Journals / Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2026 / Cilt: 17 Sayı: 33

THE EXTENDED JOINT CONNECTEDNESS TVP-VAR APPROACH FOR EXAMINING POLICY, FINANCIAL, AND ENERGY UNCERTAINTIES ON GREEN BONDS

POLİTİKA, FİNANSAL VE ENERJİ BELİRSİZLİKLERİNİN YEŞİL TAHVİLLER ÜZERİNDEKİ ETKİSİNİ İNCELEMEYE YÖNELİK GENİŞLETİLMİŞ ORTAK BAĞLANTILILIK TVP-VAR YAKLAŞIMI

Çiğdem Kurt Cihangir(İZMİR BAKIRÇAY ÜNİVERSİTESİ)0000-0003-1761-1038
Özge Dinç Cavlak(ANKARA HACI BAYRAM VELİ ÜNİVERSİTESİ)0000-0002-7728-983X
Pages
104–128
Publication Date
2026-06-28
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Empirical findings reveal a moderate level of connectedness between green bond markets and the three uncertainty channels, with financial market volatility being the most dominant transmission mechanism.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study explores the time-varying and directional connectedness of the United States and China green bond markets in response to multidimensional uncertainty.
📊
Key Findings & Data:The empirical analysis applies a TVP-VAR based extended joint connectedness scheme to trace evolving spillovers over the 2014 to 2024 period.
💡
Conclusion & Impact:Empirical findings reveal a moderate level of connectedness between green bond markets and the three uncertainty channels, with financial market volatility being the most dominant transmission mechanism.
🔬Design:Araştırma Makalesi
📍Scope:2014 Verileri
⚙️Variables:Yeşil tahvil piyasaları · ortak bağlantılılık · TVP-VAR

Abstract

This study explores the time-varying and directional connectedness of the United States and China green bond markets in response to multidimensional uncertainty. By jointly examining two leading green bond markets, the analysis offers a comparative perspective on how spillovers evolve across different institutional and market settings. Uncertainty measures are organized into three mechanism-based channels, policy related uncertainty (EPU, GPR, CPU), financial market uncertainty (VIX, MOVE), and energy market uncertainty (OVX), to facilitate interpretation and comparative inference. The empirical analysis applies a TVP-VAR based extended joint connectedness scheme to trace evolving spillovers over the 2014 to 2024 period. Empirical findings reveal a moderate level of connectedness between green bond markets and the three uncertainty channels, with financial market volatility being the most dominant transmission mechanism. Political uncertainty demonstrates more variable and time-contingent effects, while energy market-based uncertainty exhibits context-sensitive and alternating impacts. Beyond documenting average connectedness, the results indicate that the net transmitter and net receiver roles of markets and uncertainty channels change across subperiods, underscoring the importance of accounting for evolving regimes when assessing spillovers. Overall, this study strengthens dynamic connectivity findings with a channel-focused mechanism perspective and provides comparative insights into how leading green bond markets react to evolving uncertainty dynamics.

Özet

Bu çalışma, ABD ve Çin yeşil tahvil piyasalarının çok boyutlu belirsizliğe yanıt olarak zamanla değişen ve yönlü bağlantılılığını incelemektedir. Önde gelen iki yeşil tahvil piyasasının eşanlı analizi, yayılma etkilerinin farklı kurumsal ve piyasa yapılarında nasıl bir gelişim gösterdiğine dair karşılaştırmalı bir perspektif sunmaktadır. Belirsizlik ölçütleri; yorumlamayı ve karşılaştırmalı çıkarımları kolaylaştırmak amacıyla mekanizma temelli üç kanal altında sınıflandırılmıştır: politika kaynaklı belirsizlik (EPU, GPR, CPU), finansal piyasa belirsizliği (VIX, MOVE) ve enerji piyasası belirsizliği (OVX). Ampirik analizde, 2014-2024 dönemi boyunca dinamik yayılma etkilerini analiz etmek amacıyla TVP-VAR tabanlı genişletilmiş ortak bağlantılılık çerçevesi uygulanmıştır. Ampirik bulgular, yeşil tahvil piyasaları ile üç belirsizlik kanalı arasında orta düzeyde bir bağlantılılık olduğunu ortaya koymakta; finansal piyasa oynaklığı en baskın iletim mekanizması olarak bulunmaktadır. Politik belirsizlik daha değişken ve zamana bağlı etkiler gösterirken, enerji piyasası temelli belirsizlik, bağlama duyarlı ve alternatif etkiler sergilemektedir. Ortalama bağlantılılık bulgularının ötesinde sonuçlar, piyasaların ve belirsizlik kanallarının net verici ve net alıcı rollerinin alt dönemler itibarıyla değiştiğini teyit ederek, yayılma etkilerinin değerlendirilmesinde rejim değişikliklerinin dikkate alınmasının önemini vurgulamaktadır. Genel olarak bu çalışma, dinamik bağlantılılık bulgularını kanal odaklı bir mekanizma perspektifiyle güçlendirmekte ve öncü yeşil tahvil piyasalarının gelişen belirsizlik dinamiklerine nasıl tepki verdiklerine dair karşılaştırmalı içgörüler sunmaktadır.

