Bitcoin's Effect on Selected Crypto Assets: Quantitative Evidence

Yıl 2024, Cilt: 8 Sayı: 3, 735 - 750, 30.09.2024

Ersin Sünbül

https://doi.org/10.29023/alanyaakademik.1422810

Öz

Çalışmanın temel amacı, Bitcoin'in belirli kripto varlıklar üzerindeki etkilerini sistemli bir biçimde araştırmaktır. Bu bağlamda, finansal piyasalarda değeri en yüksek olan Ethereum, Tether, USD Coin, BNB, XRP ve Cardano gibi yedi kripto varlığın 2020W01 ile 2022W33 arasındaki 135 gözlemleri çalışma örneklemine dahil edilmiştir. Bu kripto varlıklar, Bitcoin ile birlikte toplam kripto varlık portföyünün neredeyse %95'ini oluşturmaktadır. Bu durum, çalışmanın geniş bir evreni temsil etmesinden kaynaklanan önemini artırmakta ve akademik literatüre katkı sağlamaktadır. Bitcoin'in seçilen kripto varlıklar üzerindeki etkilerini incelemek için, Çok Değişkenli Dinamik VAR Modeli kullanılmıştır. Model, değişkenler arasında nedensellik olduğunu kabul eder. Bu varsayımı sağlamayan değişkenler çalışma örneğinden çıkarılmıştır. Çalışmanın sonuçları, Bitcoin'in tüm kripto para birimleri üzerinde anlamlı ve tek yönlü etkiler gösterdiğini ortaya koymaktadır. Ancak, diğer kripto varlıkların Bitcoin'e etkisi, göz ardı edilebilecek kadar küçük olmuştur. Bu bağlamda, iki yönlü değerlendirmelerden elde edilen yatırım kararlarının riskli ve yanıltıcı olabileceği vurgulanmaktadır. Kripto varlıklara yönelik yatırım risklerini daha kabuledilebilir boyutlara indirebilmek maksadıyla, kapsamlı analiz yöntemlerinin kullanılması önerilmektedir.

Anahtar Kelimeler

Bitcoin, Kripto Varlıklar, Nedensellik, Çok Değişkenli Dinamik VAR Modeli, Etki Tepkisi ve Varyans Ayrıştırma Fonksiyonları, Korelasyon ve Kovaryans Matris

Etik Beyan

Çalışmada etik kurul onayına ihtiyaç duyulan herhangi bir veri ve çalışma olmadığını taahhüt ederim. Dolayısıyla etik kurul onayı alınmamıştır.

Destekleyen Kurum

yok

Teşekkür

Editör ve hakemlere destek ve emekleri için teşekürler

Bitcoin's Effect on Selected Crypto Assets: Quantitative Evidence

Öz

The main objective of the study is to systematically investigate the effects of Bitcoin on specific crypto assets. In this context, a sample of 135 observations between 2020W01 and 2022W33 for seven crypto assets with the highest value in the financial markets, including Ethereum, Tether, USD Coin, BNB, XRP, and Cardano, has been included in the study sample. These crypto assets, along with Bitcoin, constitute almost 95% of the total crypto asset portfolio. This situation increases the importance of representing a broad universe in the study and contributes to the academic literature. To examine the effects of Bitcoin on the selected crypto assets, a Multivariate Dynamic VAR Model has been used. The model assumes causality among variables. Variables that do not meet this assumption have been excluded from the study sample. The results of the study demonstrate that Bitcoin exhibits significant and one-way effects on all cryptocurrencies. However, the impact of other crypto assets on Bitcoin has been negligible. In this context, it is emphasized that investment decisions obtained from two-way evaluations may be risky and misleading. In order to reduce investment risks for crypto assets to more acceptable levels, the use of comprehensive analysis methods is recommended.

Anahtar Kelimeler

Bitcoin, Crypto-assets, Causality, Multivariate Dynamic VAR Model, Impulse-Response and Variance Decomposition Functions, Correlation and Covariance Matrix

