Which industries are affected by risk spillovers from the Shanghai crude oil futures market more

Abstract

Abstract: Since its establishment in 2018, the association between the Shanghai crude oil futures market and the risk volatility of China’s stock market has become more and more obvious. Different from previous studies that focus more on the overall stock market, we investigate the risk spillover effects from the Shanghai crude oil futures market to the industries in China from the industry dimension in this paper. The 5-minute returns of ten first-level industry indices of the Shanghai Stock Exchange for the period from September 1, 2019, to September 1, 2022, are selected, we introduce the GAS model to make up for the shortcomings of the GARCHs model, and we establish the MIDAS-Copula-CoVaR model to measure the conditional risk of each industry as well as the risk spillover effects it carries. The results show that, first, the Copula model containing the MIDAS structure fits better, which fully demonstrates the importance of incorporating high-frequency data. Second, the upside risk of each industry conditional on the Shanghai crude oil futures market is significantly larger than the downside risk, showing a more obvious asymmetry, which indicates that the oil price increases have a greater impact on each industry. Third, in terms of industries, the falling Shanghai crude oil futures prices have the greatest impact on the energy industry and the smallest impact on the utility industry, while rising prices have the greatest impact on the medical industry and the smallest impact on the financial industry. Fourth, compared to normal cases, extreme upside risk has the largest risk spillovers for the medical industry and the smallest for the consumer industry, and extreme downside risk has the largest risk spillovers for the optional industry and the smallest for the financial industry.

Key words: hanghai crude oil futures market, GAS model, mixed Data sampling, risk spillovers

CLC Number:

F830

SONG Jiashan, WEI Siyao, JIANG Kunliang. Which industries are affected by risk spillovers from the Shanghai crude oil futures market more[J]. Journal of Guizhou University of Finance and Economics, 2023(06): 11-21.

