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Journals / Muhasebe ve Finansman Dergisi / 2026 / Sayı: 111

Artificial Intelligence, Digital Finance, and the Green Bond Market Nexus: Volatility Spillover Effects

Yapay Zekâ, Dijital Finans ve Yeşil Tahvil Piyasası İlişkisi: Volatilite Yayılım Etkileri

Tuğba Nur(ŞIRNAK ÜNİVERSİTESİ)0000-0002-0974-4896
Emre Esat Topaloğlu(ŞIRNAK ÜNİVERSİTESİ)0000-0001-8771-779X
Türker Kaymaz0000-0001-7006-8412
İlhan Ege(MERSİN ÜNİVERSİTESİ)0000-0002-5765-1926
Pages
19–58
Publication Date
2026-07-11
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AI Summary & Key Takeaways

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

The findings indicate that the most appropriate model for the FinTech index is EGARCH(1,1), while TGARCH(1,1) is the most suitable for the artificial intelligence and green bond indices.

📌 3 Key Takeaways

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Objective & Hypothesis:The study examines the volatility dynamics and spillover effects among artificial intelligence, FinTech, and green bond indices during the period from June 15, 2018, to April 17, 2025, using ARCH/GARCH-type conditional heteroskedasticity models, as well as the CCC-GARCH, QDCC-GARCH, and QFCON approaches.
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Key Findings & Data:The findings indicate that the most appropriate model for the FinTech index is EGARCH(1,1), while TGARCH(1,1) is the most suitable for the artificial intelligence and green bond indices.
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Conclusion & Impact:QFCON results confirm that FinTech acts as a net shock transmitter, while green bonds are predominantly shock absorbers.
🔬Design:Modeling & Simulation
📍Scope:2018 Verileri
⚙️Variables:Fintek · yapay zekâ · yeşil tahvil

Abstract

The study examines the volatility dynamics and spillover effects among artificial intelligence, FinTech, and green bond indices during the period from June 15, 2018, to April 17, 2025, using ARCH/GARCH-type conditional heteroskedasticity models, as well as the CCC-GARCH, QDCC-GARCH, and QFCON approaches. The findings indicate that the most appropriate model for the FinTech index is EGARCH(1,1), while TGARCH(1,1) is the most suitable for the artificial intelligence and green bond indices. Significant volatility clustering was observed in all indices during the pandemic period. FinTech shocks are more persistent and asymmetric. The artificial intelligence market, however, exhibits the highest shock persistence. CCC-TGARCH results reveal a bidirectional volatility spillover between FinTech and artificial intelligence, while green bonds exhibit weaker spillover behavior. QDCC-GARCH findings show that conditional correlations vary by time and quantiles, with the strongest link observed between artificial intelligence and FinTech. QFCON results confirm that FinTech acts as a net shock transmitter, while green bonds are predominantly shock absorbers.

Özet

Çalışma, 15 Haziran 2018–17 Nisan 2025 döneminde yapay zeka, FinTech ve yeşil tahvil endeksleri arasındaki volatilite dinamiklerini ve yayılma etkilerini ARCH/GARCH tipi koşullu heteroskedastisite modelleri, CCC-GARCH, QDCC-GARCH ve QFCON yaklaşımlarıyla incelemektedir. Bulgular, FinTech endeksi için en uygun modelin EGARCH(1,1), yapay zeka ve yeşil tahvil endeksleri için ise TGARCH(1,1) olduğunu göstermektedir. Pandemi döneminde tüm endekslerde belirgin volatilite kümelenmesi gözlemlenmiştir. FinTech şokları daha kalıcı ve asimetriktir. Yapay zeka piyasası ise en yüksek şok kalıcılığına sahiptir. CCC-TGARCH sonuçları, FinTech ile yapay zeka arasında çift yönlü volatilite yayılımı olduğunu, yeşil tahvillerin ise daha zayıf yayılım davranışı sergilediğini ortaya koymaktadır. QDCC-GARCH bulguları, koşullu korelasyonların zaman ve kantillere göre değiştiğini ve en güçlü bağlantının yapay zeka ile FinTech arasında olduğunu göstermektedir. QFCON sonuçları ise FinTech’in net şok yayıcı, yeşil tahvillerin ise çoğunlukla şok alıcı olduğunu doğrulamaktadır.

