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Pass-through from temperature intervals to China's commodity futures’ interval-valued returns: Evidence from the varying-coefficient ITS model

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  • Wu, Dan
  • Dai, Xingyu
  • Zhao, Ruikun
  • Cao, Yaru
  • Wang, Qunwei

Abstract

This paper proposes a novel varying-coefficient interval-valued time series (VC-ITS) model to reveal the impact of temperature intervals on China's commodity futures’ interval-valued returns. 14 Chinese commodities futures from 2018 to 2023 were analyzed and results show that temperature intervals have a dynamic impact on crude oil futures interval-valued returns and a static impact on steaming coal futures in the selected energy futures. There is a negative pass-through effect of temperature intervals on almost all selected agricultural futures return intervals. Changes in temperature intervals have almost no pass-through effect on changes in metal futures' interval-valued returns, except for nickel futures.

Suggested Citation

  • Wu, Dan & Dai, Xingyu & Zhao, Ruikun & Cao, Yaru & Wang, Qunwei, 2023. "Pass-through from temperature intervals to China's commodity futures’ interval-valued returns: Evidence from the varying-coefficient ITS model," Finance Research Letters, Elsevier, vol. 58(PA).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pa:s154461232300661x
    DOI: 10.1016/j.frl.2023.104289
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    as
    1. Li, Ziyang & Chen, Yanjun & Li, Yanlin, 2023. "Top management abnormal turnover and stock price crash risk: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 89(C).
    2. Lu, Xinjie & Ma, Feng & Wang, Jiqian & Zhu, Bo, 2021. "Oil shocks and stock market volatility: New evidence," Energy Economics, Elsevier, vol. 103(C).
    3. Zhou, Bo & Ding, Hao, 2023. "How public attention drives corporate environmental protection: Effects and channels," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    4. Shoudong Chen & Yueshan Li, 2023. "Targeted Poverty Alleviation And Stock Price Crash Risk: Evidence From China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 68(04), pages 1271-1301, June.
    5. Zhao, Yi & Dai, Xingyu & Zhang, Dongna & Wang, Qunwei & Cao, Yaru, 2023. "Do weather conditions drive China's carbon-coal-electricity markets systemic risk? A multi-timescale analysis," Finance Research Letters, Elsevier, vol. 51(C).
    6. Xie, Linlin & Liu, Guangqiang & Liu, Boyang, 2023. "Patent pledge policy and stock price crash risk: Evidence from China," Research in International Business and Finance, Elsevier, vol. 65(C).
    7. Lu, Xinjie & Ma, Feng & Wang, Jiqian & Wang, Jianqiong, 2020. "Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models," Energy, Elsevier, vol. 212(C).
    8. Xingyu Dai & Dongna Zhang & Chi Keung Marco Lau & Qunwei Wang, 2023. "Multiobjective portfolio optimization: Forecasting and evaluation under investment horizon heterogeneity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2167-2196, December.
    9. Quanying Lu & Yuying Sun & Yongmiao Hong & Shouyang Wang, 2022. "Forecasting interval-valued crude oil prices using asymmetric interval models," Quantitative Finance, Taylor & Francis Journals, vol. 22(11), pages 2047-2061, November.
    10. Sun, Yuying & Han, Ai & Hong, Yongmiao & Wang, Shouyang, 2018. "Threshold autoregressive models for interval-valued time series data," Journal of Econometrics, Elsevier, vol. 206(2), pages 414-446.
    11. Symeonidis, Lazaros & Daskalakis, George & Markellos, Raphael N., 2010. "Does the weather affect stock market volatility?," Finance Research Letters, Elsevier, vol. 7(4), pages 214-223, December.
    12. Zhou, Bo & Zhang, Cheng, 2023. "When green finance meets banking competition: Evidence from hard-to-abate enterprises of China," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    13. Xinghe Liu & Jun Gao & Zeyi Chen & Yuqing Huang, 2023. "Depoliticization and Stock Price Crash Risk: Evidence from China," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 59(7), pages 2313-2327, May.
    14. Dai, Xingyu & Dai, Peng-Fei & Wang, Qunwei & Ouyang, Zhi-Yi, 2023. "The impact of energy-exporting countries’ EPUs on China’s energy futures investors: Risk preference, investment position and investment horizon," Research in International Business and Finance, Elsevier, vol. 64(C).
    15. Sun, Yuying & Zhang, Xun & Hong, Yongmiao & Wang, Shouyang, 2019. "Asymmetric pass-through of oil prices to gasoline prices with interval time series modelling," Energy Economics, Elsevier, vol. 78(C), pages 165-173.
    16. Gao, Haoyu & Wen, Huiyu & Yu, Shujiaming, 2022. "Weathering information disruption: Typhoon strikes and analysts’ forecast dispersion," Finance Research Letters, Elsevier, vol. 49(C).
    17. Zhang, Dongna & Dai, Xingyu & Wang, Qunwei & Lau, Chi Keung Marco, 2023. "Impacts of weather conditions on the US commodity markets systemic interdependence across multi-timescales," Energy Economics, Elsevier, vol. 123(C).
    18. Kong, Xiaowei & Jin, Yifan & Liu, Lihua & Xu, Jialu, 2023. "Firms' exposures on COVID-19 and stock price crash risk: Evidence from China," Finance Research Letters, Elsevier, vol. 52(C).
    19. Ji, Qiang & Zhang, Dayong, 2019. "China’s crude oil futures: Introduction and some stylized facts," Finance Research Letters, Elsevier, vol. 28(C), pages 376-380.
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