Interconnectedness and Risk Structure Among Digital Assets: Empirical Findings Based on the Generalized R² Approach (2020–2025)

Abstract

This study analyzes the time-varying interactions among assets in the digital financial asset market. Within the scope of the study, 1,820 daily observations from the 2020–2025 period for Ethereum, Ripple, Binance Coin, Cardano, Stellar, IOTA, Stacks, and Chainlink are examined using the Generalized R² method proposed by Balli et al. (2023). This approach reveals both contemporaneous and lagged interconnectedness between assets, thereby enabling an understanding of how dynamic relationships evolve over time. The results indicate that market interconnectedness is not stable over time and that the transmission of shocks tends to intensify particularly during periods of uncertainty. The findings show that Ethereum maintained a central role throughout the analysis period, while Cardano, STX, LINK, and IOTA were more exposed to shocks. These results underscore the necessity of policy frameworks that address not only individual asset risks but also contagion risks to promote market stability. From an investor’s perspective, it is recommended that portfolio compositions consider both contemporaneous and lagged effects.

Keywords

• Digital Financial Assets
• Cryptocurrency
• Interconnectedness
• Risk
• Generalized R²

Dijital Varlıklar Arasındaki Bağlantılılık ve Risk Yapısı: Genelleştirilmiş R² Yaklaşımına Dayalı Ampirik Bulgular (2020–2025)

Öz

Bu çalışma, dijital finansal varlık piyasasındaki varlıklar arasındaki zamanla değişen etkileşimleri analiz etmektedir. Çalışma kapsamında, Ethereum, Ripple, Binance Coin, Cardano, Stellar, IOTA, Stacks ve Chainlink için 2020-2025 dönemine ait 1.820 günlük gözlem, Balli ve diğerleri (2023) tarafından önerilen Genelleştirilmiş R² yöntemi kullanılarak incelenmiştir. Bu yaklaşım, varlıklar arasındaki eşzamanlı ve gecikmeli karşılıklı bağlantıları ortaya çıkararak, dinamik ilişkilerin zaman içinde nasıl geliştiğini anlamayı mümkün kılmaktadır. Sonuçlar, piyasa karşılıklı bağlantısının zaman içinde istikrarlı olmadığını ve şokların iletilmesinin özellikle belirsizlik dönemlerinde yoğunlaşma eğiliminde olduğunu göstermektedir. Bulgular, Ethereum'un analiz dönemi boyunca merkezi bir rol sürdürdüğünü, Cardano, STX, LINK ve IOTA'nın ise şoklara daha fazla maruz kaldığını göstermektedir. Bu sonuçlar, piyasa istikrarını teşvik etmek için yalnızca bireysel varlık risklerini değil, aynı zamanda bulaşma risklerini de ele alan politika çerçevelerinin gerekliliğini vurgulamaktadır. Yatırımcıların bakış açısından, portföy bileşimlerinde hem eşzamanlı hem de gecikmeli etkilerin dikkate alınması önerilmektedir.

Anahtar Kelimeler

• Dijital Finansal Varlıklar
• Kripto Varlıklar
• Bağlantılılık
• Risk
• Genelleştirilmiş R2

References

1. Akkus, H. T., & Dogan, M. (2024). Analysis of dynamic connectedness relationships between cryptocurrency, NFT and DeFi assets: TVP-VAR approach. Applied Economics Letters, 31(21), 2250-2255. https://doi.org/10.1080/13504851.2023.2216437
2. Anscombe, F. J., & Glynn, W. J. (1983). Distribution of the kurtosis statistic b2 for normal samples. Biometrika, 70(1), 227-234. https://doi.org/10.1093/biomet/70.1.227
3. Balcı, N. (2024). Volatility spillover effects between stock markets during the crisis periods: Diagonal BEKK approach. Pamukkale University Journal of Social Sciences Institute, (65), 1-18. https://doi.org/10.30794/pausbed.1462608
4. Balcı, N. (2025). Dynamic linkages between Turkish Islamic stock market and global macroeconomic risk factors: Evidence from DCC-GARCH model. Akademik Hassasiyetler, 12(27), 399-428. https://doi.org/10.58884/akademik-hassasiyetler.1590078
5. Balli, F., Balli, H. O., Dang, T. H. N., & Gabauer, D. (2023). Contemporaneous and lagged R2 decomposed connectedness approach: New evidence from the energy futures market. Finance Research Letters, 57, 104168. https://doi.org/10.1016/j.frl.2023.104168
6. Baur, D. G., & Hoang, L. T. (2021). The importance of spillovers. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3973795
7. Baur, D. G., Hong, K., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets?. Journal of International Financial Markets, Institutions and Money, 54, 177-189. https://doi.org/10.1016/j.intfin.2017.12.004
8. Beneki, C., Koulis, A., Kyriazis, N. A., & Papadamou, S. (2019). Investigating volatility transmission and hedging properties between Bitcoin and Ethereum. Research in International Business and Finance, 48, 219-227. https://doi.org/10.1016/j.ribaf.2019.01.001

