Cilt 14 Sayı 1 (2026): Business & Management Studies: An International Journal
Makaleler

İklim politikası belirsizliğinin yeşil tahvil getirileri üzerindeki heterojen çok ölçekli kantil etkileri

PDF (İngilizce)
  • Aslan Aydoğdu
Aslan Aydoğdu
Dr. Öğr. Görv., Sivas Bilim ve Teknoloji Üniversitesi, Sivas, Türkiye

Yayınlanmış 25.03.2026

Anahtar Kelimeler

  • Green Finance, Quantile-on-Quantile Regression, Green Bond, Wavelet Analysis, Quantile -on-Quantile Causality
  • Yeşil Finans, Kantil-Kantil Regresyon, Yeşil Tahvil, Wavelet Analizi, Kantil-Kantil Nedensellik

Nasıl Atıf Yapılır

İklim politikası belirsizliğinin yeşil tahvil getirileri üzerindeki heterojen çok ölçekli kantil etkileri. (2026). Business & Management Studies: An International Journal, 14(1), 500-517. https://doi.org/10.15295/bmij.v14i1.2728

Telif Hakkı (c) 2026 Aslan Aydoğdu

Creative Commons License

Bu çalışma Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ile lisanslanmıştır.

Nasıl Atıf Yapılır

İklim politikası belirsizliğinin yeşil tahvil getirileri üzerindeki heterojen çok ölçekli kantil etkileri. (2026). Business & Management Studies: An International Journal, 14(1), 500-517. https://doi.org/10.15295/bmij.v14i1.2728

Öz

Bu çalışma, iklim politikası belirsizliğinin (CPU) yeşil tahvil getirileri (GB) üzerindeki etkisini farklı zaman ufukları ve piyasa koşulları altında incelemektir. Bu kapsamda, Gavriilidis (2021) tarafından geliştirilen haber temelli CPU endeksi ile S&P Green Bond Index’e ait Haziran 2015-Haziran 2025 dönemine ilişkin aylık veriler kullanılmıştır. Analiz, doğrusal olmayan, asimetrik ve dağılıma bağlı dinamikleri yakalayabilen wavelet quantile-on-quantile regression (WQQR) ve wavelet quantile-on-quantile Granger causality (WQQGC) yöntemleriyle gerçekleştirilmiştir. Elde edilen bulgular, CPU’nun yeşil tahvil getirileri üzerindeki etkisinin hem zaman ufkuna hem de kantil düzeylerine bağlı olarak farklılaştığını ortaya koymaktadır. Kısa vadede CPU artışlarının, düşük getiri rejimlerinde yeşil tahvil getirilerini baskıladığı, buna karşılık yüksek getiri rejimlerinde pozitif etki yarattığı tespit edilmiştir. Orta vadede bu ilişkinin yön değiştirdiği ve CPU etkisinin negatiften pozitife doğru evrildiği görülmektedir. Uzun vadede ise CPU’nun tüm kantil düzeylerinde yeşil tahvil getirileri üzerinde istatistiksel olarak anlamlı ve pozitif bir etki oluşturduğu belirlenmiştir. Nedensellik analizi sonuçları, CPU’nun yeşil tahvil piyasası üzerindeki etkilerinin zaman-frekans ve kantil boyutlarında heterojen ve piyasa koşullarına bağlı bir yapı sergilediğini göstermektedir.

PDF (İngilizce)

