Kripto Para Kripto Para Birimleri Arasındaki Getiri Ve Volatilite Yayılımının İncelenmesi

Year 2021, Volume: 18 Issue: 2, 1 - 16, 30.12.2021

Önder Büberkökü

Abstract

Bu çalışmada Bitcoin (BTC), Binance coin (BNB), Bitcoin cash (BCH), Stellar (XLM) ve Chainlink’ten (LINK) oluşan beş kripto para birimi arasındaki getiri ve volatilite yayılımı günlük veriler kullanılarak incelenmiştir. Analizlerde hem getiri ve hem de volatilite yayılımını dikkate alabilen Cheung ve Ng (1996) testinden yararlanılmıştır. Çalışma bulgular öncelikle ilgili kripto para birimlerinin hem getiri oranları hem de volatilite değerleri arasında eşanlı güçlü bir etkileşimin olduğu sonucuna işaret etmektedir. Bulgular ayrıca Stellar’ın getiri yayılımı açısından kripto para piyasalarında öncü rolü oynadığını, Binance coin’in getiri oranlarının ise Chainlink dışındaki kripto para birimlerinin getiri oranlarındaki değişimlerden tek yönlü olarak etkilendiğini göstermektedir. Volatilite yayılımına ilişkin sonuçlar incelendiğinde ise kripto para piyasalarındaki öncü rolü belirgin bir şekilde Binance coin’in oynadığı görülmektedir. Bulgular ayrıca Bitcoin ile Chainlink ve Bitcoin cash ile Stellar’ı içeren iki varlıklı portföylerin beklenen portföy çeşitlendirme etkisini sunabileceklerini göstermektedir. Son olarak da tüm bu analizler sonucunda Bitcoin’in sahip olduğu piyasa değeri ve işlem hacmi büyüklüğüne rağmen ne getiri ne de volatilite yayılımında öncü bir rolünün bulunmadığı anlaşılmaktadır.

