Vector Autoregression Analysis of Bitcoin and Ethereum Returns With Blockchain Exchange Traded Funds (ETF) Selected by Forbes

Öz

After Bitcoin emerged in 2008, cryptocurrencies quickly became critical financial assets. Cryptocurrencies operate with blockchain technology, a decentralized structure based on the principle of consensus that allows direct transactions between units and all units to access the ledger. This study aims to analyze the relationships between the weekly returns of the best exchange-traded funds specializing in capital assets investments of companies operating in the blockchain industry, which Forbes selected in April 2024, and Bitcoin and Ethereum weekly returns in the period between October 2021 and June 2024, using the Vector Autoregression (VAR) method. The effect levels of the series against each other were examined by performing variance decomposition and impulse-response tests. We also investigated the causality relationships between the series using the Granger Causality Test. As a result of the study, it has been determined that the weekly returns of the First Trust SkyBridge Crypto Industry and Digital Economy ETF (CRPT) have a one-way Granger causality relationship with the weekly returns of Bitcoin and Ethereum at a 5% significance level. In addition, CRPT has a bidirectional Granger causality relationship with only Bitcoin weekly returns at a 10% significance level.

Anahtar Kelimeler

• Blockchain Exchange Traded Funds
• Bitcoin
• Ethereum
• VAR Analysis

Forbes Tarafından Seçilen Blockchain Borsa Yatırım Fonları (BYF) İle Bitcoin ve Ethereum Getirilerinin Vektör Otoregresyon Analizi İle İncelenmesi

Öz

2008 yılında Bitcoin’in ortaya çıkmasından sonra kripto paralar kısa zamanda önemli bir varlık sınıfı haline gelmiştir. Kripto paralar; uzlaşma prensibine dayalı, birimler arası doğrudan işlem yapma imkânı sunan, işlemlere ait kayıtlara tüm birimlerin erişebildiği, merkeziyetsiz bir yapı olan blockchain teknolojisi ile işletilirler. Bu çalışmanın amacı, Forbes tarafından 2024 yılı için, blockchain endüstrisinde faaliyet gösteren firmalara ait sermaye varlıkları yatırımlarında uzmanlaşan en iyi borsa yatırım fonlarının 2021 Ekim ile 2024 Haziran dönemindeki haftalık getirileri ile aynı dönemdeki Bitcoin ve Ethereum haftalık getirilerinin zaman serileri Vektör Oto Regresyon Analizi ile incelenmesidir. Çalışmada Varyans Ayrıştırması ve Etki-Tepki Testleri yapılarak serilerin birbirlerine karşı etki düzeyleri incelenmiştir. Ayrıca seriler arasındaki nedensellik ilişkileri Granger Nedensellik Testi yöntemiyle araştırılmıştır. Çalışmanın sonucunda; seçili blockchain yatırım fonlarından First Trust SkyBridge Crypto Industry and Digital Economy (CRPT) haftalık getirilerinin, Bitcoin ve Ethereum haftalık getirileri ile %5 anlamlılık seviyesinde tek yönlü, sadece Bitcoin haftalık getirileri ile %10 anlamlılık düzeyinde çift yönlü Granger Nedensellik ilişkisine sahip olduğu belirlenmiştir.

Anahtar Kelimeler

• Blockchain Borsa Yatırım Fonları
• Bitcoin
• Ethereum
• VAR Analizi

Kaynakça

1. Adams, M. (2024, 05 06). 7 Best Blockchain ETFs of June 2024. 05 Ekim 2024 tarihinde erişim adresi https://www.forbes.com/advisor/investing/cryptocurrency/best-blockchain-etfs
2. Al-Jaroodi, J., & Mohamed, N. (2019). Blockchain in industries: A Survey. IEEE Access, 7, 36500–36515. https://doi.org/10.1109/ACCESS.2019.2903554
3. Andrianto, Y., & Diputra, Y. (2017). The Effect of Cryptocurrency on Investment Portfolio Effectiveness. Journal of Finance and Accounting, 5(6), 229-238. https://doi.org/10.11648/j.jfa.20170506.14
4. Autore, D. M., Clarke, N., & Jiang, D. (2021). Blockchain speculation or value creation? Evidence from corporate investments. Financial Management, 50(3), 727–746. https://doi.org/10.1111/fima.12336
5. Barnard, G. A. (1959). Control Charts and Stochastic Processes. Journal of the Royal Statistical Society, 21(2), 239-271. https://doi.org/10.1111/j.2517-6161.1959.tb00336
6. Bianchi, D., & Babiak, M. (2022). On the performance of cryptocurrency funds. Journal of Banking & Finance, 138, 106467. https://doi.org/10.1016/j.jbankfin.2022.106467
7. Bitcoin Geçmiş Verileri. (2024, 06 13). Investing-TR. https://tr.investing.com/crypto/bitcoin/historical-data
8. Blau, B. M., & Whitby, R. J. (2019). The Introduction of Bitcoin futures: An examination of volatility and potential spillover effects. Economics Bulletin, 39(2), 1030-1038.