Referanslar

  1. Adebayo, T. S., & Olanrewaju, V. O. (2024). How effective are trade policy and monetary policy in achieving a pathway to sustainable development? Evidence from a wavelet quantile‐on‐quantile Granger causality analysis. Sustainable Development, 33(1), 861-877. https://doi.org/10.1002/sd.3157
  2. Adebayo, T. S., & Özkan, O. (2024). Evaluating the role of financial globalisation and oil consumption on ecological quality: A new perspective from quantile-on-quantile granger causality. Heliyon, 10(2). https://doi.org/10.1016/j.heliyon.2024.e24636
  3. Adebayo, T. S., Olanrewaju, V. O., & Uzun, B. (2025). Do energy patents and energy prices drive the shift toward sustainable energy sources? A wavelet quantile-based analysis. Geoscience Frontiers, 102101. https://doi.org/10.1016/j.gsf.2025.102101
  4. Adebayo, T. S., Özkan, O., Uzun Ozsahin, D., Eweade, B. S., & Gyamfi, B. A. (2025). Exploring the role of ICT adoption technologies and renewable energy consumption in achieving a sustainable environment in the United States: an SDGs-based policy framework. Environmental Sciences Europe, 37(1), 20. https://doi.org/10.1186/s12302-025-01059-z
  5. Aharon, D. Y., Demir, E., & Umar, Z. (2025). On the connectedness between climate policy uncertainty, green bonds, and equity. Modern Finance, 3(1), 25-37. https://doi.org/10.3390/mf.v3i1.230
  6. Aydoğdu, A., & Çakır, H. M. (2025). Volatility Modelling of Cryptocurrencies According to Different Investment Horizons: The Case of Bitcoin. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 12(2), 724-749. https://doi.org/10.30798/makuiibf.1609311
  7. Aydoğdu, A., & Uyar, U. (2025). Enerji ve Kıymetli Metal Piyasaları Arasında Yayılım Etkisi: Wavelet Uyum Analizine Dayalı DCC-GARCH Yaklaşımı. Sosyoekonomi, 33(64), 557-585. https://doi.org/10.17233/sosyoekonomi.2025.02.24
  8. Barnett, M. (2023). Climate change and uncertainty: An asset pricing perspective. Management Science, 69(12), 7562-7584. https://doi.org/10.1287/mnsc.2022.4635
  9. Bouri, E., Iqbal, N., & Klein, T. (2022). Climate policy uncertainty and the price dynamics of green and brown energy stocks. Finance Research Letters, 47, 102740. https://doi.org/10.1016/j.frl.2022.102740
  10. Bouri, E., Rognone, L., Sokhanvar, A., & Wang, Z. (2023). From climate risk to the returns and volatility of energy assets and green bonds: A predictability analysis under various conditions. Technological Forecasting and Social Change, 194, 122682. https://doi.org/10.1016/j.techfore.2023.122682
  11. Broock, W. A., Scheinkman, J. A., Dechert, W. D., & LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric reviews, 15(3), 197-235. https://doi.org/10.1080/07474939608800353
  12. 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
  13. Diaz-Rainey, I., Gehricke, S. A., Roberts, H., & Zhang, R. (2021). Trump vs. Paris: The impact of climate policy on US listed oil and gas firm returns and volatility. International Review of Financial Analysis, 76, 101746. https://doi.org/10.1016/j.irfa.2021.101746
  14. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431. https://doi.org/10.1080/01621459.1979.10482531
  15. Dong, X., Wang, K. H., Tao, R., Sorana, V., & Moldovan, N. C. (2024). Is there a relationship between climate policy uncertainty and green finance? Evidence from bootstrap rolling window test. Economic Analysis and Policy, 82, 277-289. https://doi.org/10.1016/j.eap.2024.03.013
  16. Engle, R. F., Giglio, S., Kelly, B., Lee, H., & Stroebel, J. (2020). Hedging climate change news. The Review of Financial Studies, 33(3), 1184-1216. https://doi.org/10.1093/rfs/hhz072
  17. Gavriilidis, K. (2021). Measuring climate policy uncertainty. Available at SSRN 3847388. https://dx.doi.org/10.2139/ssrn.3847388
  18. Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society, 424-438. https://doi.org/10.2307/1912791
  19. Gulal, O. S., Topcu, M., Emirmahmutoglu, F., & Daud, A. F. (2025). Climate policy uncertainty and green bond markets. Environmental Research Communications. https://doi.org/10.1088/2515-7620/ade36b
  20. He, M., & Zhang, Y. (2022). Climate policy uncertainty and the stock return predictability of the oil industry. Journal of International Financial Markets, Institutions and Money, 81, 101675. https://doi.org/10.1016/j.intfin.2022.101675