9. Bouri, E., Cepni, O., Gabauer, D., & Gupta, R. (2021). Return connectedness across asset classes around the COVID-19 outbreak. International Review of Financial Analysis, 73, 101646. https://doi.org/10.1016/j.irfa.2020.101646
10. Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?. Finance Research Letters, 20, 192-198. https://doi.org/10.1016/j.frl.2016.09.025
11. Cheah, E. T., & Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32-36. https://doi.org/10.1016/j.econlet.2015.02.029
12. Conrad, C., Custovic, A., & Ghysels, E. (2018). Long-and short-term cryptocurrency volatility components: A GARCH-MIDAS analysis. Journal of Risk and Financial Management, 11(2), 23. https://doi.org/10.3390/jrfm11020023
13. Corbet, S., Lucey, B., & Yarovaya, L. (2018). Datestamping the Bitcoin and Ethereum bubbles. Finance Research Letters, 26, 81-88. https://doi.org/10.1016/j.frl.2017.12.006
14. Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182-199. https://doi.org/10.1016/j.irfa.2018.09.003
15. D'Agostino, R. B. (1970). Transformation to normality of the null distribution of g1. Biometrika, 57(3), 679-681. https://doi.org/10.2307/2334794
16. Dataset available website: https://tr.investing.com/
17. 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
18. 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
19. 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. https://doi.org/10.3390/su15076025
20. Elliott, B. Y. G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813-836. https://doi.org/10.3386/t0130
21. Fang, F., Ventre, C., Basios, M., Kanthan, L., Martinez-Rego, D., Wu, F., & Li, L. (2022). Cryptocurrency trading: A comprehensive survey. Financial Innovation, 8(1), 13. https://doi.org/10.1186/s40854-021-00321-6
22. 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(498), 777-787. https://doi.org/10.1080/01621459.2012.688465
23. Gong, X., & Xu, J. (2022). Geopolitical risk and dynamic connectedness between commodity markets. Energy Economics, 110, 106028. https://doi.org/10.1016/j.eneco.2022.106028
24. 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
25. Ji, Q., Bouri, E., Lau, C. K. M., & Roubaud, D. (2019). Dynamic connectedness and integration in cryptocurrency markets. International Review of Financial Analysis, 63, 257-272. https://doi.org/10.1016/j.irfa.2018.12.002
26. Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, 3-6. https://doi.org/10.1016/j.econlet.2017.06.023
27. Kristoufek, L. (2015). What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis. PloS One, 10(4), e0123923. https://doi.org/10.1371/journal.pone.0123923
28. Kyriazis, N., & Corbet, S. (2024). Evaluating the dynamic connectedness of financial assets and bank indices during black-swan events: A Quantile-VAR approach. Energy Economics, 131, 107329. https://doi.org/10.1016/j.eneco.2024.107329
29. Li, B., Haneklaus, N., & Rahman, M. M. (2024). Dynamic connectedness and hedging opportunities of the commodity and stock markets in China: Evidence from the TVP-VAR and cDCC-FIAPARCH. Financial Innovation, 10(1), 52. https://doi.org/10.1186/s40854-023-00607-x
30. Li, Z., Mo, B., & Nie, H. (2023). Time and frequency dynamic connectedness between cryptocurrencies and financial assets in China. International Review of Economics & Finance, 86, 46-57. https://doi.org/10.1016/j.iref.2023.01.015
31. Shahzad, S. J. H., Bouri, E., Roubaud, D., & Kristoufek, L. (2020). Safe haven, hedge and diversification for G7 stock markets: Gold versus Bitcoin. Economic Modelling, 87, 212-224. https://doi.org/10.1016/j.econmod.2019.07.023
32. Sharma, I., Bamba, M., Verma, B., & Verma, B. (2024). Dynamic connectedness and investment strategies between commodities and ESG stocks: Evidence from India. Australasian Accounting, Business and Finance Journal, 18(3). https://doi.org/10.14453/aabfj.v18i3.05
33. Sutbayeva, R., Abdeshov, D., Shodyrayeva, S., Maukenova, A., Bekteshi, X., & Doğan, M. (2024). The nexus between ICT, trade openness, urbanization, natural resources, foreign direct investment and economic growth. International Journal of Sustainable Development & Planning, 19(2), 723-730. https://doi.org/10.18280/ijsdp.190229
34. Umar, Z., Gubareva, M., & Teplova, T. (2021). The impact of Covid-19 on commodity markets volatility: Analyzing time-frequency relations between commodity prices and coronavirus panic levels. Resources Policy, 73, 102164. https://doi.org/10.1016/j.resourpol.2021.102164
35. Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82. https://doi.org/10.1016/j.econlet.2016.09.019
36. Wątorek, M., Drożdż, S., Kwapień, J., Minati, L., Oświęcimka, P., & Stanuszek, M. (2021). Multiscale characteristics of the emerging global cryptocurrency market. Physics Reports, 901, 1-82. https://doi.org/10.1016/j.physrep.2020.10.005
37. Yadav, M. P., Al-Qudah, A. A., Sandhu, K., & Gupta, N. (2025). Resolving an enigma of FinTech, digital assets and electronic commerce: Insight to time-varying dynamic connectedness. FIIB Business Review, 23197145241300899. https://doi.org/10.1177/23197145241300899
38. Yousaf, I., & Yarovaya, L. (2022). Static and dynamic connectedness between NFTs, Defi and other assets: Portfolio implication. Global Finance Journal, 53, 100719. https://doi.org/10.1016/j.gfj.2022.100719