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

Year 2024, , 575 - 595, 25.12.2024
https://doi.org/10.37093/ijsi.1510272

Abstract

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.

References

  • 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
  • Al-Jaroodi, J., & Mohamed, N. (2019). Blockchain in industries: A Survey. IEEE Access, 7, 36500–36515. https://doi.org/10.1109/ACCESS.2019.2903554
  • 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
  • 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
  • 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
  • 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
  • Bitcoin Geçmiş Verileri. (2024, 06 13). Investing-TR. https://tr.investing.com/crypto/bitcoin/historical-data
  • 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.
  • 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
  • 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
  • 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
  • Di Pierro, M. (2017). What Is the blockchain? Computing in Science & Engineering, 19(5), 92–95. https://doi.org/10.1109/MCSE.2017.3421554
  • 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
  • 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
  • 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
  • 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
  • Granger, C. W. J., & Newbold, P. (1977). Forecasting economic time series. Academic Press.
  • 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
  • Karame, G., & Capkun, S. (2018). Blockchain security and privacy. IEEE Security & Privacy, 16(4), 11–12. https://doi.org/10.1109/MSP.2018.3111241
  • 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
  • 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
  • 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
  • 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
  • Mazur, M., & Polyzos, E. (2024, April 29). Spot Bitcoin ETF. SSRN. http://dx.doi.org/10.2139/ssrn.4810965
  • 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
  • Page, E. S. (1954). Continuous inspection scheme. Biometrika, 41(1-2), 100-115. https://doi.org/10.1093/biomet/41.1-2.100
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Whalen, R. C. (2008). The subprime crisis—cause, effect and consequences. Journal of Affordable Housing & Community Development Law, 17(3), 219–235.
  • 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
  • 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

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

Year 2024, , 575 - 595, 25.12.2024
https://doi.org/10.37093/ijsi.1510272

Abstract

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.

References

  • 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
  • Al-Jaroodi, J., & Mohamed, N. (2019). Blockchain in industries: A Survey. IEEE Access, 7, 36500–36515. https://doi.org/10.1109/ACCESS.2019.2903554
  • 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
  • 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
  • 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
  • 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
  • Bitcoin Geçmiş Verileri. (2024, 06 13). Investing-TR. https://tr.investing.com/crypto/bitcoin/historical-data
  • 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.
  • 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
  • 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
  • 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
  • Di Pierro, M. (2017). What Is the blockchain? Computing in Science & Engineering, 19(5), 92–95. https://doi.org/10.1109/MCSE.2017.3421554
  • 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
  • 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
  • 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
  • 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
  • Granger, C. W. J., & Newbold, P. (1977). Forecasting economic time series. Academic Press.
  • 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
  • Karame, G., & Capkun, S. (2018). Blockchain security and privacy. IEEE Security & Privacy, 16(4), 11–12. https://doi.org/10.1109/MSP.2018.3111241
  • 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
  • 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
  • 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
  • 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
  • Mazur, M., & Polyzos, E. (2024, April 29). Spot Bitcoin ETF. SSRN. http://dx.doi.org/10.2139/ssrn.4810965
  • 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
  • Page, E. S. (1954). Continuous inspection scheme. Biometrika, 41(1-2), 100-115. https://doi.org/10.1093/biomet/41.1-2.100
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Whalen, R. C. (2008). The subprime crisis—cause, effect and consequences. Journal of Affordable Housing & Community Development Law, 17(3), 219–235.
  • 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
  • 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
There are 39 citations in total.

Details

Primary Language Turkish
Subjects Finance, Financial Forecast and Modelling, Financial Markets and Institutions
Journal Section Articles
Authors

Ozan Kaymak 0000-0001-5492-2877

Early Pub Date December 25, 2024
Publication Date December 25, 2024
Submission Date July 4, 2024
Acceptance Date October 14, 2024
Published in Issue Year 2024

Cite

APA Kaymak, O. (2024). Forbes Tarafından Seçilen Blockchain Borsa Yatırım Fonları (BYF) İle Bitcoin ve Ethereum Getirilerinin Vektör Otoregresyon Analizi İle İncelenmesi. International Journal of Social Inquiry, 17(3), 575-595. https://doi.org/10.37093/ijsi.1510272

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