Original scientific paper
https://doi.org/10.1080/1331677X.2021.1967771
Volatility analysis based on GARCH-type models: Evidence from the Chinese stock market
Yuling Wang
Yunshuang Xiang
Xinyu Lei
Yucheng Zhou
Abstract
Volatility is integral for the financial market. As an emerging market, the Chinese stock market is acutely volatile. In this study, the
data of the Shanghai Composite Index and Shenzhen Component
Index returns were selected to conduct an empirical analysis
based on the generalised autoregressive conditional heteroskedasticity (GARCH)-type model. We established the autoregressive
moving average (ARMA)-GARCH model with t-distribution for the
sample series to compare model effects under different distributions and orders. In contrast, we proposed threshold-GARCH
(TGARCH) and exponential-GARCH (EGARCH) models to capture
the features of the index. Additionally, the error degree and prediction results of different models were evaluated in terms of
mean squared error (MSE), mean absolute error (MAE) and rootmean-squared error (RMSE). The results denote that the ARMA
(4,4)-GARCH (1,1) model under Student’s t-distribution outperforms other models when forecasting the Shanghai Composite
Index return series. For the return series of the Shenzhen
Component Index, ARMA(1,1)-TGARCH(1,1) display the best forecasting performance among all models. This study could provide
an effective information reference for the macro decision-making
of the government, the operation of listed companies and investors’ investment decision-making.
Keywords
ARMA-GARCH; GARCH; stock markets; volatility
Hrčak ID:
302286
URI
Publication date:
31.3.2023.
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