Dijital Varlıkların Geleneksel Finansal Araçlarla İlişkisi: TVP-VAR Yaklaşımı ile Araştırılması

Year 2025, Volume: 28 Issue: 2, 380 - 401, 30.11.2025

Aslan Aydoğdu

Abstract

Blockchain piyasalarındaki yüksek volatilite, yatırımcıların ve piyasa katılımcılarının NFT’ler, DeFi token’ları ve kripto paralar aracılığıyla çeşitlendirme fırsatlarına odaklanmasına neden olmuştur. Bu çalışma da NFT’ler, DeFi varlıkları ve kripto paraların geleneksel finansal varlıklarla olan ilişkilerini incelemektir. Araştırma, 02.02.2018-06.01.2025 tarihleri arasında elde edilen veri setiyle, Zamanla Değişen Parametreli Vektör Otoregresif (TVP-VAR) yaklaşımı ve çeşitli portföy stratejileri kullanılarak gerçekleştirilmiştir. Bulgular, sürdürülebilir yatırım stratejileri geliştirmek için bilgiler sunmaktadır. Bu bağlamda, NFT’ler ve DeFi varlıklarının geleneksel finansal varlık sınıflarından ve Bitcoin’den hâlâ bağımsız hareket ettiği ortaya koyulmuştur. Buna ek olarak, bu dijital varlıkların özellikle COVID-19 ve 2021 kripto balonu dönemlerinde diğer varlıklarla daha yüksek dinamik ilişkilere sahip olduğu tespit edilmiştir. Portföy analizi, NFT’lerin ve DeFi varlıklarının altın, petrol ve pay senetlerinden oluşan portföylerde çeşitlendirme avantajları sağlayabileceğini göstermiştir. Çalışmanın sonuçları, yatırımcılara ve politika yapıcılara sürdürülebilir portföy stratejileri oluşturma ve risk yönetimini iyileştirme konusunda öneriler sunmaktadır. Özellikle minimum varyans ve minimum bağlantılılık yaklaşımlarının, portföy riskini optimize etmek için önemli araçlar olduğu vurgulanmıştır. Bu analiz, dijital ve geleneksel finansal varlıklar arasındaki ilişkilere dair literatüre katkı sağlamaktadır. Ayrıca, tüm piyasalar için zamanla değişen aktarım-algılama modelleri göz önüne alındığında, yatırımcılar ve politika yapıcıların portföy edinme ve düzenleme kararlarını iyileştirmek için yayılma analizinden yararlanmaları önerilmektedir.

Keywords

NFT’ler , DeFi varlıklar , Portföy Çeşitlendirme , TVP-VAR yaklaşımı , Volatilite yayılımı , Kripto Para

The Relationship of Digital Assets with Traditional Financial Instruments: Investigation with TVP-VAR Approach

Abstract

The high volatility in blockchain markets has led investors and market participants to focus on diversification opportunities through NFTs, DeFi tokens, and cryptocurrencies. Therefore, this study aims to examine the relationship between NFTs, DeFi assets, cryptocurrencies, and traditional financial assets. The research uses a time-varying parameter vector autoregressive (TVP-VAR) approach and various portfolio strategies with the data set obtained between 02.02.2018 and 06.01.2025. The findings provide valuable insights for developing sustainable investment strategies. In this context, it is revealed that NFTs and DeFi assets still act independently from traditional financial asset classes and Bitcoin. In addition, these digital assets were found to have higher dynamic relationships with other assets, especially during the COVID-19 and 2021 crypto bubble periods. Portfolio analysis showed that NFTs and DeFi assets can provide diversification benefits in portfolios of gold, oil, and equities. The study results provide recommendations to investors and policymakers on building sustainable portfolio strategies and improving risk management. In particular, minimum variance and connectedness approaches are essential for optimising portfolio risk. This analysis contributes to the literature on the relationship between digital and traditional financial assets. Moreover, given the time-varying transmission-perception patterns for all markets, investors and policymakers should utilise spillover analysis to improve portfolio acquisition and regulation decisions.