🔖

Cite This Article

Cihangir, Ç. K., Cavlak, Ö. D. (2026). THE EXTENDED JOINT CONNECTEDNESS TVP-VAR APPROACH FOR EXAMINING POLICY, FINANCIAL, AND ENERGY UNCERTAINTIES ON GREEN BONDS. Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Cilt: 17 Sayı: 33, 104–128. https://doi.org/10.36543/kauiibfd.2026.006

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (66)

  1. Abdelmalek, W., & M. Khemakhem. (2025). Exploring the Connectedness Between Green Bonds and Financial Markets and Its Drivers During Recent Crises: New Evidence From a TVP-VAR Extended Joint Connectedness Approach and Wavelet Coherence Analysis. International Journal of Finance & Economics 1–20. https://doi.org/10.1002/ijfe.70035.
  2. Adebayo, T. S., Akadiri, S. S., & Rjoub, H. (2022). On the relationship between economic policy uncertainty, geopolitical risk, and stock market returns in South Korea: A quantile causality analysis. Annals of Financial Economics, 17(1), 225–205. https://doi.org/10.1142/S2010495222500087
  3. Antonakakis, N. & Gabauer, D. (2017). Refined measures of dynamic connectedness based on TVP-VAR. MPRA Paper 78282, University Library of Munich, Germany.
  4. 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
  5. Arif, M., Naeem, M. A., Farid, S., Nepal, R. & Jamasb, T. (2022). Diversifier or More? Hedge and Safe Haven Properties of Green Bonds During COVID-19. Energy Policy, 168, 113102. https://doi.org/10.1016/j.enpol.2022.113102
  6. Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636. https://doi.org/10.1093/qje/qjw024
  7. Balcilar, M., Gabauer, D., & Umar, Z. (2021). Crude Oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach. Resources Policy, 73, 102219. https://doi.org/10.1016/j.resourpol.2021.102219
  8. Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77 (3), 623-685 https://doi.org/10.3982/ECTA6248
  9. Bouri, E., Gök, R., Gemici, E., & Kara, E. (2024). Do geopolitical risk, economic policy uncertainty, and oil implied volatility drive assets across quantiles and time-horizons?. The Quarterly Review of Economics and Finance, 93, 137-154. https://doi.org/10.1016/j.qref.2023.12.004
  10. 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
  11. Caldara, D. & Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review, 112(4), 1194-1225. https://doi.org/10.1257/aer.20191823
  12. Demir, S. (2024). Yatırımcı duyarlılığı ile ABD geleneksel ve yeşil tahvil piyasaları arasındaki bağlantılılık analizi. KOCATEPEİİBFD, 26(2), 264-275. https://doi.org/10.33707/akuiibfd.1451827
  13. Dickey, D. A., and Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 1057-1072. https://doi.org/10.2307/1912517
  14. 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
  15. 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
  16. Diebold, F.X. & Yılmaz, 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
  17. Doğan, B., Trabelsi, N., Tiwari, A. K., & Ghosh, S. (2023). Dynamic dependence and causality between crude oil, green bonds, commodities, geopolitical risks, and policy uncertainty. The Quarterly Review of Economics and Finance, 89, 36–62. https://doi.org/10.1016/j.qref.2023.02.006
  18. Dogan, E., Luni, T., Majeed, M. T. & Tzeremes, P. (2023). The nexus between global carbon and renewable energy sources: A step towards sustainability, Journal of Cleaner Production. 416. https://doi.org/10.1016/j.jclepro.2023.137927.
  19. Dogan, E., Madaleno, M., Taskin, D., Tzeremes, P. (2022). Investigating the spillovers and connectedness between green finance and renewable energy sources. Renewable Energy, 197, 709-722. https://doi.org/10.1016/j.renene.2022.07.131
  20. World Economic Forum, (WEF) (2023) Global Risks Report 2023. https://www.weforum.org/publications/global-risks-report-2023/
  21. Elsayed, A. H., Naifar, N. & Nasreen, S. (2022). Dependence structure and dynamic connectedness between green bonds and financial markets: fresh insights from time-frequency analysis before and during Covid-19 pandemic. Energy Economics, 107. https://doi.org/10.1016/j.eneco.2022.105842
  22. Engle, R. F., Ito, T., & Lin, W.-L. (1990). Meteor Showers or Heat Waves? Heteroskedastic Intra-Daily Volatility in the Foreign Exchange Market. Econometrica, 58(3), 525-542. https://doi.org/10.3386/w2609
