Kripto Döngüsü ve ABD Para Politikası: Faktör Temelli VAR ve EKK Analizi
DOI:
https://doi.org/10.63556/tisej.2026.1921Anahtar Kelimeler:
ABD Para Politikası- Kripto Para Fiyatları- Kriptofaktör- VAR Modeli- Küresel Finansal DöngüÖz
Bu çalışma, ABD para politikası ve küresel finansal koşulların kripto varlık fiyatları üzerindeki etkisini Nisan 2020–Mayıs 2026 dönemi için incelemektedir. Dokuz kripto paranın ortak fiyat hareketi Temel Bileşenler Analizi ile kriptofaktöre dönüştürülmüştür. Birinci bileşenin toplam varyansın yüzde 74’ünü açıklaması kripto para piyasaları için baskın sistemik bir bileşenin varlığını teyit etmektedir. Kısa dönemli dinamikler VAR Modeli, uzun dönemli seviye ilişkileri ise Newey-West HAC düzeltmeli En Küçük Kareler yöntemiyle tahmin edilmiştir. Model güvenilirliği AR karakteristik polinom ve Bai-Perron çoklu yapısal kırılmalar testleriyle doğrulanmıştır. VAR etki tepki analizi, SOFR ve 2 yıl vadeli ABD Hazine tahvil faizindeki pozitif şokların kriptofaktör üzerinde kalıcı bir negatif tepkiyle ilişkili olduğunu ortaya koymaktadır. Kriptofaktörün kendi şoklarına verdiği tepki otuzuncu döneme kadar negatif seyrederek ortalamaya dönüş dinamizmini yansıtmaktadır. Varyans ayrıştırması, kriptofaktör varyansının yüzde 98,89’inin içsel şoklardan kaynaklandığını ve emtia değişkenlerinin para politikası değişkenlerinden daha yüksek bir dışsal pay taşıdığını göstermektedir. EKK bulguları, altın ve Brent petrolün kriptofaktör fiyatlamaları üzerinde istatistiksel olarak güçlü ve anlamlı etkiler taşırken para politikası değişkenlerinin gecikmeli ve şok odaklı bir yapı sergilediğini ortaya koymaktadır. Bulgular, kripto paraların kısa vadede içsel momentum, orta vadede para politikası şokları ve uzun vadede küresel emtia döngüsü ile yapısal büyüme trendi tarafından biçimlenen çok katmanlı bir fiyatlama yapısı sergilediğini göstermektedir.
Referanslar
Adrian, T., Iyer, T., & Qureshi, M. (2022). Crypto prices move more in sync with stocks, posing new risks. In IMF. https://www.imf.org/en/Blogs/Articles/2022/01/11/crypto-prices-move-more-in-sync-with-stocks-posing-new-risks
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
Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. https://doi.org/10.2307/2998540
Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1–22. https://doi.org/10.1002/jae.659
Baur, D. G., Hong, K., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets? Journal of International Financial Markets, Institutions and Money, 54, 177-189. https://doi.org/10.1016/j.intfin.2017.12.004
Benigno, P. (2023). Monetary policy in a world of cryptocurrencies. Journal of the European Economic Association, 21(4), 1363–1396. https://doi.org/10.1093/jeea/jvac066
Bianchi, D., Faccini, R., & Huse, C. (2022). Monetary policy uncertainty and Bitcoin returns. Journal of Financial Economics, 146(3), 778–805. https://doi.org/10.1016/j.jfineco.2022.09.004
Borio, C. (2021). Back to the future: Intellectual challenges for monetary policy (BIS Working Papers No. 950). Bank for International Settlements. https://www.bis.org/publ/work950.htm
Campello, M., Gallo, A., & Terracciano, T. (2025). Demand for safety in the crypto ecosystem. The Journal of Finance. https://afajof.org/management/viewp.php?n=170464
Che, N., Copestake, A., Furceri, D., & Terracciano, T. (2023). The Crypto Cycle and US Monetary Policy. IMF Working Papers, 2023(163), 1. https://doi.org/10.5089/9798400245411.001
Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28–34. https://doi.org/10.1016/j.econlet.2018.01.004
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(366), 427–431. https://doi.org/10.2307/2286348
Dyhrberg, A. H. (2016). Hedging capabilities of bitcoin. Is it the virtual gold? Finance Research Letters, 16, 139–144. https://doi.org/10.1016/j.frl.2015.10.025
Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. https://doi.org/10.2307/1913236
Federal Reserve Bank of New York. (t.y.). Secured overnight financing rate (SOFR): Data and methodology. https://www.newyorkfed.org/markets/reference-rates/sofr
Granger, C. W. J., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2(2), 111–120. https://doi.org/10.1016/0304-4076(74)90034-7
Greene, W. H. (2003). Econometric analysis (5th ed.). Prentice Hall.
