Substitution Dynamics Between Traditional Money and Crypto Assets: An Analysis of Bitcoin and the M2 Money Supply

Abstract

In the literature, the concept of monetary substitution is defined as economic units preferring alternative stores of value or exchange instruments instead of domestic currency in their portfolio choices and is mostly associated with inflation, financial instability, and trust factors. Cryptocurrencies, which emerged with the digitalization process, have added a new dimension to this traditional framework, causing monetary substitution to become a phenomenon not limited solely to foreign currencies. This study aims to evaluate the monetary substitution potential of cryptocurrencies by examining the nature of the relationship between Bitcoin and the traditional money supply. In this context, the dynamic relationships between Bitcoin, the US M2 money supply, and the VIX index, representing global risk perception, are analyzed using monthly data from 2015M05–2025M12. The stationarity properties of the variables are examined using the ADF and KPSS unit root tests, and then an extended VAR model with level values is estimated in accordance with the Toda–Yamamoto approach. The impulse response functions, cumulative impulse response functions, and variance decomposition results obtained from the model are used to evaluate the dynamic transmission mechanism between the variables. The findings reveal that Bitcoin is strongly affected by its own internal shocks, that increases in global risk perception have negative effects on Bitcoin, and that the impact of changes in the M2 money supply on Bitcoin increases over time. Overall, the results indicate that Bitcoin is not so much a strong monetary substitute for the traditional money supply, but rather a financial asset shaped by risk perception, liquidity conditions, and intra-market dynamics.

Keywords

• Bitcoin
• Money Supply
• Crypto Assets
• Monetary Substitution
• Toda–Yamamoto VAR

Geleneksel Para ve Kripto Varlıklar Arasında İkame Dinamikleri: Bitcoin ve M2 Para Arzı Üzerine Bir Analizi

Abstract

Literatürde parasal ikame kavramı, ekonomik birimlerin portföy tercihlerinde yerli para yerine alternatif değer saklama ya da mübadele araçlarını tercih etmeleri şeklinde tanımlanmakta ve çoğunlukla enflasyon, finansal istikrarsızlık ile güven faktörleriyle ilişkilendirilmektedir. Dijitalleşme süreciyle birlikte ortaya çıkan kripto varlıklar ise bu geleneksel çerçeveye yeni bir boyut ekleyerek, parasal ikamenin yalnızca yabancı para birimleriyle sınırlı kalmayan bir olguya dönüşmesine neden olmuştur. Bu çalışma, Bitcoin ile geleneksel para arzı arasındaki ilişkinin niteliğini inceleyerek kripto varlıkların parasal ikame potansiyelini değerlendirmeyi amaçlamaktadır. Bu kapsamda Bitcoin, ABD M2 para arzı ve küresel risk algısını temsil eden VIX endeksi arasındaki dinamik ilişkiler 2015M05–2025M12 dönemine ait aylık veriler kullanılarak analiz edilmiştir. Çalışmada değişkenlerin durağanlık özellikleri ADF ve KPSS birim kök testleriyle incelenmiş, ardından Toda–Yamamoto yaklaşımı doğrultusunda düzey değerlerle genişletilmiş VAR modeli tahmin edilmiştir. Modelden elde edilen etki-tepki fonksiyonları, kümülatif etki-tepki fonksiyonları ve varyans ayrıştırması sonuçları değişkenler arasındaki dinamik aktarım mekanizmasını değerlendirmek amacıyla kullanılmıştır. Bulgular, Bitcoin’in kendi içsel şoklarından güçlü biçimde etkilendiğini, küresel risk algısındaki artışların Bitcoin üzerinde negatif etkiler oluşturduğunu ve M2 para arzındaki değişimlerin Bitcoin üzerindeki etkisinin zaman içerisinde artış gösterdiğini ortaya koymaktadır. Genel olarak sonuçlar, Bitcoin’in geleneksel para arzına karşı güçlü bir parasal ikame aracı olmaktan ziyade, risk algısı, likidite koşulları ve piyasa içi dinamikler tarafından şekillenen finansal bir varlık niteliği taşıdığını göstermektedir.