  23. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417. https://doi.org/10.2307/2325486
  24. Fasanya, I. O. & Mubaiwa, D. T. (2025). Dynamics of extreme spillovers between clean energy stocks and fossil fuels: The role of climate policy uncertainty and geopolitical risks, Energy, 335. https://doi.org/10.1016/j.energy.2025.138267.
  25. Flammer, C. (2021). Corporate green bonds. Journal of Financial Economics, 142(2), 499-516. https://doi.org/10.1016/j.jfineco.2021.01.010
  26. Gao, Y., Li, Y., & Wang, Y. (2021). Risk spillover and network connectedness analysis of China’s green bond and financial markets: evidence from financial events of 2015–2020. The North American Journal of Economics and Finance, 57. https://doi.org/10.1016/j.najef.2021.101386
  27. Gavriilidis, K. (2021). Measuring climate policy uncertainty. Available at SSRN 3847388. https://dx.doi.org/10.2139/ssrn.3847388
  28. Geng, J-B., Liu, C., Ji, Q., & Zhang, D. (2021). Do oil price changes really matter for clean energy returns? Renewable and Sustainable Energy Review, 150, 111429. https://doi.org/10.1016/j.rser.2021.111429
  29. Gong, Y., Li, X, & Xue, W. (2023). The Impact of EPU spillovers on the bond market volatility: Global evidence. Finance Research Letters, 55, 103931. https://doi.org/10.1016/j.frl.2023.103931
  30. He, Z. (2023). Geopolitical risks and investor sentiment: Causality and TVP-VAR analysis. The North American Journal of Economics and Finance, 67, 101947. https://doi.org/10.1016/j.najef.2023.101947
  31. Huang, Y., & Luk, P. (2020). Measuring economic policy uncertainty in China. China Economic Review, 59, 101367. https://doi.org/10.1016/j.chieco.2019.101367
  32. Climate Bonds Initiative (CBI). (2023). Global State of the Market Report 2023. https://www.climatebonds.net/files/documents/publications/Global-State-of-the-Market-Report-2023.pdf
  33. Kayani, U., Hassan, M. K., Dejan, A., Khan, M., & Nawaz, F. (2024). Assessment of economic policy uncertainty spillovers: A cross-border analysis of global and BRIC economies. International Economics, 179, 100530. https://doi.org/10.1016/j.inteco.2024.100530
  34. Kocaarslan, B. (2021). How does the reserve currency (US dollar) affect the diversification capacity of green bond investments?. Journal of Cleaner Production, 307. http://dx.doi.org/10.1016/j.jclepro.2021.127275
  35. Kocaarslan, B. (2024). Dynamic spillovers between oil market, monetary policy, and exchange rate dynamics in the US. Finance Research Letters, 69, 106137. https://doi.org/10.1016/j.frl.2024.106137
  36. 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
  37. Lastrapes, W.D. & Wiesen T.F. (2021). The joint spillover index. Economic Modelling, 94, 681-691. https://doi.org/10.1016/j.econmod.2020.02.010
  38. Lee, C. C., Tang, H., & Li, D. (2022). The roles of oil shocks and geopolitical uncertainties on China’s green bond returns. Economic Analysis and Policy, 74, 494-505. https://doi.org/10.1016/j.eap.2022.03.008
  39. Lin, B. & Su, T. (2023). Uncertainties and green bond markets: Evidence from tail dependence. International Journal of Finance & Economics, 28 (4), 4458-4475. https://doi.org/10.1002/ijfe.2659
  40. 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
  41. Long, S., Tian, H., & Li, Z. (2022). Dynamic spillovers between uncertainties and green bond markets in the US, Europe, and China: evidence from the quantile VAR framework. International Review Financial Analysis, 84, 102416. https://doi.org/10.1016/j.irfa.2022.102416
  42. Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
  43. Mertzanis, C., & Tebourbi, I. (2024). Geopolitical risk and global green bond market growth. European Financial Management. https://doi.org/10.1111/eufm.12484
  44. 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
  45. Pham, S.D., Nguyen, T.T.T., & Do, H.X. (2024). Impact of climate policy uncertainty on return spillover among green assets and portfolio implications. Energy Economics, 134, 107631. https://doi.org/10.1016/j.eneco.2024.107631