  21. Hoque, M. M., Jakowski, N., & Prol, F. S. (2022). A new climatological electron density model for supporting space weather services. Journal of Space Weather and Space Climate, 12, 1
  22. Huo, D., Bagadeem, S., Elsherazy, T. A., Nasnodkar, S. P., & Kalra, A. (2023). Renewable energy consumption and the rising effect of climate policy uncertainty: Fresh policy analysis from China. Economic Analysis and Policy, 80, 1459-1474. https://doi.org/10.1016/j.eap.2023.10.017
  23. Husain, S., Sohag, K., & Wu, Y. (2022). The response of green energy and technology investment to climate policy uncertainty: An application of twin transitions strategy. Technology in Society, 71, 102132. https://doi.org/10.1016/j.techsoc.2022.102132
  24. Ilhan, E., Sautner, Z., & Vilkov, G. (2021). Carbon tail risk. The Review of Financial Studies, 34(3), 1540-1571. https://doi.org/10.1093/rfs/hhaa071
  25. Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics letters, 6(3), 255-259. https://doi.org/10.1016/0165-1765(80)90024-5
  26. Khalfaoui, R., Mefteh-Wali, S., Viviani, J. L., Jabeur, S. B., Abedin, M. Z., & Lucey, B. M. (2022). How do climate risk and clean energy spillovers, and uncertainty affect US stock markets?. Technological Forecasting and Social Change, 185, 122083. https://doi.org/10.1016/j.techfore.2022.122083
  27. Kirikkaleli, D., Sowah Jr, J. K., Addai, K., & Altuntaş, M. (2023). Energy productivity and environmental quality in Sweden: Evidence from Fourier and non‐linear based approaches. Geological Journal, 58(9), 3452-3465. https://doi.org/10.1002/gj.4684
  28. Köycü, E. (2025). Possıble Effects of Uncertaınty Factors on Green Bond Returns. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (68), 131-146. https://doi.org/10.30794/pausbed.1611428
  29. Lasisi, L., Omoke, P. C., & Salisu, A. A. (2024). Climate policy uncertainty and stock market volatility. Asian Economics Letters, 5(2). https://doi.org/10.46557/001c.37246
  30. Li, G., Li, Y., & Tsai, C. L. (2015). Quantile correlations and quantile autoregressive modeling. Journal of the American Statistical Association, 110(509), 246-261, https://doi.org/10.1080/01621459.2014.892007
  31. Liang, C., Umar, M., Ma, F., & Huynh, T. L. (2022). Climate policy uncertainty and world renewable energy index volatility forecasting. Technological Forecasting and Social Change, 182, 121810. https://doi.org/10.1016/j.techfore.2022.121810
  32. Liu, M. (2022). The driving forces of green bond market volatility and the response of the market to the COVID-19 pandemic. Economic Analysis and Policy, 75, 288-309. https://doi.org/10.1016/j.eap.2022.05.012
  33. Lv, W., & Li, B. (2023). Climate policy uncertainty and stock market volatility: Evidence from different sectors. Finance Research Letters, 51, 103506. https://doi.org/10.1016/j.frl.2022.103506
  34. Menteşe, B. (2021). Yeşil Tahvilin Gelişimi ve Türkiye’deki Uygulamaları. Uluslararası Muhasebe ve Finans Araştırmaları Dergisi, 3(1), 94-117. https://izlik.org/JA38YR87PC
  35. Naifar, N. (2024). Climate policy uncertainty and comparative reactions across sustainable sectors: Resilience or vulnerability?. Finance Research Letters, 65, 105543. https://doi.org/10.1016/j.frl.2024.105543
  36. Özkan, O., Adebayo, T. S., & Usman, O. (2024). Dynamic connectedness of clean energy markets, green markets, and sustainable markets: The role of climate policy uncertainty. Energy, 303, 131957. https://doi.org/10.1016/j.energy.2024.131957
  37. Özkan, O., Eweade, B. S., & Usman, O. (2024). Assessing the impact of resource efficiency, renewable energy R&D spending, and green technologies on environmental sustainability in Germany: evidence from a wavelet quantile-on-quantile regression. Journal of Cleaner Production, 450, 141992. https://doi.org/10.1016/j.jclepro.2024.141992
  38. Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. biometrika, 75(2), 335-346. https://doi.org/10.1093/biomet/75.2.335
  39. Policy Uncertainty. (2025). Economic Policy Uncertainty Index. https://www.policyuncertainty.com
  40. Raza, S. A., Khan, K. A., Benkraiem, R., & Guesmi, K. (2024). The importance of climate policy uncertainty in forecasting the green, clean and sustainable financial markets volatility. International Review of Financial Analysis, 91, 102984. https://doi.org/10.1016/j.irfa.2023.102984
