Title：Distributional uncertainty of the financial time series measured by G-expecations
Speaker：Shige Peng（Shandong University）
Time：08:30-09:30, July9
Location： Room 214, Activity Center
Abstract：Financial time series admits inherent uncertainty and randomness that changes over time. To clearly describe volatility uncertainty of the time series, we assume that the volatility of risky assets holds value between the minimum volatility and maximum volatility of the assets. This study establishes autoregressive models to determine the maximum and minimum volatilities, where the ratio of minimum volatility to maximum volatility can measure volatility uncertainty. By utilizing the value at risk (VaR) predictor model under volatility uncertainty, we introduce the risk and uncertainty, and show that the autoregressive model of volatility uncertainty is a powerful tool in predicting the VaR for a benchmark dataset.
