KRİPTO PARA PİYASALARINDA FİNANSAL RİSK YÖNETİMİ

Yıl 2021, Cilt: 6 Sayı: 4, 735 - 755, 31.12.2021

Önder Büberkökü

https://doi.org/10.29106/fesa.996151

Cited By: 1

Öz

Bu çalışmada Binance coin (BCH), Bitcoin cash (BNB), Stellar (XLM) ve Cardano’dan (ADA) oluşan kripto para birimlerini içeren yatırımların yol açabileceği risklerin nasıl ölçülebileceği ve yönetilebileceğine ilişkin analizler üzerinde durulmuştur. Bu amaçla öncelikle van der Weide (2002) tarafından geliştirilen dört değişkenli GO-GARCH-NLS (Generalized orthogonal- general autoregressive conditional heteroskedasticity- non-linear least squares) modeli kullanılarak ilgili kripto para birimleri için zamanla değişen şartlı varyans, kovaryans ve korelasyon değerleri elde edilmiş, ardından Kroner ve Sultan (1993) ile Kroner ve Ng (1998) tarafından geliştirilen yaklaşımlar dikkate alınarak optimal portföy ağırlıkları ile optimal hedge rasyoları belirlenmiştir. Çalışmada ayrıca hem tekil kripto para birimleri hem de bu kripto para birimlerine dayalı olarak oluşturulan optimal portföyler için kısa ve uzun pozisyonlar dikkate alınarak yeniden örnekleme yöntemine (boostrapped) dayalı tarihi simülasyon yöntemi ile piyasa riski ölçüm analizlerine yer verilmiştir. Tüm bu analizler sonucunda bu kripto para birimlerine dayalı olarak beklenen getiri oranlarında bir değişikliğe yol açmadan riski minimize eden optimal portföy ağırlıklarının nasıl belirlenebileceği, bu optimal portföylerin taşıdığı piyasa riskinin ve sağladığı çeşitlendirme etkisin ne olduğu ve her bir kripto para biriminde taşınabilecek uzun (kısa) pozisyonların yol açabileceği risklerin diğer para birimlerinde taşınabilecek kısa (uzun) pozisyonlar ile nasıl hedge edilebileceği gibi konulara dönük olarak önemli bulgulara ulaşılmıştır.

Anahtar Kelimeler

Kripto para birimleri, Yatırım stratejileri, Finansal risk yönetimi, Hedging işlemleri