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Cite This Article

Nur, T., Topaloğlu, E. E., Kaymaz, T., Ege, İ. (2026). Artificial Intelligence, Digital Finance, and the Green Bond Market Nexus: Volatility Spillover Effects. Muhasebe ve Finansman Dergisi, Sayı: 111, 19–58. https://doi.org/10.25095/mufad.1827721

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References (53)

  1. Abakah, Emmanuel J. A.-Tiwari, Aviral K.- Lee, Chi-Chuan- Ntow Gyamfi, Matthew (2023). “Quantile price convergence and spillover effects among Bitcoin, Fintech, and artificial intelligence stocks”. International Review of Finance, 23(1), 187-205. https://doi.org/10.1111/irfi.12393
  2. Adebayo, T. S.- Özkan, O. (2024). “Evaluating the Role of Financial Globalization and Oil Consumption on Ecological Quality: A New Perspective from Quantile-on-Quantile Granger Causality”. Heliyon, 10(2), e24636. https://doi.org/10.1016/j.heliyon.2024.e24636
  3. Adekoya, O. B.- Oliyide, Johnson A.- Saleem, O.- Adeoye, Habeeb A. (2022). “Asymmetric connectedness between Google-based investor attention and the fourth industrial revolution assets: The case of FinTech and Robotics & Artificial intelligence stocks”. Technology in Society, 68, 101925. https://doi.org/10.1016/j.techsoc.2022.101925
  4. Bollerslev, T. (1990). “Modelling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model”. The Review of Economics and Statistics, 498-505. https://doi.org/10.2307/2109358
  5. Bollerslev, T.- Engle, Robert F.- Wooldridge, Jeffrey M. (1988). “A capital asset pricing model with time-varying covariances”. Journal of Political Economy, 96(1), 116-131. https://doi.org/10.1086/261527
  6. Bollerslev, T. (1986). “Generalized autoregressive conditional heteroskedasticity”. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
  7. Broock, William. A.- Scheinkman, Jose. A.- Dechert, W. D.- LeBaron, Blake. (1996). “A test for independence based on the correlation dimension”. Econometric Reviews, 15(3), 197-235. https://doi.org/10.1080/07474939608800353
  8. Chatziantoniou, I., A.- Emmanuel J. A.- Gabauer, D. - Tiwari, Aviral K. (2022). “Quantile Time–Frequency Price Connectedness Between Green Bond, Green Equity, Sustainable Investments and Clean Energy Markets”. Journal of Cleaner Production, 361, 132088. https://doi.org/10.1016/j.jclepro.2022.132088
  9. Chen, H. (2023). “Fintech and investment risk of digital finance: mediating role of clean energy and green bonds through the dynamics of spill over”. Environmental Science and Pollution Research, 30(34), 82286-82296. https://doi.org/10.1007/s11356-023-28181-w
  10. Chen, Y.- Chiu, J.- Chung, H. (2022). “Givers or Receivers? Return and volatility spillovers between Fintech and the Traditional Financial Industry”. Finance Research Letters, 46, 102458. https://doi.org/10.1016/j.frl.2021.102458
  11. Dai, Z. (2023). “Exploring the synergies between digital finance and clean energy: a case study of green bond spillover effects”. Environmental Science and Pollution Research, 30(44), 100188-100202. https://doi.org/10.1007/s11356-023-29205-1
  12. Dickey, D. A.- Fuller, Wayne A. (1979). “Distribution of estimators for autoregressive time series with a unit root”. Journal of the American Statistical Association 74:427–431. https://doi.org/10.1080/01621459.1979.10482531
  13. El Khoury, R.- Nasrallah, N.- Hussainey, K.- Assaf, R. (2023). “Spillover analysis across FinTech, ESG, and renewable energy indices before and during the Russia–Ukraine war: International evidence”. Journal of International Financial Management & Accounting, 34(2), 279-317. https://doi.org/10.1111/jifm.12179
  14. Enders, Walter (2008). “Applied econometric time series”. John Wiley & Sons.
  15. Engle, R. F. (1982). “Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation”. Econometrica: Journal of the econometric society, 987-1007. https://doi.org/10.2307/1912773
  16. Gabauer, D. (2020). “Volatility Impulse Response Analysis for DCC‐GARCH Models: The Role of Volatility Transmission Mechanisms”. Journal of Forecasting, 39(5), 788–796. https://doi.org/10.1002/for.2658
