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Transportation sector and Chinese stock volatility forecasting: Evidence from freight and passenger traffic

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  • Zhang, Lili
  • Zhong, Juandan

Abstract

Based on the GARCH-MIDAS model, this paper explores the forecasting power of four transportation sub-sectors. The empirical results show that the freight volume and passenger traffic of air aviation outperform other models during MCS and DoC tests. This illustrates the important role of air aviation in the operation of a country's economy. The findings of this paper could help the government to formulate infrastructure planning and provide insights into the allocation of different resources to different transportation sectors.

Suggested Citation

  • Zhang, Lili & Zhong, Juandan, 2024. "Transportation sector and Chinese stock volatility forecasting: Evidence from freight and passenger traffic," Finance Research Letters, Elsevier, vol. 60(C).
    • Handle: RePEc:eee:finlet:v:60:y:2024:i:c:s154461232301334x
    DOI: 10.1016/j.frl.2023.104962
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    1. Dave Donaldson, 2018. "Railroads of the Raj: Estimating the Impact of Transportation Infrastructure," American Economic Review, American Economic Association, vol. 108(4-5), pages 899-934, April.
    2. Kajal Lahiri & Wenxiong Yao, 2012. "Should transportation output be included as part of the coincident indicators system?," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2012(1), pages 1-24.
    3. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
    4. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    5. Banerjee, Abhijit & Duflo, Esther & Qian, Nancy, 2020. "On the road: Access to transportation infrastructure and economic growth in China," Journal of Development Economics, Elsevier, vol. 145(C).
    6. Chauvet, Marcelle & Senyuz, Zeynep & Yoldas, Emre, 2015. "What does financial volatility tell us about macroeconomic fluctuations?," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 340-360.
    7. Mittnik, Stefan & Robinzonov, Nikolay & Spindler, Martin, 2015. "Stock market volatility: Identifying major drivers and the nature of their impact," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 1-14.
    8. Pesaran, M. Hashem & Timmermann, Allan, 2009. "Testing Dependence Among Serially Correlated Multicategory Variables," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 325-337.
    9. Fangni Zhang & Daniel J. Graham, 2020. "Air transport and economic growth: a review of the impact mechanism and causal relationships," Transport Reviews, Taylor & Francis Journals, vol. 40(4), pages 506-528, July.
    10. Hanson, Daniel & Toru Delibasi, Tuba & Gatti, Matteo & Cohen, Shamai, 2022. "How do changes in economic activity affect air passenger traffic? The use of state-dependent income elasticities to improve aviation forecasts," Journal of Air Transport Management, Elsevier, vol. 98(C).
    11. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    12. Hu, Yi & Xiao, Jin & Deng, Ying & Xiao, Yi & Wang, Shouyang, 2015. "Domestic air passenger traffic and economic growth in China: Evidence from heterogeneous panel models," Journal of Air Transport Management, Elsevier, vol. 42(C), pages 95-100.
    13. Hakim, Md Mahbubul & Merkert, Rico, 2016. "The causal relationship between air transport and economic growth: Empirical evidence from South Asia," Journal of Transport Geography, Elsevier, vol. 56(C), pages 120-127.
    14. Wei, Yu & Liu, Jing & Lai, Xiaodong & Hu, Yang, 2017. "Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 141-150.
    15. Ruhe Xie & Haibo Chen & Chris Nash, 2002. "Migration of railway freight transport from command economy to market economy: The case of China," Transport Reviews, Taylor & Francis Journals, vol. 22(2), pages 159-177, January.
    16. Ma, Feng & Lu, Xinjie & Liu, Jia & Huang, Dengshi, 2022. "Macroeconomic attention and stock market return predictability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    17. Valeriano F. García & Lin Liu, 1999. "Macroeconomic Determinants of Stock Market Development," Journal of Applied Economics, Universidad del CEMA, vol. 2, pages 29-59, May.
    18. Ma, Feng & Zhang, Yaojie & Huang, Dengshi & Lai, Xiaodong, 2018. "Forecasting oil futures price volatility: New evidence from realized range-based volatility," Energy Economics, Elsevier, vol. 75(C), pages 400-409.
    19. Valeriano F. Garcia & Lin Liu, 1999. "Macroeconomic Determinants of Stock Market Development," Journal of Applied Economics, Taylor & Francis Journals, vol. 2(1), pages 29-59, May.
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