Research Article
BibTex RIS Cite

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

Year 2025, Volume: 7 Issue: 3, 160 - 171, 30.09.2025

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.

References

  • 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
  • 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
  • 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
  • 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
  • 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
  • Baur, D. G., & Hoang, L. T. (2021). The importance of spillovers. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3973795
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Dataset available website: https://tr.investing.com/
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82. https://doi.org/10.1016/j.econlet.2016.09.019
  • 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
  • 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
  • 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

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

Year 2025, Volume: 7 Issue: 3, 160 - 171, 30.09.2025

Abstract

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.

References

  • 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
  • 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
  • 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
  • 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
  • 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
  • Baur, D. G., & Hoang, L. T. (2021). The importance of spillovers. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3973795
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Dataset available website: https://tr.investing.com/
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82. https://doi.org/10.1016/j.econlet.2016.09.019
  • 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
  • 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
  • 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
There are 38 citations in total.

Details

Primary Language English
Subjects Finance
Journal Section Research Articles
Authors

Burhan Erdoğan 0000-0002-6171-0554

Publication Date September 30, 2025
Submission Date September 12, 2025
Acceptance Date September 29, 2025
Published in Issue Year 2025 Volume: 7 Issue: 3

Cite

APA Erdoğan, B. (2025). Interconnectedness and Risk Structure Among Digital Assets: Empirical Findings Based on the Generalized R² Approach (2020–2025). International Journal of Business and Economic Studies, 7(3), 160-171. https://doi.org/10.54821/uiecd.1783148


28007

BES JOURNAL-International Journal of Business and Economic Studies is licensed with Creavtive Commons (CC) Attribution 4.0 International Licence (CC BY 4.0).