9. Braun, P. A., & Mittnik, S. (1993). Misspecifications in vector autoregressions and their effects on impulse responses and variance decompositions. Journal of Econometrics, 59(3), 319–341. https://doi.org/10.1016/0304-4076(93)90029-5
10. Brini, A., & Lenz, J. (2022, July 08). Bitcoin ETFs: Measuring the performance of this new market niche. SSRN. http://dx.doi.org/10.2139/ssrn.4157711
11. Davidson, P. (2008). Is the current financial distress caused by the subprime mortgage crisis a Minsky moment? Or is it the result of attempting to securitize illiquid noncommercial mortgage loans? Journal of Post Keynesian Economics, 30(4), 669–676. https://doi.org/10.2753/PKE0160-3477300409
12. Di Pierro, M. (2017). What Is the blockchain? Computing in Science & Engineering, 19(5), 92–95. https://doi.org/10.1109/MCSE.2017.3421554
13. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427–431. https://doi.org/10.1080/01621459.1979.10482531
14. Ding, M., Chen, Y., & Bressler, S. L. (2006). Granger causality: Basic theory and application to neuroscience. In B. Schelter, M. Winterhalder & J. Timmer (Eds.), Handbook of Time Series Analysis: Recent Theoretical Developments and Applications (pp. 437–460). John Wiley & Sons, Ltd. https://doi.org/10.1002/9783527609970.ch17
15. Gartenberg, C., & Pierce, L. (2015). Subprime governance: Agency Costs in vertically integrated banks and the 2008 mortgage crisis. Strategic Management Journal, 38(2), 300-321. https://doi.org/10.1002/smj.2481
16. Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438. https://doi.org/10.2307/1912791
17. Granger, C. W. J., & Newbold, P. (1977). Forecasting economic time series. Academic Press.
18. Inoue, A., & Kilian, L. (2013). Inference on impulse response functions in structural VAR models. Journal of Econometrics, 177(1), 1–13. https://doi.org/10.1016/j.jeconom.2013.02.009
19. Karame, G., & Capkun, S. (2018). Blockchain security and privacy. IEEE Security & Privacy, 16(4), 11–12. https://doi.org/10.1109/MSP.2018.3111241
20. 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
21. Kristjanpoller, W., Bouri, E., & Takaishi, T. (2020). Cryptocurrencies and equity funds: Evidence from an asymmetric multifractal analysis. Physica A: Statistical Mechanics and its Applications, 545, 123711. https://doi.org/10.1016/j.physa.2019.123711
22. Kristjanpoller, W., Nekhili, R., & Bouri, E. (2024). Blockchain ETFs and the cryptocurrency and Nasdaq markets: Multifractal and asymmetric cross-correlations. Physica A: Statistical Mechanics and Its Applications, 637, 129589. https://doi.org/10.1016/j.physa.2024.129589
23. Malik, N., Wei, Y. “Max,” Appel, G., & Luo, L. (2023). Blockchain technology for creative industries: Current state and research opportunities. International Journal of Research in Marketing, 40(1), 38–48. https://doi.org/10.1016/j.ijresmar.2022.07.004
24. Mazur, M., & Polyzos, E. (2024, April 29). Spot Bitcoin ETF. SSRN. http://dx.doi.org/10.2139/ssrn.4810965
25. Nofer, M., Gomber, P., Hinz, O., & Schiereck, D. (2017). Blockchain. Business & Information Systems Engineering, 59(3), 183–187. https://doi.org/10.1007/s12599-017-0467-3
26. Page, E. S. (1954). Continuous inspection scheme. Biometrika, 41(1-2), 100-115. https://doi.org/10.1093/biomet/41.1-2.100
27. Patrickson, B. (2021). What do blockchain technologies imply for digital creative industries? Creativity and Innovation Management, 30(3), 585–595. https://doi.org/10.1111/caim.12456
28. Pavlova, I. (2021). Blockchain ETFs: Dynamic correlations and hedging capabilities. Managerial Finance, 47(5), 687–702. https://doi.org/10.1108/MF-11-2019-0565
29. 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
30. 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
31. Pichl, L., & Kaizoji, T. (2017). Volatility analysis of bitcoin price time series. Quantative Finance and Economics, 1(4), 474-485. https://doi.org/10.3934/QFE.2017.4.474
32. Sharma, S., Tiwari, A. K., & Nasreen, S. (2022). Are FinTech, Robotics, and Blockchain index funds providing diversification opportunities with emerging markets?Lessons from pre and postoutbreak of COVID-19. Electronic Commerce Research, 24(1), 341–370. https://doi.org/10.1007/s10660-022-09611-2
33. Sunyaev, A., Kannengießer, N., Beck, R., Treiblmaier, H., Lacity, M., Kranz, J., Fridgen, G., Spankowski, U., & Luckow, A. (2021). Token economy. Business & Information Systems Engineering, 63, 457–478. https://doi.org/10.1007/s12599-021-00684-1
34. Velazquez, M., Gormus, A., & Vafai, N. (2023). The dynamic dependency between a cryptocurrency ETF and ETFs representing conventional asset classes. Journal of Risk and Financial Management, 16(9), 412-421. https://doi.org/10.3390/jrfm16090412
35. Wang, X., & Hui, X. (2024). Price-volume relationship in Bitcoin futures ETF market: An information perspective. Discrete Dynamics in Nature and Society, 2024(1), 8066742. https://doi.org/10.1155/2024/8066742
36. Wang, J., Ma, F., Bouri, E., & Guo, Y. (2023). Which factors drive Bitcoin volatility: Macroeconomic, technical, or both? Journal of Forecasting, 42(4), 970–988. https://doi.org/10.1002/for.2930
37. Whalen, R. C. (2008). The subprime crisis—cause, effect and consequences. Journal of Affordable Housing & Community Development Law, 17(3), 219–235.
38. Yadav, S. P., Agrawal, K. K., Bhati, B. S., Al-Turjman, F., & Mostarda, L. (2020). Blockchain-based cryptocurrency regulation: An overview. Computational Economics, 59, 1659–1675. https://doi.org/10.1007/s10614-020-10050-0
39. Zhang, R., Xue, R., & Liu, L. (2019). Security and privacy on blockchain. ACM Computing Surveys, 52(3), 1-34. https://doi.org/10.1145/3316481