  41. Ren, X., Li, J., He, F., & Lucey, B. (2023). Impact of climate policy uncertainty on traditional energy and green markets: Evidence from time-varying granger tests. Renewable and sustainable energy reviews, 173, 113058. https://doi.org/10.1016/j.rser.2022.113058
  42. Schlenker, W., & Taylor, C. A. (2021). Market expectations of a warming climate. Journal of financial economics, 142(2), 627-640. https://doi.org/10.1016/j.jfineco.2020.08.019.
  43. Shabir, M., Ali, M., Hashmi, S. H., & Bakhsh, S. (2022). Heterogeneous effects of economic policy uncertainty and foreign direct investment on environmental quality: cross-country evidence. Environmental Science and Pollution Research, 29(2), 2737-2752. https://doi.org/10.1007/s11356-021-15715-3
  44. Shang, Y., Han, D., Gozgor, G., Mahalik, M. K., & Sahoo, B. K. (2022). The impact of climate policy uncertainty on renewable and non-renewable energy demand in the United States. Renewable Energy, 197, 654-667. https://doi.org/10.1016/j.renene.2022.07.159
  45. Siddique, M. A., Nobanee, H., Hasan, M. B., Uddin, G. S., Hossain, M. N., & Park, D. (2023). How do energy markets react to climate policy uncertainty? Fossil vs. renewable and low-carbon energy assets. Energy Economics, 128, 107195. https://doi.org/10.1016/j.eneco.2023.107195
  46. Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking & Finance, 55, 1-8. https://doi.org/10.1016/j.jbankfin.2015.01.013
  47. Sunday Adebayo, T., Abbas, S., Olanrewaju, V. O., & Uzun, B. (2025). Unpacking policy ambiguities in residential and commercial renewable energy adoption: a novel multivariate wavelet quantile regression analysis. Applied Economics, 1-22. https://doi.org/10.1080/00036846.2025.2590632
  48. Syed, Q. R., Apergis, N., & Goh, S. K. (2023). The dynamic relationship between climate policy uncertainty and renewable energy in the US: Applying the novel Fourier augmented autoregressive distributed lags approach. Energy, 275, 127383. https://doi.org/10.1016/j.energy.2023.127383
  49. Tian, H., Long, S., & Li, Z. (2022). Asymmetric effects of climate policy uncertainty, infectious diseases-related uncertainty, crude oil volatility, and geopolitical risks on green bond prices. Finance Research Letters, 48, 103008. https://doi.org/10.1016/j.frl.2022.103008
  50. Treepongkaruna, S., Chan, K. F., & Malik, I. (2023). Climate policy uncertainty and the cross-section of stock returns. Finance Research Letters, 55, 103837. https://doi.org/10.1016/j.frl.2023.103837
  51. Wang, C. W., Wu, Y. C., Hsieh, H. Y., Huang, P. H., & Lin, M. C. (2022). Does green bond issuance have an impact on climate risk concerns?. Energy Economics, 111, 106066. https://doi.org/10.1016/j.eneco.2022.106066
  52. Wang, H., Li, S., & Ma, Y. (2023a). Climate policy and financial system stability: evidence from Chinese fund markets. Climate Policy, 23(4), 395-408. https://doi.org/10.1080/14693062.2022.2104790
  53. Wang, J., Mishra, S., Sharif, A., & Chen, H. (2024). Dynamic spillover connectedness among green finance and policy uncertainty: Evidence from QVAR network approach. Energy Economics, 131, 107330. https://doi.org/10.1016/j.eneco.2024.107330
  54. Wang, K. H., Wang, Z. S., Yunis, M., & Kchouri, B. (2023b). Spillovers and connectedness among climate policy uncertainty, energy, green bond and carbon markets: A global perspective. Energy Economics, 128, 107170. https://doi.org/10.1016/j.eneco.2023.107170
  55. Yang, D. X., Jing, Y. Q., Wang, C., Nie, P. Y., & Sun, P. (2021). Analysis of renewable energy subsidy in China under uncertainty: Feed-in tariff vs. renewable portfolio standard. Energy Strategy Reviews, 34, 100628. https://doi.org/10.1016/j.esr.2021.100628
  56. Ying, W., Ramzan, M., Olanrewaju, V. O., Brika, S. K., & Abozrib, A. (2025). The role of democratic governance and patent innovations in advancing sustainable energy transition. Energy & Environment, 0958305X251343060. https://doi.org/10.1177/0958305X251343060
  57. 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
  58. Zhu, P., Lu, T., & Wei, Y. (2025). Can Chinese green bond play a long-run safe haven for different crude oil under multiple uncertainties? A comparative analysis with the US green bond. Applied Economics Letters, 32(6), 812-816. https://doi.org/10.1080/13504851.2023.2289416