Keywords

NFTs , DeFi assets , Portfolio diversification , TVP-VAR approach , Volatility spillover , Cryptocurrency

References

• Aharon, D. Y., & Demir, E. (2021). NFTs and asset class spillovers: Lessons from the period around the COVID-19 pandemic. Finance Research Letters, 47, 102515. https://doi.org/10.1016/j.frl.2021.102515
• 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.
• Angerer, M., Hoffmann, C. H., Neitzert, F., & Kraus, S. (2021). Objective and subjective risks of investing into cryptocurrencies. Finance Research Letters, 40, 101737. https://doi.org/10.1016/j.frl.2020.101737
• 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
• Arouri, M. E. H., Lahiani, A., & Nguyen, D. K. (2015). World gold prices and stock returns in China: Insights for hedging and diversification strategies. Economic Modelling, 44, 273–282.
• 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
• Broadstock, D. C., Chatziantoniou, J., & Gabauer, D. (2022). Minimum connectedness portfolios and the market for green bonds: Advocating socially responsible investment (SRI) activity. In Applications in Energy Finance (pp. 217–253). Palgrave Macmillan. https://doi.org/10.1007/978-3-030-92957-2_9
• Caldarelli, G., & Ellul, J. (2021). The Blockchain Oracle problem in decentralized finance – A multivocal approach. Applied Sciences, 11, 7572. https://doi.org/10.3390/app11167572
• Chatziantoniou, I., Gabauer, D., & Marfatia, H. A. (2021). Dynamic connectedness and spillovers across sectors: Evidence from the Indian stock market. Scottish Journal of Political Economy, 69(3), 283–300. https://doi.org/10.1111/sjpe.12291
• Chen, J., Xia, X., Lo, D., Grundy, J., & Yang, X. (2021). Maintenance-related concerns for post-deployed Ethereum smart contract development: Issues, techniques, and future challenges. Empirical Software Engineering, 6.
• Chen, Y., & Bellavitis, C. (2020). Blockchain disruption and decentralized finance: The rise of decentralized business models. Journal of Business Venturing Insights, 13, e00151. https://doi.org/10.1016/j.jbvi.2019.e00151
• Chohan, U. (2021). Decentralized Finance (DeFi): An emergent alternative financial architecture. SSRN. https://doi.org/10.2139/ssrn.3791921
• Christoffersen, P., Errunza, V., Jacobs, K., & Jin, X. (2014). Correlation dynamics and international diversification benefits. International Journal of Forecasting, 30(3), 807–824. https://doi.org/10.1016/j.ijforecast.2014.01.001
• Christoffersen, P., Errunza, V., Jacobs, K., & Jin, X. (2014). Correlation dynamics and international diversification benefits. International Journal of Forecasting, 30(3), 807–824. Coinstats. (2025). Market cap charts. https://coinstats.app/tr/market-cap-charts/ (Accessed January 27, 2025).
• Corbet, S., Goodell, J., Gunay, S., & Kaskaloglu, K. (2021). Are DeFi tokens a separate asset class from conventional cryptocurrencies? SSRN. https://ssrn.com/abstract=3810599
• 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.
• Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28–34. https://doi.org/10.1016/j.econlet.2018.01.004
• Dahir, A. M., Mahat, F., Amin Noordin, B. A., & Hisyam Ab Razak, N. (2020). Dynamic connectedness between Bitcoin and equity market information across BRICS countries: Evidence from TVP-VAR connectedness approach. International Journal of Managerial Finance, 16(3), 357–371. https://doi.org/10.1108/IJMF-03-2019-0117
• 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
• Dowling, M. (2021a). Fertile land: Pricing non-fungible tokens. Finance Research Letters. https://doi.org/10.1016/j.frl.2021.102096
• Dowling, M. (2021b). Is non-fungible token pricing driven by cryptocurrencies? Finance Research Letters. https://doi.org/10.1016/j.frl.2021.102097
• Dowling, M. (2022). Is non-fungible token pricing driven by cryptocurrencies? Finance Research Letters, 44, 102097. https://doi.org/10.1016/j.frl.2021.102097
• Dubovitskaya, A., Ackerer, D., & Xu, J. (2021). A game-theoretic analysis of cross-ledger swaps with packetized payments. In M. Bernhard et al. (Eds.), Financial Cryptography and Data Security. FC 2021 International Workshops. FC 2021. Lecture Notes in Computer Science (Vol. 12676, pp. 316–336). Springer. https://doi.org/10.1007/978-3-662-63958-0_16
• Durand, D. (1960). Portfolio selection: Efficient diversification of investments. The American Economic Review, 50(1), 234–236. https://www.jstor.org/stable/1813505
• Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813–836. https://doi.org/10.2307/2171846
