İklim Politikası Belirsizliği, Lojistik Firma Hisse Getirileri ve ESG Puanları Arasındaki İlişkisinin Analizi: TVP-VAR Modelinden Kanıtlar

Abstract

Bu çalışma iklim politikası belirsizliği (CPU), ESG skorları ve lojistik hisse getirileri arasındaki ilişkiyi incelemektedir. Lojistik sektörünü temsilen China Ocean Shipping Company (COSCO) seçilmiştir. Ekim 2007-Temmuz 2022 döneminin aylık verileri kullanılarak Zamanla Değişen Parametre Vektörü Otoregresif Modeli (TVP-VAR) uygulanmıştır. Analiz sonucunda COSCO`nun volatiliteyi Çin ESG ve CPU`ya yaydığı tespit edilmiştir. Bu durum küresel piyasalarda öncü firmalardan olan COSCO`nun Çin Borsasının sürdürülebilirlik skorları üzerinde etkili olduğunu işaret etmektedir. Başka bir ifadeyle, COSCO değişkeninden Çin ESG skorları ve CPU değişkenlerine şok aktarımı gerçekleştirdiği görülmüştür. Son olarak, özellikle Çin için Lojistik sektöründe faaliyet gösteren firmaların sürdürülebilirlik skorlarının tüm diğer sektör skorları içinde de baskın olduğunu kanıtlar niteliktedir.

Keywords

• İklim Politika Belirsizliği
• ESG
• Lojistik Sektörü
• COSCO

The Analysis of the Relationship Among Climate Policy Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model

Abstract

This study examines the relationship between climate policy uncertainty (CPU), China's environmental impact, social responsibility and corporate governance practices (ESG) leader scores and logistics stocks. China Ocean Shipping Company (COSCO), one of the pioneers in global markets, was chosen to represent the logistics industry. The variables were analyzed with the Time-Varying Parameter Vector Autoregressive Model (TVP-VAR) using monthly data from October 2007 to July 2022. As a result of the analysis, it was determined that the COSCO logistics sector variable spreads the volatility to the Chinese ESG Leaders and CPU variables. This indicates that COSCO, one of the leading companies in the global markets, has an impact on the sustainability scores of the CHINA Stock Exchange. In other words, it has been observed that shock transfer occurs from the COSCO variable to the China ESG Leader and CPU variables. Finally, it proves that the sustainability scores of companies operating in the Logistics sector, especially for China, are dominant among all other sector scores.

Keywords

• Climate Policy Uncertainty
• ESG
• Logistics Sector
• COSCO

References

1. Adekoya, O. B., Akinseye, A. B., Antonakakis, N., Chatziantoniou, I., Gabauer, D., & Oliyide, J. (2022). Crude oil and Islamic sectoral stocks: Asymmetric TVP-VAR connectedness and investment strategies. Resources Policy, 78, 102877. https://doi.org/10.1016/J.RESOURPOL.2022.102877
2. Agyabeng-Mensah, Y., Afum, E., & Ahenkorah, E. (2020). Exploring financial performance and green logistics management practices: examining the mediating influences of market, environmental and social performances. Journal of cleaner production, 258, 120613. https://doi.org/10.1016/j.jclepro.2020.120613.
3. Akhtaruzzaman, M., Boubaker, S., & Umar, Z. (2022). COVID–19 media coverage and ESG leader indices. Finance Research Letters, 45, 102170. https://doi.org/10.1016/j.frl.2021.102170
4. Akkus, H. T., & Dogan, M. (2023). Analysis of dynamic connectedness relationships between cryptocurrency, NFT and DeFi assets: TVP-VAR approach. Applied Economics Letters, 1-6.
5. Alphaliner (2023). https://alphaliner.axsmarine.com/PublicTop100/ Accessed: 19 May 2023.
6. Antonakakis, N., & Gabauer, D. (2017). Refined Measures of Dynamic Connectedness based on TVP-VAR. In MPRA Paper (No. 78282; MPRA Paper). University Library of Munich, Germany.
7. Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions. Journal of Risk and Financial Management, 13(4), 84. https://doi.org/10.3390/JRFM13040084.
8. Cepni, O., Demirer, R., Pham, L., & Rognone, L. (2023). Climate uncertainty and information transmissions across the conventional and ESG assets. Journal of International Financial Markets, Institutions and Money, 83, 101730. https://doi.org/10.1016/j.intfin.2022.101730.

