基于不同分布假设GARCH模型对上证指数波动性预测能力的比较研究

An Comparative Study on Forecasting Volatility of Shanghai Stock Index Using GARCH Model with Different Distributions

本文在四种不同的分布假设(Normal,Student-t,GED和SkewedStudent-t)下,对上证指数波动性进行了GARCH(1,1)模型预测能力实证比较研究,目的在于揭示分布假设对GARCH模型预测能力的影响。研究结果表明,使用厚尾分布假设(Student-t,GED)提高了模型的预测绩效。但引入偏斜student-t分布并未能进一步提高模型预测能力。

This paper examines the forecasting performance of GARCH(1,1) model used with four distributions (Normal, Student-t, GED and Skewed Student-t). We explore and compare different possible sources of forecasts improvements: fat-tailed distributions and skewed distribution. The Shanghai stock index is studied using daily data over a 6-years period. Better forecasts are achieved when using fat-tailed distributions. However, increased performance of the forecasts is not clearly observed when using skewed distribution.