• Fabozzi, F. J., Gupta, F., & Markowitz, H. M. (2002). The legacy of modern portfolio theory. The Journal of Investing, 11(3), 7–22.
• Gandal, N., Hamrick, J., Moore, T., & Vasek, M. (2021). The rise and fall of cryptocurrency coins and tokens. Decisions in Economics and Finance.
• Gülcan, N., & Küçükçaylı, F. M. The Volatility Spillover in Metaverse Token Market: TVP-VAR Model Application. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 19(3), 906-922. https://doi.org/10.17153/oguiibf.1399452.
• Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity, and serial independence of regression residuals. Economics Letters, 6, 255–259. https://doi.org/10.1016/0165-1765(80)90024-5
• Karim, S., Lucey, B. M., Naeem, M. A., & Uddin, G. S. (2022). Examining the interrelatedness of NFTs, DeFi tokens, and cryptocurrencies. Finance Research Letters, 47, 102696. https://doi.org/10.1016/j.frl.2022.102696
• Katsiampa, P., Corbet, S., & Lucey, B. (2019). Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis. Finance Research Letters, 29, 68–74. https://doi.org/10.1016/j.frl.2019.03.009
• Markowitz, H. (2014). Mean–variance approximations to expected utility. European Journal of Operational Research, 234(2), 346–355. https://doi.org/10.1016/j.ejor.2012.08.023
• Nadini, M., Alessandretti, L., Di Giacinto, F., Martino, M., Aiello, L. M., & Baronchelli, A. (2021). Mapping the NFT revolution: Market trends, trade networks, and visual features. Scientific Reports, 11(1), 20902.
• Papathanasiou, S., Vasiliou, D., Magoutas, A., & Koutsokostas, D. (2022). Do hedge and merger arbitrage funds actually hedge? A time-varying volatility spillover approach. Finance Research Letters, 44, 102088. https://doi.org/10.1016/j.frl.2021.102088
• Polat, O. (2023). Dynamic volatility connectedness among cryptocurrencies: Evidence from time-frequency connectedness networks. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 23(1), 29–50. https://doi.org/10.18037/ausbd.1272534
• Polat, O., Ertuğrul, H. M., Sakarya, B., & Akgül, A. (2024). TVP-VAR based time and frequency domain food & energy commodities connectedness: An analysis for financial/geopolitical turmoil episodes. Applied Energy, 357, 122487. https://doi.org/10.1016/j.apenergy.2023.122487
• Symitsi, E., & Chalvatzis, K. J. (2018). Return, volatility, and shock spillovers of bitcoin with energy and technology companies. Economics Letters, 170, 127–130. https://doi.org/10.1016/j.econlet.2018.06.012
• Umar, Z., & Gubareva, M. (2020). A time-frequency analysis of the impact of the Covid-19-induced panic on the volatility of currency and cryptocurrency markets. Journal of Behavioral and Experimental Finance, 28, 100404. https://doi.org/10.1016/j.jbef.2020.100404
• Umar, Z., Gubareva, M., Teplova, T., & Tran, D. K. (2022b). COVID-19 impact on NFTs and major asset classes interrelations: Insights from the wavelet coherence analysis. Finance Research Letters, 47, 102725. https://doi.org/10.1016/j.frl.2022.102725
• Umar, Z., Gubareva, M., Tran, D. K., & Teplova, T. (2021b). Impact of the COVID-19-induced panic on the environmental, social, and governance leaders equity volatility: A time-frequency analysis. Research in International Business and Finance, 58, 101493. https://doi.org/10.1016/j.ribaf.2021.101493
• Umar, Z., Polat, O., Choi, S. Y., & Teplova, T. (2022a). Dynamic connectedness between non-fungible tokens, decentralized finance, and conventional financial assets in a time-frequency framework. Pacific-Basin Finance Journal, 76, 101876. https://doi.org/10.1016/j.pacfin.2022.101876
• Vardar, G., & Aydogan, B. (2019). Return and volatility spillovers between Bitcoin and other asset classes in Turkey: Evidence from the VAR-BEKK-GARCH approach. EuroMed Journal of Business, 14(3), 12. https://doi.org/10.1108/EMJB-10-2018-0066
• Xu, D., Corbet, S., Lang, C., & Hu, Y. (2024). Understanding dynamic return connectedness and portfolio strategies among international sustainable exchange-traded funds. Economic Modelling, 141, 106864. https://doi.org/10.1016/j.econmod.2024.106864
• Yousaf, I., & Ali, S. (2020). Discovering interlinkages between major cryptocurrencies using high-frequency data: New evidence from the COVID-19 pandemic. Financial Innovation, 6(1), 1–18.
• Yousaf, I., Nekhili, R., & Gubareva, M. (2022). Linkages between DeFi assets and conventional currencies: Evidence from the COVID-19 pandemic. International Review of Financial Analysis, 81, 102082. https://doi.org/10.1016/j.irfa.2022.102082