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

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Listed:
  • Lu, Linna
  • Lei, Yalin
  • Yang, Yang
  • Zheng, Haoqi
  • Wang, Wen
  • Meng, Yan
  • Meng, Chunhong
  • Zha, Liqiang

Abstract

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 weak-form 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.

Suggested Citation

  • Lu, Linna & Lei, Yalin & Yang, Yang & Zheng, Haoqi & Wang, Wen & Meng, Yan & Meng, Chunhong & Zha, Liqiang, 2023. "Assessing nickel sector index volatility based on quantile regression for Garch and Egarch models: Evidence from the Chinese stock market 2018–2022," Resources Policy, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:jrpoli:v:82:y:2023:i:c:s030142072300274x
    DOI: 10.1016/j.resourpol.2023.103563
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    as
    1. Xing, Dun-Zhong & Li, Hai-Feng & Li, Jiang-Cheng & Long, Chao, 2021. "Forecasting price of financial market crash via a new nonlinear potential GARCH model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    2. Sudhi Sharma & Vaibhav Aggarwal & Miklesh Prasad Yadav, 2021. "Comparison of linear and non-linear GARCH models for forecasting volatility of select emerging countries," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 18(4), pages 526-547, February.
    3. Yuling Wang & Yunshuang Xiang & Xinyu Lei & Yucheng Zhou, 2022. "Volatility analysis based on GARCH-type models: Evidence from the Chinese stock market," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 2530-2554, December.
    4. Hu, Jianming & Luo, Qingxi & Tang, Jingwei & Heng, Jiani & Deng, Yuwen, 2022. "Conformalized temporal convolutional quantile regression networks for wind power interval forecasting," Energy, Elsevier, vol. 248(C).
    5. Sangyeol Lee & Jungsik Noh, 2013. "Quantile Regression Estimator for GARCH Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(1), pages 2-20, March.
    6. Peter Christoffersen & Kris Jacobs, 2004. "Which GARCH Model for Option Valuation?," Management Science, INFORMS, vol. 50(9), pages 1204-1221, September.
    7. Le, Thai Hong & Luong, Anh Tram, 2022. "Dynamic spillovers between oil price, stock market, and investor sentiment: Evidence from the United States and Vietnam," Resources Policy, Elsevier, vol. 78(C).
    8. Tse, Y. K., 2000. "A test for constant correlations in a multivariate GARCH model," Journal of Econometrics, Elsevier, vol. 98(1), pages 107-127, September.
    9. Endri Endri & Zaenal Abidin & Torang P. Simanjuntak & Immas Nurhayati, 2020. "Indonesian Stock Market Volatility: GARCH Model," Montenegrin Journal of Economics, Economic Laboratory for Transition Research (ELIT), vol. 16(2), pages 7-17.
    10. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    11. Hu, Yan & Ni, Jian & Wen, Liu, 2020. "A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    12. Zhao, Lu-Tao & Liu, Kun & Duan, Xin-Lei & Li, Ming-Fang, 2019. "Oil price risk evaluation using a novel hybrid model based on time-varying long memory," Energy Economics, Elsevier, vol. 81(C), pages 70-78.
    13. Jian Liu & Ziting Zhang & Lizhao Yan & Fenghua Wen, 2021. "Forecasting the volatility of EUA futures with economic policy uncertainty using the GARCH-MIDAS model," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-19, December.
    14. Jaydip Sen & Sidra Mehtab & Abhishek Dutta, 2021. "Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH," Papers 2105.13898, arXiv.org.
    15. Nagaraj Naik & Biju R. Mohan, 2021. "Stock Price Volatility Estimation Using Regime Switching Technique-Empirical Study on the Indian Stock Market," Mathematics, MDPI, vol. 9(14), pages 1-18, July.
    16. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    17. Muhammad Mohsin & Li Naiwen & Muhammad Zia-UR-Rehman & Sobia Naseem & Sajjad Ahmad Baig, 2020. "The volatility of bank stock prices and macroeconomic fundamentals in the Pakistani context: an application of GARCH and EGARCH models," Oeconomia Copernicana, Institute of Economic Research, vol. 11(4), pages 609-636, December.
    18. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong, 2019. "Measuring tail risk with GAS time varying copula, fat tailed GARCH model and hedging for crude oil futures," Pacific-Basin Finance Journal, Elsevier, vol. 55(C), pages 95-109.
    19. McDonald, J G & Fisher, A K, 1972. "New Issue Stock Price Behavior," Journal of Finance, American Finance Association, vol. 27(1), pages 97-102, March.
    20. Fakhfekh, Mohamed & Jeribi, Ahmed, 2020. "Volatility dynamics of crypto-currencies’ returns: Evidence from asymmetric and long memory GARCH models," Research in International Business and Finance, Elsevier, vol. 51(C).
    21. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    22. He, Yaoyao & Qin, Yang & Wang, Shuo & Wang, Xu & Wang, Chao, 2019. "Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network," Applied Energy, Elsevier, vol. 233, pages 565-575.
    23. Sudhi Sharma & Vaibhav Aggarwal & Miklesh Prasad Yadav, 2021. "Comparison of linear and non-linear GARCH models for forecasting volatility of select emerging countries," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 18(4), pages 526-547, February.
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