Assessing nickel sector index volatility based on quantile regression for Garch and Egarch models: Evidence from the Chinese stock market 2018-2022

Assessing nickel sector index volatility in China is essential to observe the price dynamics of China's nickel industry and to promote its sound development. This study proposed a volatility analysis method based on quantile regression for Garch and Egarch models to depict the fluctuation characteristics of the nickel sector index from June 12, 2018 to June 1, 2022. Garch, Egarch, Garch-Quantile Regression (QR) and Egarch-Quantile Regression (QR) models were established. The results indicated that the nickel sector market had been weakform efficient and it could respond to market information effectively. An ARCH effect and volatility agglomeration characteristics exist while the nickel sector index is closely related to the global economic climate and geopolitics. Alienated conditional standard deviation shows the role of special events in fueling stock prices and the abnormal rise or fall of asset fluctuations in the current period. The sensitivity of the nickel sector index to negative information is not necessarily greater than that to positive information. By comparing the volatility fitted by different estimation methods, it can be observed that the EGARCH-QR estimation method has the best fitting effect on the volatility. Therefore, a more robust estimator can be obtained. EGARCH-QR model can more accurately reflect market fluctuations and improve the robustness of risk measurement estimates. Listed companies in the domestic nickel industry should grasp the dynamics of nickel supply and demand at home and abroad, and reasonably arrange production and sales.