Kaynakça

• ABAD, P., BENITO, S. ve LÓPEZ, C. (2014). A Comprehensive Review of Value At Risk Methodologies. The Spanish Review of Financial Economics 12, 15–32.
• AGGARWAL, V. (2021). Optimum Investor Portfolio Allocation in New Age Digital Assets. International Journal of Innavation Science, Yayımlanma aşamasında.
• ALEXANDER, C. (2001). Orthogonal GARCH, Chapter 2 (pp. 21–28) in C. Alexander (Ed.), Mastering Risk. London: Financial Times-Prentice Hall.
• AL-MANSOUR, B.Y. (2020). Cryptocurrency Market: Behavioral Finance Perspective. Journal of Asian Finance, Economics and Business, 7 (12), 159-168.
• ANTONAKAKIS, N., CHATZIANTONIOU, I. ve GABAUER, D. (2019). Cryptocurrency Market Contagion: Market Uncertainty, Market Complexity, and Dynamic Portfolios. Journal of International Financial Markets, Institutions & Money, 61, 37-51.
• AROURI, M. E.H., LAHIANI, A. ve NGUYEN, D.K. (2011). Return and Volatility Transmission Between World Oil Prices and Stock Markets of the GCC Countries. Economic Modelling, 28, 1815-1825.
• ARTZNER, P., DELBAEN, F., EBER, J. M. ve HEATH, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3), 203-228.
• ASHFORD, K. ve SCHMIDT, J. (2020). What Is Cryptocurrency?, https://www.forbes.com/advisor/ investing/what-is-cryptocurrency/ (Erişim tarihi :12.04.2021).
• BASEL COMMITTEE ON BANKING SUPERVISION (2016). Minimum Capital Requirements for Market Risk, https://www.bis.org/bcbs/publ/d352.htm (Erişim tarihi: 18.04.2021).
• BASHER, S.A. ve SADORSKY, P. (2016). Hedging Emerging Market Stock Prices With Oil, Gold, VIX, and Bonds: A Comparison Between DCC,ADCC and GO-GARCH. Energy Economics, 54, 235-247.
• BOLLERSLEV, T. (1990). Modelling The Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. The Review of Economics and Statistics, 72(3), 498-505.
• BOLLERSLEV, T. ve WOOLDRIDGE, J.M. (1992). Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time-Varying Covariances. Econometrics Review, 11(2),143-172.
• BOSWIJK, H.P. ve VAN DER WEIDE,R. (2006). Wake Me Up Before You GO-GARCH. UVA Econometrics, Discussion Paper: 2006/03,1-28.
• CHARFEDDINE, L., BENLAGHA, N. ve MAOUCHI, Y. (2020). Investigating The Dynamic Relationship Between Cryptocurrencies And Conventional Assets: Implications For Financial Investors. Economic Modelling,85, 198-217.
• COINMARKETCAP. Today's Cryptocurrency Prices by Market Cap. https://coinmarketcap.com/. (Erişim Tarihi: 28.04.2021).
• DICKEY, D. A. ve FULLER, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with Unit Root. Journal of the American Statistical Association, 74, 427–431.
• DUTTA, D. ve BHATTACHARYA, B. (2008). A Bootstrapped Historical Simulation Value-at-Risk Approach to S&P CNX Nifty. The National Conference on Money and Banking, IGIDR, Mumbai, India.
• EDERINGTON, L.H. (1979). The Hedging Performance of The New Futures Markets. The Journal of Finance, 34(1),157-170.
• EFRON, B. (1979). Bootstrap Methods: Another Look at The Jackknife. The Annals of Statistics,7(1), 1-26.
• EFRON, B. ve TIBSHIRANI, R. (1993). An Introduction to The Bootstrap. Chapman&Hall, New: York.
• ENGLE, R. (2002). Dynamic Conditional Correlation: A Simple Class Of Multivariate Generalized Autoregressive Conditional Heteroscedasticity Models. Journal of Business and Economic Statistics, 20(3), 339-350.
• ENGLE, R.F. ve KRONER, K.F.(1995). Multivariate Simultaneous Generalized ARCH. Econometric Theory, 11, 122–150.
• ENGLE, R.F., NG, V.K. ve ROTHSCHİLD, M. (1990). Asset Pricing With a Factorarch Covariance Structure. Journal of Econometrics, 45(1), 213-237.
• ESCANCIANO, J.C. VE PEI, P. (2012). Pitfalls in backtesting Historical Simulation VaR models. Journal of Banking & Finance 36, 2233–2244.
• HIDAJAT, T. (2019). Behavioural Biases in Bitcoın Trading. Fokus Ekonomi, 14(2), 337-354.
• HOSKİNG, J. R. M. (1980). The Multivariate Portmanteau Statistic. Journal of American Statistical Association 75(371), 602–7.
• ISENAH, G. M. ve OLUBUSOYE, O. E. (2016). Empirical Model For Forecasting Exchange Rate Dynamics: The GO-GARCH Approach. CBN Journal of Applied Statistics, The Central Bank of Nigeria, 7(1), 179-208.
• JARQUE, C.M. ve BERA, A. K. (1980). Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals. Economics Letters, 6 (3), 255–259.
• JIN, J., HAN, L. WU, L. ve ZENG, H. (2020).The Hedging Effectiveness Of Global Sectors in Emerging and Developed Stock Market. International Review of Economics & Finance,66, 92-117.