Keywords

Kripto para birimleri, Getiri yayılımı, Volatilite yayılımı

References

• 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.
• Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity, Journal of Econometrics, 31: 307-327.
• Bollerslev, T. (1990). Modeling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Models, Review of Economics and Statistics,72: 498-505.
• Bollerslev, T. & Wooldridge, J.M. (1992). Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models With Time-Varying Covariances,Econometrics Review, 11(2):143-172.
• Bouri,, E., Gabauer, D., Gupta, R. & Tiwari, A.K. (2021). Volatility Connectedness of Major Cryptocurrencies: The Role of Investor Happiness, Journal of Behavioral and Experimental Finance,30: 1-10.
• Cheung, Y.W. & Ng, L.K. (1996). A Causality-in-Variance Test and its Application to Financial Market Prices, Journal of Econometrics, 72 (1-2): 33-48.
• Coinmarketcap (2021). Today's Cryptocurrency Prices by Market Cap, https://coinmarketcap.com/, Erişim Tarihi: 19.05.2021.
• Dickey, D. A. & Fuller, W. A., (1979). Distribution of the Estimators for Autoregressive Time Series with Unit Root, Journal of the American Statistical Association,74: 427–431.
• Fasanya, I.O. ve Oyewole, O. & Odudu, T. (2020). Return And Volatility Spillovers Among Cryptocurrency Portfolios, International of Managerial Finance, 17(2): 327-341.
• Gebka, B. & Serwa, D. (2007). Intra-and Inter-Regional Spillovers between Emerging Capital Markets Around the World, Research in International Business and Finance, 21: 203-221.
• Gemici, E. ve Polat, M. (2021). Causality-in-Mean and Causality-in-Variance among Bitcoin, Litecoin, and Ethereum, Studies in Economics and Finance, Yayımlanma aşamasında.
• Hong,Y. (2001). A Test for Volatility Spillover with Application to Exchange Rates, Journal of Econometrics, 103: 183-224.
• Hu, J. W-S., Chen, M-Y., Fok, R.C.W. & Huang, B-N.( 1997). Causality in Volatility and Volatility Spillover Effects Between U.S., Japan and Four Equity Markets in the South China Financial Markets Growth Triangular, Journal of International Financial Markets, Institutions & Money,7: 351-367.
• Huynh, T.L.D., Nasir, M.A., Vo, X.V. & Nguyen, T.T. (2020). Small Things Matter Most: The Spillover Effects in The Cryptocurrency Market and Gold as a Silver Bullet, North American Journal of Economics and Finance, 54: 1-12.
• Inagaki, K., (2007). Testing for Volatility Spillover between the British Pound and the Euro, Research in International Business and Finance, 21: 161-174.
• Jarque, C.M. & Bera, A. K. (1980). Efficient Tests for Normality, Homoscedasticity and Serial İndependence of Regression Residuals, Economics Letters, 6 (3): 255–259.
• Katsiampa, P.K., Corbet, S. & Lucey, B. (2019). Volatility Spillover Effects in Leading Cryptocurrencies : A BEKK-MGARCH Analysis, Finance Research Letters, 29: 68-74.
• Koutmos, D. (2018). Return and Volatility Spillovers among Cryptocurrencies, Economics Letters, 173: 122-127.
• Kumar, A.S. & Anandarao, S. (2019). Volatility Spillover in Crypto-Currency Markets: Some Evidences From GARCH And Wavelet Analysis, Physica A, 524: 448-458.
• Kyle, A.S. (1985).Continuous Auctions and Inseder Trading, Econometrica, 53,1315-1335.
• Ljung, G.M. & Box, G.E.P. (1978). On a Measure of a Lack of Fit in Time Series Models, Biometrika, 65 (2): 297–303.
• Mensi, W., Al-Yahyaee, K., Al-Jarrah, I.M.W., Vo, X.V. & Kang S.H. (2021). Does Volatility Connectedness Across Major Cryptocurrencies Behave the Same at Different Frequencies? A Portfolio Risk Analysis, International Review of Economics and Finance,76, 96-113.
• Nakajima, T., & Hamori, S., (2012). Causality-in-Mean and Causality-in-Variance Among Electricity Prices, Crude Oil Prices, and Yen-US Dollar Exchange Rates in Japon, Research in International Business and Finance,26: 371-386.
• Omane-Adjepong, M. & Alagidede, I.P. (2019). Multiresolution Analysis and Spillover of Major Cryptocurrency Markets, Research in International Business and Finance, 49: 191-206.
• Papież, M. & Śmiech, S. (2013). Causality-in-Mean and Causality-in-Variance within the International Steam Coal Market, Energy Economics, 36: 594-604.
• Phillips, P.C.B. & Perron, P. (1988). Testing for a Unit Root in Time Series Regression, Biometrika, 75(2), 335–346.
• Qiao, X., Zhu, H. & Hau, L. (2020). Time-Frequency Co-Movement of Cryptocurrency Return and Volatlity: Evidence from Wavelet Coherence Analysis, International Review of Financial Analysis, 71: 1-14.
• Ross,S.A.(1989). Information and Volatility : The No-Arbitrage Martingale Approach to Timing and Resolution Irrelevancy, Journal of Finance,44: 1-17.
• Sensoy, A., Silva, T.C., Corbet, S. & Tabak, B.M. (2021). High-Frequency Return and Volatility Spillovers Among Cryptocurrencies , Applied Economics, 134: 1-22.
• Tu, Z., & Xue, C. (2021). Effect of Bifurcation on the Interaction between Bitcoin and Litecoin, Finance Research Letters, 31, 382–385.
• Yahoo Finance (2021). Cryptocurency Data, https://finance.yahoo.com/cryptocurrencies/ , Erişim Tarihi: 19.05.2021.
• Yousaf, I. & Ali, S. (2020). The COVID-19 Outbreak and High Frequency Information Transmission between Major Cryptocurrencies: Evidence from the VAR-DCC-GARCH Approach, Borsa Istanbul Review, 20: 1-10.