Hamilton, J. D. (1994). Time series analysis. Princeton University Press.
Hodula, M. (2025). Retail crypto investors when facing financial constraints: Evidence from energy shocks and the use and downloads of crypto trading apps. Energy Economics, 144, 108338. https://doi.org/10.1016/j.eneco.2025.108338
Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179–185. https://doi.org/10.1007/BF02289447
Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417–441. https://doi.org/10.1037/h0071325
Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2–3), 231–254. https://doi.org/10.1016/0165-1889(88)90041-3
Karau, S. (2023). Monetary policy and Bitcoin. Journal of International Money and Finance, 137, 102880. https://doi.org/10.1016/j.jimonfin.2023.102880
Katsiampa, P., Corbet, S., & Lucey, B. (2019). Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis. Finance Research Letters, 29, 68–74. https://doi.org/10.1016/j.frl.2019.03.009
Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer. https://doi.org/10.1007/978-3-540-27752-1
Ma, C., Tian, Y., Hsiao, S., & Deng, L. (2022). Monetary policy shocks and Bitcoin prices. Research in International Business and Finance, 62, 101711. https://doi.org/10.1016/j.ribaf.2022.101711
MacKinnon, J. G. (1996). Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics, 11(6), 601–618. https://doi.org/10.1002/(SICI)1099-1255(199611)11:6%3C601::AID-JAE417%3E3.0.CO;2-T
Miranda-Agrippino, S., & Rey, H. (2020). US monetary policy and the global financial cycle. The Review of Economic Studies, 87(6), 2754–2776. https://doi.org/10.1093/restud/rdaa019
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. https://bitcoin.org/bitcoin.pdf
Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703-708. https://doi.org/10.2307/1913610
Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559–572. https://doi.org/10.1080/14786440109462720
Polyzos, S., & Youssef, M. (2025). Do cryptocurrencies react differently to macroeconomic and geopolitical events? Evidence from a big data event study. Finance Research Open, 1, 100008. https://doi.org/10.1016/j.finreop.2025.100008
Raza, S. A., Shah, N., & Shahbaz, M. (2022). Does economic policy uncertainty influence gold prices? Evidence from a nonparametric causality-in-quantiles approach. Resources Policy, 49, 352–360. https://doi.org/10.1016/j.resourpol.2016.09.006
Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599–607. https://doi.org/10.1093/biomet/71.3.599
Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48. https://doi.org/10.2307/1912017
Smales, L. A. (2022). Investor attention and the response of US stock market sectors to the COVID-19 crisis. International Review of Economics & Finance, 80, 1180–1194. https://doi.org/10.1016/j.iref.2022.03.002
Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82. https://doi.org/10.1016/j.econlet.2016.09.019.
Wang, P., Li, X., Shen, D., & Zhang, W. (2020). How does economic policy uncertainty affect the Bitcoin market? Research in International Business and Finance, 53, 101234. https://doi.org/10.1016/j.ribaf.2020.101234
Zhao, J., & Zhang, T. (2023). Exploring the time-varying dependence between Bitcoin and the global stock market: Evidence from a TVP-VAR approach. Finance Research Letters, 58, 104342. https://doi.org/10.1016/j.frl.2023.104342
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Telif Hakkı (c) 2026 Üçüncü Sektör Sosyal Ekonomi Dergisi

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