellent performance in\npredicting the VaR for a benchmark dataset comparing to many well-known VaR\npredictors.\n

Cited by

• Discrete-time asset price bubbles with short sales prohibitions under model uncertainty

Related

• A Method for Predicting VaR by Aggregating Generalized Distributions Driven by the Dynamic Conditional Score
• G-Brownian Motion and Dynamic Risk Measure under Volatility Uncertainty
• Uncertainty in the financial market and application to forecastabnormal financial fluctuations
• There is a VaR beyond usual approximations
• g-Expectation of Distributions