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Modeling and forecasting listed tourism firms’ risk in China using a trend asymmetric GARCH-MIDAS model

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  • Peng Yang
  • Haiyan Song
  • Long Wen
  • Han Liu

Abstract

This study employs the multivariate trend asymmetric GARCH-MIDAS (TAGM) model, an extension of the GARCH-MIDAS model, to explore the potential asymmetric impact of uncertainty shocks, including oil and infectious disease shocks, on the long-term volatility of China’s listed tourism firms. Furthermore, we test the out-of-sample forecasting accuracy of uncertainty shocks to China’s listed tourism firms’ risk, which is measured by the volatility of tourism stocks after the outbreak of coronavirus disease 2019 (COVID-19). The results show that uncertainty shocks have a significant asymmetric effect on the long-run volatility of tourism stocks. The included uncertainty shocks improved accuracy in forecasting China’s listed tourism firms’ risk after the pandemic outbreak. The empirical results have important implications for tourism investment strategies in unstable environments.

Suggested Citation

  • Peng Yang & Haiyan Song & Long Wen & Han Liu, 2024. "Modeling and forecasting listed tourism firms’ risk in China using a trend asymmetric GARCH-MIDAS model," Tourism Economics, , vol. 30(6), pages 1404-1422, September.
    • Handle: RePEc:sae:toueco:v:30:y:2024:i:6:p:1404-1422
    DOI: 10.1177/13548166231207671
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    1. Hsiao-I Kuo & Chia-Lin Chang & Bing-Wen Huang & Chi-Chung Chen & Michael McAleer, 2009. "Estimating the Impact of Avian Flu on International Tourism Demand Using Panel Data," Tourism Economics, , vol. 15(3), pages 501-511, September.
    2. Karabulut, Gokhan & Bilgin, Mehmet Huseyin & Demir, Ender & Doker, Asli Cansin, 2020. "How pandemics affect tourism: International evidence," Annals of Tourism Research, Elsevier, vol. 84(C).
    3. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    4. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    5. Schiff, Aaron & Becken, Susanne, 2011. "Demand elasticity estimates for New Zealand tourism," Tourism Management, Elsevier, vol. 32(3), pages 564-575.
    6. Oguzhan Cepni & Tarik Dogru & Ozgur Ozdemir, 2023. "The contagion effect of COVID-19-induced uncertainty on US tourism sector: Evidence from time-varying granger causality test," Tourism Economics, , vol. 29(4), pages 906-928, June.
    7. Ming-Hsiang Chen, 2013. "The Impact of Demand and Supply Shocks on US Hospitality Index Returns," Tourism Economics, , vol. 19(2), pages 349-371, April.
    8. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    9. James Lennox, 2012. "Impacts of High Oil Prices on Tourism in New Zealand," Tourism Economics, , vol. 18(4), pages 781-800, August.
    10. 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.
    11. Salisu, Afees A. & Gupta, Rangan, 2021. "Oil shocks and stock market volatility of the BRICS: A GARCH-MIDAS approach," Global Finance Journal, Elsevier, vol. 48(C).
    12. 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.
    13. Park, Jungwook & Ratti, Ronald A., 2008. "Oil price shocks and stock markets in the U.S. and 13 European countries," Energy Economics, Elsevier, vol. 30(5), pages 2587-2608, September.
    14. Qin, Yun & Chen, Jinyu & Dong, Xuesong, 2021. "Oil prices, policy uncertainty and travel and leisure stocks in China," Energy Economics, Elsevier, vol. 96(C).
    15. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    16. Ling-en Wang & Bing Tian & Viachaslau Filimonau & Zhizhong Ning & Xuechun Yang, 2022. "The impact of the COVID-19 pandemic on revenues of visitor attractions: An exploratory and preliminary study in China," Tourism Economics, , vol. 28(1), pages 153-174, February.
    17. He, Feng & Ma, Feng & Wang, Ziwei & Yang, Bohan, 2021. "Asymmetric volatility spillover between oil-importing and oil-exporting countries' economic policy uncertainty and China's energy sector," International Review of Financial Analysis, Elsevier, vol. 75(C).
    18. Albert Assaf & Raffaele Scuderi, 2020. "COVID-19 and the recovery of the tourism industry," Tourism Economics, , vol. 26(5), pages 731-733, August.
    19. Bai, Lan & Wei, Yu & Wei, Guiwu & Li, Xiafei & Zhang, Songyun, 2021. "Infectious disease pandemic and permanent volatility of international stock markets: A long-term perspective," Finance Research Letters, Elsevier, vol. 40(C).
    20. 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.
    21. Martin Falk & Eva Hagsten & Xiang Lin, 2023. "Uneven domestic tourism demand in times of pandemic," Tourism Economics, , vol. 29(3), pages 596-611, May.
    22. Lucie Plzáková & Egon Smeral, 2022. "Impact of the COVID-19 crisis on European tourism," Tourism Economics, , vol. 28(1), pages 91-109, February.
    23. Jiachong Hu & Xiao-Guang Yue & Deimante Teresiene & Irfan Ullah, 2022. "How COVID19 pandemic affect film and drama industry in China: an evidence of nonlinear empirical analysis," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 2254-2272, December.
    24. Siamand Hesami & Bezhan Rustamov & Husam Rjoub & Wing-Keung Wong, 2020. "Implications of Oil Price Fluctuations for Tourism Receipts: The Case of Oil Exporting Countries," Energies, MDPI, vol. 13(17), pages 1-17, August.
    25. Mohanty, Sunil & Nandha, Mohan & Habis, Essam & Juhabi, Eid, 2014. "Oil price risk exposure: The case of the U.S. Travel and Leisure Industry," Energy Economics, Elsevier, vol. 41(C), pages 117-124.
    26. Shahzad, Syed Jawad Hussain & Caporin, Massimiliano, 2020. "On the volatilities of tourism stocks and oil," Annals of Tourism Research, Elsevier, vol. 81(C).
    27. Oguz Ersan & Sagi Akron & Ender Demir, 2019. "The effect of European and global uncertainty on stock returns of travel and leisure companies," Tourism Economics, , vol. 25(1), pages 51-66, February.
    28. Omneya Abdelsalam & Ahmet Faruk Aysan & Oguzhan Cepni & Mustafa Disli, 2023. "The spillover effects of the COVID-19 pandemic: Which subsectors of tourism have been affected more?," Tourism Economics, , vol. 29(2), pages 559-567, March.
    29. Becken, Susanne & Lennox, James, 2012. "Implications of a long-term increase in oil prices for tourism," Tourism Management, Elsevier, vol. 33(1), pages 133-142.
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