• KANG, H-J., LEE,S-G. ve PARK, S-Y. (2021). Information Efficiency in the Cryptocurrency market: The Efficient-Market Hypothesis. Journal of Computer Information Systems,2, 1-10.
• KAYA, Y. (2018). Analysis of Cryptocurrency Market and Drivers of the Bitcoin Price: Understanding The Price Drivers Of Bitcoin Under Speculative Environment. Master of Science Thesis, Stockholm: KTH Industrial Engineering and Management.
• KELLER, A. ve SCHOLZ, M. (2019). Trading Cryptocurrency Markets: Analyzing the Behavior of Bitcoin Investors. Fortieth International Conference on Information Systems. Munich, 15-18 December, p.1-17.
• KRİSTOUFEK, L. (2013). Bitcoin Meets Google Trends and Wikipedia: Quantifying the Relationship Between Phenomena of the Internet Era. Scientific Reports, 3, 1-7.
• KRONER, K.F. VE SULTAN, J. (1993). Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures. The Journal of Financial and Quantitative Analysis, 28(4), 535-551.
• KRONER, K.F. ve NG, V.K. (1998). Modeling Asymmetric Comovements of Asset Returns, The Review of Financial Studies, 11(4), 817–844.
• LEHMAN, R. (2017). A Behavioral Finance View of Cryptocurrencies. Retrieved from https://www. behavioral finance.com/bitcoin-behavior/2017/12/13/a-behavioralfinance-view-of-cryptocurrencies. (Erişim tarihi: 12.04.2021).
• LI, W. K. ve MCLEOD, A. I. (1981). Distribution of the Residual Autocorrelation in Multivariate ARMA Time Series Models. Journal of the Royal Statistical Society, Series B 43(2), 231–9.
• LJUNG, G.M. ve BOX, G.E.P. (1978). On a Measure of a Lack of Fit in Time Series Models. Biometrika, 65 (2), 297–303.
• MEEGAN, A., CORBET,S., LARKIN, C. ve LUCEY, B. (2021). Does Cryptocurrency Pricing Response to Gegulatory Intervantion Depend On Underlying Blockchain Architecture ?. Journal of International Financial Markets, Institutions & Money, 70, 1-22.
• MENSI, W., AL-YAHYAEE, K.M., AL-JARRAH, I.M.W., VO, X.V. ve KANG, S.H. (2020). Dynamic Volatility Transmission and Portfolio Management Across Major Cryptocurrencies: Evidence From Hourly Data. North American Journal of Economics and Finance, 54,1-14.
• PAL, D. ve MITRA, S.K. (2019). Hedging Bitcoin with other Financial Assets. Finance Research Letters, 30, 30-36.
• PAVLOVA, I. (2020). Blockchain ETFs: Dynamic Correlations and Hedging Capabilities. Managerial Finance, 47(5), 687-702.
• PFLUG, G. (2000). Some Remarks on the Value-at-Risk and the Conditional Value-at-Risk, in S. Uryasev (ed.), Probabilistic Constrained Optimization: Methodology and Applications, Netherlands: Kluwer Academic Publishers, 1-11.
• PHILLIPS, P.C.B. ve PERRON, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrika,75(2), 335–346.
• PIETERS, G. ve VIVANCO, S. (2016). Financial Regulations and Price Inconsistence across Bitcoin Markets. Information Economics and Policy, 39,1-14.
• PŁUCIENNIK, P. (2013). Influence Of The Amerıcan Fınancıal Market On Other Markets Durıng The Subprıme Crısıs. Folia Oeconomica Stetinensia, 12(2), 19-30.
• RADIVOJEVİĆ, N., DEVİĆ, Z. ve MUHOVİĆ, A. (2016). Bootstrap Historıcal Simulation. Bankarstvo, 45(3), 36-49.
• SHAHZAD, S.J.H., BOURI, E., ROUBAUD, D. ve KRISTOUFEK, L. (2020). Safe Haven, Hedge And Diversification For G7 Stock Markets: Gold Versus Bitcoin. Economic Modelling, 87, 212-224.
• SJOWALL, F. (2014). Alternative methods for value-at-risk estimation: A study from a Regulatory Perspective focused on the Swedish Market, Master of Science Thesis, KHT Industrial Engineering and Management Industrial Management.
• SONGÜL, H. (2010). Otoregresif Koşullu Değişen Varyans Modelleri: Döviz Kurları Üzerine Uygulama, Türkiye Cumhuriyet Merkez Bankası, Uzmanlık Yeterlilik Tezi, 1-68.
• TEMEL, G.O., ERDOĞAN, S. ve ANKARALI, H. (2012). Sınıflama Modelinin Performansını Değerlendirmede Yeniden Örnekleme Yöntemlerinin Kullanımı. Bilişim Teknolojileri Dergisi, 5 (3), 1-7.
• van der Weide, R. (2002). GO-GARCH: A Multivariate Generalized Orthogonal GARCH Model. Journal of Applied Econometrics, 17, 549-564.
• YAHOO FINANCE. Cryptocurency Data, https://finance.yahoo.com/cryptocurrencies/, (Erişim Tarihi: 24.04.2021).
• YOUSAF, I. ve ALI, S. (2020). The COVID-19 Outbreak and High Frequency Information Transmission Between Major Crtyptocurrencies: Evidence from the VAR-DCC-GARCH Approach. Borsa İstanbul Review, 20, 1-10.