  46. Pham L. & Nguyen, C.P. (2022). How do stock, oil, and economic policy uncertainty influence the green bond market? Finance Research Letters, 45. https://doi.org/10.1016/j.frl.2021.102128
  47. Pham, L. (2016). Is it risky to go green? A volatility analysis of the green bond market. Journal of Sustainable Finance & Investment, 6 (4), 263-291 https://doi.org/10.1080/20430795.2016.1237244
  48. Pham, L. & Cepni, O. (2022). Extreme directional spillovers between investor attention and green bond markets. International Review of Economics & Finance, 80, 186-210. https://doi.org/10.1016/j.iref.2022.02.069
  49. Phillips, P. C. B., & 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
  50. Reboredo, J. C. (2018). Green bond and financial markets: Co-movement, diversification and price spillover effects. Energy Economics, 74, 38-50. https://doi.org/10.1016/j.eneco.2018.05.030
  51. Reboredo, J. C., & Ugolini, A. (2020). Price connectedness between green bond and financial markets. Economic Modelling, 88, 25–38. https://doi.org/10.1016/j.econmod.2019.09.004
  52. Ren, X., Xiao, Y., He, F., & Gozgor, G. (2024). The contagion of extreme risks between fossil and green energy markets: evidence from China. Quantitative Finance, 24(5), 627–642. https://doi.org/10.1080/14697688.2024.233937
  53. 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
  54. Rumokoy, L. J., Omura, A., & Roca, E. (2023). Geopolitical risk and corporate investment in the metals and mining industry: Evidence from Australia. Pacific-Basin Finance Journal, 79, 101991. https://doi.org/10.1016/j.pacfin.2023.101991
  55. Saeed, T., Bouri, E., & Tran, D. K. (2020). Hedging strategies of green assets against dirty energy assets. Energies, 13, 3141. https://doi.org/10.3390/en13123141
  56. Sohag, K., Hammoudeh, S., Elsayed, A. H., Mariev, O. & Safonova, Y. (2022). Do geopolitical events transmit opportunity or threat to green markets? Decomposed measures of geopolitical risks. Energy Economics, 111, 106068. https://doi.org/10.1016/j.eneco.2022.106068
  57. Tang, D.Y. & Zhang, Y. (2020). Do shareholders benefit from green bonds? Journal of Corporate Finance, 61, 101427. https://doi.org/10.1016/j.jcorpfin.2018.12.001
  58. Tiwari, A.K., Abakah, E.J.A., Gabauer, D., & Dwumfour, R.A. (2021). Dynamic spillover effects among green bond, renewable energy stocks and carbon markets during COVID-19 pandemic: implications for hedging and investments strategies. Global Finance J., 51. https://doi.org/10.1016/j.gfj.2021.100692
  59. 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
  60. Wan, Y.F., Wang, M.H., & Wu, F.L. (2025). Greater fragility, greater exposure: a network-based analysis of climate policy uncertainty shocks and G20 stock markets stability, The North American Journal of Economics and Finance, 76, 102343. https://doi.org/10.1016/j.najef.2024.102343
  61. Wei, P., Qi, Y., Ren, X. & Duan, K. (2022). Does economic policy uncertainty affect green bond markets? Evidence from wavelet-based quantile analysis. Emerging Markets Finance and Trade, 58(15), 4375-4388. https://doi.org/10.1080/1540496X.2022.2069487
  62. Xia, Y., Chen, Y., He, L., Shi, Z., Ji, X., & Cai, R. (2025). Can green bonds hedge against geopolitical risk? A cross-market connectedness analysis with portfolio implications. Technological and Economic Development of Economy, 31(1), 92–130. https://doi.org/10.3846/tede.2024.22088
  63. Xia, Y., Shi, Z., Du, X., Niu, M., & Cai, R. (2023). Can green assets hedge against economic policy uncertainty? Evidence from China with portfolio implications. Financial Research Letters, 55, 103874. https://doi.org/10.1016/j.frl.2023.103874
  64. Yousaf, I., Mensi, W., Vo, X.V., & Kang, S.H. (2024). Dynamic spillovers and connectedness between crude oil and green bond markets. Resources Policy, 89, 104594. https://doi.org/10.1016/j.resourpol.2023.104594
  65. 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
  66. Zhao, D., Li, P., Yang, M., & Lian, Y. (2024). How do risk shocks reshape the spillovers among the oil, gold, emerging, and developed markets? Evidence from a new TVP-VAR-based wavelet coherence framework. Applied Economics, 57(43), 6884–6900. https://doi.org/10.1080/00036846.2024.2386862