  17. Gharbi, O.- Trichilli, Y.- Boujelbéne, M.(2023). “Risk spillovers connectedness between the US Fintech industry VaR, behavioral biases and macroeconomic instability factors: COVID-19 implications”. China Finance Review International, 13(3), 410-443. https://doi.org/10.1108/CFRI-12-2022-0277
  18. Glosten, L. R.- Jagannathan, R.- Runkle, D. E. (1993). “On the relation between the expected value and the volatility of the nominal excess return on stocks”. The Journal of Finance, 48(5), 1779-1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
  19. Ha, Le T. (2023). “Dynamic connectedness between FinTech innovation and energy volatility during the war in time of pandemic”. Environmental Science and Pollution Research, 30(35), 83530-83544. https://doi.org/10.1007/s11356-023-28089-5
  20. Hassan, M. Kabir- Hasan, M. Bokhtiar- Halim, Zairihan Abdul- Maroney, Neal- Rashid, M. Mamunur (2022). “Exploring the dynamic spillover of cryptocurrency environmental attention across the commodities, green bonds, and environment-related stocks”. The North American Journal of Economics and Finance, 61, 101700. https://doi.org/10.1016/j.najef.2022.101700
  21. Huynh, Toan L. D.- Hille, E.- Nasir, M. A. (2020). “Diversification in the age of the 4th industrial revolution: The role of artificial intelligence, green bonds and cryptocurrencies”. Technological Forecasting and Social Change, 159, 120188. https://doi.org/10.1016/j.techfore.2020.120188
  22. Jin, J.- Han, L.- Wu, L.- Zeng, H. (2020). “The hedging effect of green bonds on carbon market risk”. International Review of Financial Analysis, 71, 101509. https://doi.org/10.1016/j.irfa.2020.101509
  23. Jovović, J.- Popović, S. (2025). “Investigating volatility spillovers: Connectedness between green bonds, conventional bonds, and energy markets”. Research in International Business and Finance, 76, 102850. https://doi.org/10.1016/j.ribaf.2025.102850
  24. Khalfaoui, R.- Mefteh-W., Salma- D., Buhari- Ghosh, S. (2023). Extreme spillover effect of COVID-19 pandemic-related news and cryptocurrencies on green bond markets: A quantile connectedness analysis. International Review of Financial Analysis, 86, 102496. https://doi.org/10.1016/j.irfa.2023.102496
  25. Li, J.- Li, J.- Zhu, X.- Yao, Y.- Casu, B. (2020). “Risk spillovers between FinTech and traditional financial institutions: Evidence from the US”. International Review of Financial Analysis, 71, 101544. https://doi.org/10.1016/j.irfa.2020.101544
  26. 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
  27. Liu, N.- Liu, C.- Da, B.- Zhang, T.- Guan, F. (2021). “Dependence and risk spillovers between green bonds and clean energy markets”. Journal of Cleaner Production, 279, 123595. https://doi.org/10.1016/j.jclepro.2020.123595
  28. Malik, F.- Ewing, B. T.- Payne, James E. (2005). “Measuring volatility persistence in the presence of sudden changes in the variance of Canadian stock returns”. Canadian Journal of Economics/Revue canadienne d'économique, 38, 1037-1056. https://doi.org/10.1111/j.0008-4085.2005.00315.x
  29. Maneejuk, P.- Huang, W.- Yamaka, W. (2025). “Asymmetric volatility spillover effects from energy, agriculture, green bond, and financial market uncertainty on carbon market during major market crisis”. Energy Economics, 108430. https://doi.org/10.1016/j.eneco.2025.108430
  30. Mensi, W.- Shafiullah, M.- Vo, Xuan V.- Kang, Sang H. (2022). “Spillovers and connectedness between green bond and stock markets in bearish and bullish market scenarios”. Finance Research Letters, 49, 103120. https://doi.org/10.1016/j.frl.2022.103120
  31. Mensi, W.- Vo, Xuan V.- Ko, H. U.- Kang, S. H. (2023). “Frequency spillovers between green bonds, global factors and stock market before and during COVID-19 crisis”. Economic Analysis and Policy, 77, 558-580. https://doi.org/10.1016/j.eap.2022.12.010
  32. Naifar, N. (2025). “Dynamic connectedness and portfolio strategies: Insights from fintech, robotics, renewable energy, and green bonds in China”. Journal of Climate Finance, 10, 100060. https://doi.org/10.1016/j.jclimf.2025.100060