Kaynakça

• Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. https://doi.org/10.1109/TAC.1974.1100705
• Akçalı, Y. B., & Şişmanoğlu, E. (2019). Kripto para birimleri arasındaki ilişkinin Toda–Yamamoto nedensellik testi ile analizi. EKEV Akademi Dergisi, 23(78), 99-122.
• Algan, N., İşcan, E., & Oktay, D. S. (2020). Economics of blockchain and impacts on economy: Rise of the crypto economy. In S. Evci & A. Sharma (Eds.), Studies at the crossroads of management & economics (pp. 177-186). IJOPEC Publication Limited.
• Antonopoulos, A. M. (2014). Mastering Bitcoin: Unlocking digital cryptocurrencies. O'Reilly Media.
• Antonopoulos, A. M., & Wood, G. (2018). Mastering Ethereum: Building smart contracts and dApps. O'Reilly Media.
• Binance Research. (n.d.). Binance Research. Retrieved from https://research.binance.com/en
• Binance. (n.d.). Binance. Retrieved from https://www.binance.com/
• Bouoiyour, J., & Selmi, R. (2015). What does Bitcoin look like? MPRA Paper No. 58091. University Library of Munich, Germany.
• Bourgehelle, D., Jawadi, F., & Rozin, P. (2022). Do collective emotions drive Bitcoin volatility? A triple regime-switching vector approach. Journal of Economic Behavior & Organization, 196, 294-306. https://doi.org/10.1016/j.jebo.2022.01.012
• Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213-238. https://doi.org/10.1257/jep.29.2.213
• Breusch, T. S. (1978). Testing for autocorrelation in dynamic linear models. Australian Economic Papers, 17, 334-355.
• Brown, R. L., Durbin, J., & Evans, J. (1975). Techniques for testing the constancy of regression relationship over time. Journal of the Royal Statistical Society, Series B (Methodological), 37(2), 149-163. https://doi.org/10.1111/j.2517-6161.1975.tb01440.x
• Buterin, V. (2013). Ethereum white paper. Retrieved from https://ethereum.org/en/whitepaper/
• Cakracioğlu, A. (2016). Kripto-para Bitcoin. Sermaye Piyasası Kurulu Araştırma Raporu.
• Cardano Foundation. (n.d.). Cardano. Retrieved from https://cardano.org/
• Cardano Foundation. (n.d.). Documentation. Retrieved from https://docs.cardano.org/
• Cardano Foundation. (n.d.). Forum. Retrieved from https://forum.cardano.org/
• Centre Consortium. (n.d.). USD Coin. Retrieved from https://www.centre.io/usdc
• Charles, A., & Darné, O. (2018). Volatility estimation for Bitcoin: Replication and robustness. International Economics, 154, 98-111. https://doi.org/10.1016/j.inteco.2018.06.004
• Ciaian, P., Rajcaniova, M., & Kancs, D. (2016a). The economics of Bitcoin price formation. Computational Economics, 48(1), 1-23. https://doi.org/10.1007/s10614-016-9575-7
• Ciaian, P., Rajcaniova, M., & Kancs, D. (2016b). The digital agenda of virtual currencies: Can Bitcoin become a global currency? Information Systems and e-Business Management, 14(4), 883-919. https://doi.org/10.1007/s10257-015-0291-7
• Ciaian, P., Rajcaniova, M., & Kancs, D. (2018). Virtual relationships: Short- and long-run evidence from Bitcoin and altcoin markets. Journal of International Financial Markets, Institutions & Money, 52, 173-195. https://doi.org/10.1016/j.intfin.2017.11.001
• Conti, M., Kumar, E. S., & Lal, C. (2017). A survey on security and privacy issues of Bitcoin. Retrieved from https://arxiv.org/pdf/1706.00916.pdf
• Dastgir, S., Demir, E., Downing, G., Gözgör, G., & Lau, C. K. M. (2019). The causal relationship between Bitcoin attention and Bitcoin returns: Evidence from the copula-based Granger causality test. Finance Research Letters, 28, 160-164. https://doi.org/10.1016/j.frl.2018.10.002
• Data Set. (2023). Retrieved August 22, 2023, from https://tr.investing.com/crypto/currencies
• Dilek, S. (2018). Blockchain teknolojisi ve Bitcoin. SETA Vakfı. Retrieved from https://setav.org/assets/uploads/2018/02/231.-Bitcoin.pdf
• Dirican, C., & Canoz, I. (2017). The cointegration relationship between Bitcoin prices and major world stock indices: An analysis with ARDL model approach. Journal of Economics, Finance and Accounting, 4(4), 377-392.
• Dönmez, C. C., Şen, D., & Hazır, U. (2021). Kriptopara dinamikleri: Bitcoin Cash, Ethereum, Litecoin ve Ripple. International Journal of Advances in Engineering and Pure Sciences, 33(4), 636-650.
• Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar—a GARCH volatility analysis. Finance Research Letters, 16, 85-92. https://doi.org/10.1016/j.frl.2015.10.008
• Emir, S. (2023). Kripto paralarda Bitcoin egemenliği: Bitcoin’in altcoinler üzerindeki etkisinin değerlendirilmesi. In H. Özyürek & Z. Baysal (Eds.), Blok zincir ve kripto varlıklar (pp. 155-172). Nobel Yayınları.