References

[1] Ji Q, Zhang D. China's Crude Oil Futures:introduction and Some Stylized Facts[J]. Finance Research Letters, 2019, 28:376~380.
[2] Ding H, Kim H G, Park S Y. Crude Oil and Stock Markets:Causal Relationships in Tails?[J]. Energy Economics, 2016, 59:58~69.
[3] Peng C, Zhu H, Guo Y, et al. Risk Spillover of international Crude Oil to China's Firms:Evidence from Granger Causality across Quantile[J]. Energy Economics, 2018, 72:188~199.
[4] Nguyen C C, Bhatti M I. Copula Model Dependency between Oil Prices and Stock Markets:Evidence from China and Vietnam[J]. Journal of International Financial Markets, Institutions and Money, 2012, 22(4):758~773.
[5] Chen Q, Lv X. The Extreme~Value Dependence between the Crude Oil Price and Chinese Stock Markets[J]. International Review of Economics & Finance, 2015, 39:121~132.
[6] Jiang K, Ye W. Does the Asymmetric Dependence Volatility Affect Risk Spillovers between the Crude Oil Market and BRICS Stock Markets?[J]. Economic Modelling, 2022, 117:106046.
[7] Wang X, Wang Y. Volatility Spillovers Between Crude Oil and Chinese Sectoral Equity Markets:Evidence from A Frequency Dynamics Perspective[J]. Energy Economics, 2019, 80:995~1009.
[8] Cong R G, Wei Y M, Jiao J L, et al. Relationships between Oil Price Shocks and Stock Market:An Empirical Analysis from China[J]. Energy Policy, 2008, 36(9):3544~3553.
[9] Caporale G M, Ali F M, Spagnolo N. Oil Price Uncertainty and Sectoral Stock Returns in China:A Time-Varying Approach[J]. China Economic Review, 2015, 34:311~321.
[10] Mensi W, Al Rababa'a A R, Vo X V, et al. Asymmetric Spillover and Network Connectedness between Crude Oil, Gold, and Chinese Sector Stock Markets[J]. Energy Economics, 2021, 98:105262.
[11] Zhou Y, Wu S, Zhang Z. Multidimensional Risk Spillovers among Carbon, Energy and Nonferrous Metals Markets:Evidence from the Quantile VAR Network[J]. Energy Economics, 2022, 114:106319.
[12] Zhu B, Zhou X, Liu X, et al. Exploring the Risk Spillover Effects among China's Pilot Carbon Markets:A Regular Vine Copula-Coes Approach[J]. Journal of Cleaner Production, 2020, 242:118455.
[13] 顾云,张栋浩,杜在超,黄在鑫.系统性风险度量CoES的建模和检验[J].统计研究,2022,39(1):132~145.
[14] Sun X, Liu C, Wang J, et al. Assessing the Extreme Risk Spillovers of International Commodities on Maritime Markets:A GARCH-Copula-CoVaR Approach[J]. International Review of Financial Analysis, 2020, 68:101453.
[15] Kumar S, Tiwari A K, Raheem I D, et al. Time-Varying Dependence Structure between Oil and Agricultural Commodity Markets:A Dependence-Switching CoVaR Copula Approach[J]. Resources Policy, 2021, 72:102049.
[16] Tian M, Alshater M M, Yoon S M. Dynamic Risk Spillovers from Oil to Stock Markets:Fresh Evidence from GARCH Copula Quantile Regression-Based CoVaR Model[J]. Energy Economics, 2022, 115:106341.
[17] Creal D, Koopman S J, Lucas A. Generalized Autoregressive Score Models with Applications[J]. Journal of Applied Econometrics, 2013, 28(5):777~795.
[18] 刘赛可,何晓群,夏利宇.基于GAS模型的动态VaR预测效果分析[J].数理统计与管理,2022,41(1):179~189.
[19] Salvatierra I D L, Patton A J. Dynamic Copula Models and High Frequency Data[J]. Journal of Empirical Finance, 2015, 30:120~135.
[20] 龚玉婷,陈强,郑旭.谁真正影响了股票和债券市场的相关性——基于混频Copula模型的视角[J].经济学(季刊),2016,15(3):1205~1224.
[21] 蔡光辉,徐君,应雪海.基于GAS的混频Copula模型的投资组合风险预测[J].系统工程理论与实践,2021,41(8):2030~2044.
[22] Gong Y, Ma C, Chen Q. Exchange Rate Dependence and Economic Fundamentals:A Copula-MIDAS Approach[J]. Journal of International Money and Finance, 2022, 123:102597.
[23] Zhu P, Tang Y, Wei Y, et al. Multidimensional Risk Spillovers among Crude Oil, the US and Chinese Stock Markets:Evidence during the COVID-19 Epidemic[J]. Energy, 2021, 231:120949.
[24] 寇红红,柴建,郑嘉俐,孙少龙.上海原油期货市场是否具有稳定中国股票市场的作用?[J].中国管理科学,2022,30(11):20~30.
[25] Gong Y, Chen Q, Liang J. A Mixed Data Sampling Copula Model for the Return-Liquidity Dependence in Stock Index Futures Markets[J]. Economic Modelling, 2018, 68:586~598.
[26] Andersen T G, Bollerslev T. Answering the Skeptics:Yes, Standard Volatility Models do Provide Accurate Forecasts[J]. International Economic Review, 1998:885~905.
[27] Tobias A, Brunnermeier M K. CoVaR[J]. The American Economic Review, 2016, 106(7):1705.
[28] Reboredo J C, Ugolini A. Quantile Dependence of Oil Price Movements and Stock Returns[J]. Energy Economics, 2016, 54:33~49.
[29] Liu Z, Zhang H, Ding Z, et al. When are the Effects of Economic Policy Uncertainty on Oil-Stock Correlations Larger Evidence from A Regime-Switching Analysis[J]. Economic Modelling, 2022, 114:105941.