9. Chen, Z., Zhang, L., & Weng, C. (2023). Does climate policy uncertainty affect Chinese stock market volatility?. International Review of Economics & Finance, 84, 369-381. https://doi.org/10.1016/j.iref.2022.11.030.
10. Chen, Z., Zhang, X., & Chai, J. (2021). The dynamic impacts of the global shipping market under the background of oil price fluctuations and emergencies. Complexity, 2021, 1-13. https://doi.org/10.1155/2021/8826253.
11. Christopher, M. (2011). Logistics & Supply Chain Management (4th edition), Pearson Education, London, UK.
12. Diebold, F. X., & Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal, 119(534), 158–171. https://doi.org/10.1111/J.1468-0297.2008.02208.X
13. Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/J.IJFORECAST.2011.02.006.
14. Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/J.JECONOM.2014.04.012.
15. Doğan, M., Raikhan, S., Zhanar, N., & Gulbagda, B. (2023). Analysis of Dynamic Connectedness Relationships among Clean Energy, Carbon Emission Allowance, and BIST Indexes. Sustainability, 15(7), 6025.
16. Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient Tests for An Autoregresive Unit Root. Econometrica, 64(4), 813–836.
17. Felicio, J. A., Rodrigues, R., & Caldeirinha, V. (2021). Green shipping effect on sustainable economy and environmental performance. Sustainability, 13(8), 4256. https://doi.org/10.3390/su13084256.
18. Fisher, T. J., & Gallagher, C. M. (2012). New Weighted Portmanteau Statistics for Time Series Goodness of Fit Testing. Journal of the American Statistical Association, 107, 777–787. https://doi.org/10.1080/01621459.2012.688465.
19. Gabauer, D., & Gupta, R. (2018). On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach. Economics Letters, 171, 63–71. https://doi.org/10.1016/J.ECONLET.2018.07.007.
20. Gavriilidis, K. (2021). Measuring climate policy uncertainty. Available at SSRN 3847388. http://dx.doi.org/10.2139/ssrn.3847388.
21. Guo, J., Long, S., & Luo, W. (2022). Nonlinear effects of climate policy uncertainty and financial speculation on the global prices of oil and gas. International Review of Financial Analysis, 83, 102286. https://doi.org/10.1016/j.irfa.2022.102286.
22. Gürsoy, S. (2021). Küresel Ekonomik Politik Belirsizliğin (Gepu) Döviz Kuru, Enflasyon Ve Borsa Etkisi: Türkiye`Den Kanitlar. Türkiye Mesleki Ve Sosyal Bilimler Dergisi (5), 120-131. https://doi.org/10.46236/jovosst.877608.
23. IEA, (2021). Improving the sustainability of passenger and freight transport, https://www.iea.org/topics/transport Accessed 8 April 2023.
24. IEA, (2022). Global CO2 emissions in transport by mode in the sustainable development scenario, https://www.iea.org/data-and-statistics/charts/global-co2-emissions-in-transport-by-mode-in-the-sustainable-development-scenario-2000-2070 Accessed 8 April 2023.
25. IEA Transport Report (2022). Sectoral overview, https://www.iea.org/reports/transport
26. Koop, G., & Korobilis, D. (2014). A new index of financial conditions. European Economic Review, 71, 101–116. https://doi.org/10.1016/J.EUROECOREV.2014.07.002.
27. Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119–147. https://doi.org/10.1016/0304-4076(95)01753-4.
28. Korobilis, D., & Yilmaz, K. (2018). Measuring Dynamic Connectedness with Large Bayesian VAR Models. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3099725.
29. Li, X. (2022). Dynamic spillovers between US climate policy uncertainty and global foreign exchange markets: the pass-through effect of crude oil prices. Letters in Spatial and Resource Sciences, 15, 665–673. https://doi.org/10.1007/s12076-022-00318-4.
30. Liu, M., Guo, T., Ping, W., & Luo, L. (2023). Sustainability and stability: Will ESG investment reduce the return and volatility spillover effects across the Chinese financial market?. Energy Economics, In Press, Journal Pre-proof, 106674. https://doi.org/10.1016/j.eneco.2023.106674.
31. Marine Insight, (2023). 20 Largest container shipping companies in the world in 2023, https://www.marineinsight.com/know-more/10-largest-container-shipping-companies-in-the-world/#1_MSC_%E2%80%93_Mediterranean_Shipping_Company Accessed 19 May 2023.