• Gandal, N., & Halaburda, H. (2016). Can we predict the winner in a market with network effects? Competition in the cryptocurrency market. Games, 7(16). https://doi.org/10.3390/g7030016
• Granger, C. W. J. (1980). Testing for causality. Journal of Economic Dynamics and Control, 2, 329-352. https://doi.org/10.1016/0165-1889(80)90069-X
• Granger, C. W. J., & Newbold, P. (1986). Forecasting economic time series (2nd ed.). Academic Press.
• Hacıoğlu, U. (2019). Blockchain economics and financial market innovation. Springer Nature Switzerland. https://doi.org/10.1007/978-3-030-25275-5
• Hannan, E. J., & Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society, Series B (Methodological), 41, 190-195. https://doi.org/10.1111/j.2517-6161.1979.tb01036.x
• Jarque, C. M., & Bera, A. K. (1987). A test for normality of observations and regression residuals. International Statistical Review, 55, 163-172. https://doi.org/10.2307/1403192
• Karaagaç, G. A., & Altınırmak, S. (2018). En yüksek piyasa değerine sahip on kripto paranın birbirleriyle etkileşimi. Muhasebe ve Finansman Dergisi, 79, 123-138.
• Konuskan, A., Teker, T., Ömürbek, V., & Bekçi, İ. (2019). Kripto paraların fiyatları arasındaki ilişkinin tespitine yönelik bir araştırma. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 24(2), 311-318.
• Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Retrieved from https://bitcoin.org/bitcoin.pdf
• Omri, I. (2023). Directional predictability and volatility spillover effect from stock market indexes to Bitcoin: Evidence from developed and emerging markets. Journal of Risk Finance, 24(2), 226-243. https://doi.org/10.1108/JRF-06-2022-0130
• Özaydın, O. (2021). Bitcoin's lagged effect on altcoins: A short-term research. PressAcademia Procedia, 14, 10-13. https://doi.org/10.17261/Pressacademia.2021.1477
• 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
• Ploberger, W., & Krämer, W. (1992). The CUSUM test with OLS residuals. Econometrica, 60(2), 271-285. https://doi.org/10.2307/2951670
• Polat, M., & Gemici, E. (2018). Bitcoin ve altcoinler arasındaki ilişki. In 22. Finans Sempozyumu Kitabı (pp. 83-90).
• Sak, N. (2021). Kripto paralar arasındaki ilişkinin incelenmesi: Hatemi-J asimetrik nedensellik analizi. Vizyoner Dergisi, 11(29), 149-175.
• Salihoğlu, E., & Han, A. (2019). Bitcoin ve seçilmiş kripto para birimlerinin fiyatları arasındaki ilişki üzerine bir inceleme. In 4. Uluslararası Sosyoloji ve Ekonomi Kongresi (pp. 616-622).
• Sathyanarayana, S., & Gargesa, S. (2019). Modeling cryptocurrency (Bitcoin) using vector autoregressive (VAR) model. SDMIMD Journal of Management, 10(2), 1-12. https://doi.org/10.18311/sdmimd/2019/23181
• Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464. https://doi.org/10.1214/aos/1176344136
• Sunbul, E. (2022). VAR analysis on the relationship between consumer price index, real interest and exchange rate: The case of Turkey. International Journal of Empirical Economics, 1(2), 1-24. https://doi.org/10.1142/S2810943022500081
• Sunbul, E. (2023a). Kripto varlıklar arasındaki ilişkinin ampirik yöntemlerle araştırılması. In H. Özyürek & Z. Baysal (Eds.), Blok zincir ve kripto varlıklar (pp. 121-140). Nobel Yayınları.
• Sunbul, E. (2023b). Linear and nonlinear relationship between real exchange rate, real interest rate, and consumer price index: An empirical application for countries with different levels of development. Scientific Annals of Economics and Business, 70(1), 57-70. https://doi.org/10.47743/saeb-2023-0008
• Sunbul, E., & Benli, Y. K. (2021). Cointegration and causality between exchange rate and BIST 100 index: An application with R program. In E. Alsu & M. Karahan (Eds.), Muhasebe ve Finans Üzerine Güncel Araştırmalar (pp. 127-156). İKSAD Pub: Ankara. ISBN: 978-625-8061-04-8
• Tether Limited. (2021). Tether white paper. Retrieved from https://tether.to/wp-content/uploads/2021/01/TetherWhitepaper.pdf
• Urom, C., Abid, I., Guesmi, K., & Chevallier, J. (2020). Quantile spillovers and dependence between Bitcoin, equities and strategic commodities. Economic Modelling, 93, 230-258. https://doi.org/10.1016/j.econmod.2020.07.012
• Wei, W. C. (2018). The impact of tether grants on Bitcoin. Economics Letters, 171, 9-22. https://doi.org/10.1016/j.econlet.2018.08.029
• Wright, A., & De Filippi, P. (2015). Decentralized blockchain technology and the rise of lex cryptographia. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2580664