  33. Nelson, D. B. (1991). “Conditional heteroskedasticity in asset returns: A new approach”. Econometrica: Journal of the econometric society, 347-370. https://doi.org/10.2307/2938260
  34. Park, D.- Park, J.- Ryu, D. (2020). “Volatility spillovers between equity and green bond markets”. Sustainability, 12(9), 3722. https://doi.org/10.3390/su12093722
  35. Peng, G.- Ding, J.- Zhou, Z.- Zhu, L. (2023). “Measurement of spillover effect between green bond market and traditional bond market in China”. Green Finance, 5(4), 538. https://doi.org/10.3934/GF.2023021
  36. 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
  37. 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
  38. 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
  39. Shao, Y. H.- Yang, Y. H.- Zhou, W. X. (2025). “Dynamic spillovers and investment strategies across artificial intelligence ETFs, artificial intelligence tokens, and green markets”. arXiv preprint arXiv:2503.01148. https://doi.org/10.48550/arXiv.2503.01148
  40. Standard & Poor's (2025). Data & Analytics. https://www.spglobal.com
  41. Tang, C.- Aruga, K.- Hu, Y. (2023). “The dynamic correlation and volatility spillover among green bonds, clean energy stock, and fossil fuel market”. Sustainability, 15(8), 6586. https://doi.org/10.3390/su15086586
  42. Tiwari, A. K.- Abakah, E. J. A.- Adekoya, O. B.- Hammoudeh, S. (2023). “What do we know about the price spillover between green bonds and Islamic stocks and stock market indices?”. Global Finance Journal, 55, 100794. https://doi.org/10.1016/j.gfj.2022.100794
  43. Umar, Z.- Hadhri, S.- Abakah, E. J. A.- Usman, M. – Umar, M. (2024). “Return and volatility spillovers among oil price shocks and international green bond markets”. Research in International Business and Finance, 69, 102254. https://doi.org/10.1016/j.ribaf.2024.102254
  44. Wang, K. H.- Wang, Z. S.- Yunis, M.- Kchouri, B. (2023). “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
  45. Wen, S.- Li, J.- Huang, C.- Zhu, X. (2023). “Extreme risk spillovers among traditional financial and FinTech institutions: A complex network perspective”. The Quarterly Review of Economics and Finance, 88, 190-202. https://doi.org/10.1016/j.qref.2023.01.005
  46. Wu, R.- Qin, Z. (2024). “Asymmetric volatility spillovers among new energy, ESG, green bond and carbon markets”. Energy, 292, 130504. https://doi.org/10.1016/j.energy.2024.130504
  47. Xia, Y.- Ren, H.- L., Y.- Xia, J.- He, L.- Liu, N. (2022). “Forecasting green bond volatility via novel heterogeneous ensemble approaches”. Expert Systems with Applications, 204, 117580. https://doi.org/10.1016/j.eswa.2022.117580
  48. Yadav, M. P.- Pandey, A.- Taghizadeh-H., Farhad- Arya, V.- Mishra, N. (2023). “Volatility spillover of green bond with renewable energy and crypto market”. Renewable Energy, 212, 928-939. https://doi.org/10.1016/j.renene.2023.05.056
  49. Yousaf, I.- Ijaz, M. S.- Umar, M.- Li, Y. (2024). “Exploring volatility interconnections between AI tokens, AI stocks, and fossil fuel markets: evidence from time and frequency-based connectedness analysis”. Energy Economics, 133, 107490. https://doi.org/10.1016/j.eneco.2024.107490
  50. Yousaf, I.- Mensi, W.- Vo, X. Vinh- K., Sang 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
  51. Zhang, Y.- Umair, M. (2023). “Examining the interconnectedness of green finance: an analysis of dynamic spillover effects among green bonds, renewable energy, and carbon markets”. Environmental Science and Pollution Research, 30(31), 77605-77621. https://doi.org/10.1007/s11356-023-27870-w
  52. Zhao, M.- Park, H. (2024). “Quantile time-frequency spillovers among green bonds, cryptocurrencies, and conventional financial markets”. International Review of Financial Analysis, 93, 103198. https://doi.org/10.1016/j.irfa.2024.103198
  53. Zivot, E. (2009). “Practical Issues in the Analysis of Univariate GARCH Models”. Springer Science and Business Media, Germany, 113–15. https://doi.org/10.1007/978-3-540-71297-8_5