32. McKinnon, A. (2015). Green Logistics: Improving the Environmental Sustainability of Logistics (third edition). McKinnon, A., Browne, M., Piecyk, M.& Whiteing, A. (Eds.), Environmental sustainability: A new priority for logistics managers (pp. 3-31). Kogan Page.
33. Pang, K., Lu, C.-S., Shang, K.-C. & Weng, H.-K. (2021). An empirical investigation of green shipping practices, corporate reputation and organisational performance in container shipping. International Journal of Shipping and Transport Logistics, 13(3/4), 422–444. https://doi.org/10.1504/IJSTL.2021.113996.
34. Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17–29. https://doi.org/10.1016/S0165-1765(97)00214-0.
35. Polat, O., El Khoury, R., Alshater, M. M., & Yoon, S. M. (2023). Media Coverage of COVID-19 and Its Relationship with Climate Change Indices: A Dynamic Connectedness Analysis of Four Pandemic Waves. Journal of Climate Finance, In Press, Journal Pre-proof, 100010. https://doi.org/10.1016/j.jclimf.2023.100010.
36. Ren, X., Zhang, X., Yan, C., & Gozgor, G. (2022). Climate policy uncertainty and firm-level total factor productivity: Evidence from China. Energy Economics, 113, 106209. https://doi.org/10.1016/j.eneco.2022.106209.
37. Samitas, A., Papathanasiou, S., Koutsokostas, D., & Kampouris, E. (2022-a). Volatility spillovers between fine wine and major global markets during COVID-19: A portfolio hedging strategy for investors. International Review of Economics & Finance, 78, 629-642. https://doi.org/10.1016/j.iref.2022.01.009.
38. Samitas, A., Papathanasiou, S., Koutsokostas, D., & Kampouris, E. (2022-b). Are timber and water investments safe-havens? A volatility spillover approach and portfolio hedging strategies for investors. Finance Research Letters, 47, 102657. https://doi.org/10.1016/j.frl.2021.102657.
39. Seroka-Stolka, O., & Ociepa-Kubicka, A. (2019). Green logistics and circular economy. Transportation Research Procedia, 39, 471-479. https://doi.org/10.1016/j.trpro.2019.06.049.
40. Shahbaz, M., Dogan, M., Akkus, H.T. et al. The effect of financial development and economic growth on ecological footprint: evidence from top 10 emitter countries. Environmental Science and Pollution Research. 30, 73518–73533 (2023). https://doi.org/10.1007/s11356-023-27573-2.
41. Xiao, J., & Liu, H. (2023). The time-varying impact of uncertainty on oil market fear: Does climate policy uncertainty matter?. Resources Policy, 82, 103533. https://doi.org/10.1016/j.resourpol.2023.103533.
42. Xie, Q., Cheng, L., Liu, R., Zheng, X., & Li, J. (2023). COVID-19 and risk spillovers of China's major financial markets: Evidence from time-varying variance decomposition and wavelet coherence analysis. Finance Research Letters, 52, 103545. https://doi.org/10.1016/j.frl.2022.103545.
43. Yan, W. L. & Cheung, A. (W.K.) (2023). The dynamic spillover effects of climate policy uncertainty and coal price on carbon price: Evidence from China. Finance Research Letters, 53, 103400. https://doi.org/10.1016/j.frl.2022.103400.
44. Yingfei, Y., Mengze, Z., Zeyu, L., Ki-Hyung, B., Avotra, A. A. R. N., & Nawaz, A. (2022). Green logistics performance and infrastructure on service trade and environment-measuring firm’s performance and service quality. Journal of King Saud University-Science, 34(1), 1-10. https://doi.org/10.1016/j.jksus.2021.101683.
45. Yontar, E. (2022). Assessment of the logistics activities with a structural model on the basis of improvement of sustainability performance. Environmental Science and Pollution Research, 29(45), 68904-68922. https://doi.org/10.1007/s11356-022-20562-x.
46. Yu, J., Zhang, M., Liu, R., & Wang, G. (2023). Dynamic Effects of Climate Policy Uncertainty on Green Bond Volatility: An Empirical Investigation Based on TVP-VAR Models. Sustainability, 15(2), 1692. https://doi.org/10.3390/su15021692.
47. Zhou, D., Siddik, A. B., Guo, L., & Li, H. (2023). Dynamic relationship among climate policy uncertainty, oil price and renewable energy consumption - findings from TVP-SV-VAR approach. Renewable Energy, 204, 722-732. https://doi.org/10.1016/j.renene.2023.01.018.