Keywords

• Bitcoin
• Para Arzı
• Kripto Varlıklar
• Parasal İkame
• Toda–Yamamoto VAR

References

1. Bank for International Settlements (2020). Central bank digital currencies: Foundational principles and core features.BIS Report, No. 1.
2. Baş, T.;Özaydın, O.; Dura, Y. (2023). Does Bitcoin affect term deposits? Evidence from MINT countries. Economics, 17(1), 20220056. https://doi.org/10.1515/econ-2022-0056
3. Baur, D. G.; Hong, K.; Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative asset? Journal of International Financial Markets, Institutions and Money, 54, 177–189.
4. Baur, D.G.; Dimpfl, T. (2021). The volatility of Bitcoin and its role as a medium of exchange and a store of value. Empirical Economics, 61, 2663–2683. https://doi.org/10.1007/s00181-020-01990-5
5. Bernanke, B. S. (1986). Alternative explanations of the money-income correlation. Carnegie-Rochester Conference Series on Public Policy, 25, 49–99.
6. Bernanke, B. S. (2005). The global saving glut and the U.S. current account deficit. Sandridge Lecture, Virginia Association of Economists.
7. Blau, B. M.; Griffith, T. G.; Whitby, R. J. (2021). Inflation and Bitcoin: A descriptive time-series analysis. Economics Letters, 203, 109848. https://doi.org/10.1016/j.econlet.2021.109848
8. Bouri, E.; Molnár, P.; Azzi, G.; Roubaud, D.; Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin. Finance Research Letters, 24, 196–201.

9. Böhme, R.; Christin, N.; Edelman, B.; Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213–238.
10. Cheah, E.; Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32–36. https://doi.org/10.1016/j.econlet.2015.02.029
11. Choi, S.; Shin, J. (2022). Bitcoin: An inflation hedge but not a safe haven. Finance Research Letters, 46, 102379, https://doi.org/10.1016/j.frl.2021.102379.
12. 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 and Money, 52, 173–195.
13. Conlon, T. ; Corbet, S. ; McGee, R. J. (2021). Inflation and cryptocurrencies revisited: A time-scale analysis. Economics Letters, 206, 109996, https://doi.org/10.1016/j.econlet.2021.109996.
14. 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
15. Çil Yavuz, N; (2006). Türkiye’de Turizm Gelirlerinin Ekonomik Büyümeye Etkisinin Testi: Yapısal Kırılma ve Nedensellik Analizi. Doğuş Üniversitesi Dergisi, 7(2), 162-171.
16. Dolado, J. J.,; Lütkepohl, H. (1996). MakingWaldTestsWorkforCointegrated VAR Systems. EconometricReviews, 15(4), 369–386.
17. Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar: A GARCH volatility analysis. Finance Research Letters, 16, 85–92.
18. Enders, W. (2015). Applied Econometric Time Series,4thEdition; Wiley.
19. Engle, R. F., & Granger, C. W. J. (1987). Co-integrationanderrorcorrection: Representation, estimation, andtesting. Econometrica, 55(2), 251-276.
20. Friedman, M. (1968). The role of monetary policy. American Economic Review, 58(1), 1–17.
21. Friedman, M. (1969). The Optimum Quantity of Money; Aldine De Gruyter Publishing.
22. Gaies, B.; Nakhli, M. S.; Sahut, J.-M.; Schweizer, D. (2023). Interactions between investors’ fear and greed sentiment and Bitcoin prices. The North American Journal of Economics and Finance, 67, 101924. https://doi.org/10.1016/j.najef.2023.101924
23. Gurdgiev, C.; O’Loughlin, D. (2020). Herding and anchoring in cryptocurrency markets: Investor reaction to fear and uncertainty. Journal of Behavioral and Experimental Finance, 25, 100271. https://doi.org/10.1016/j.jbef.2020.100271
24. Hacibedel, B.; Perez Saiz, H. (2023). Assessing Macrofinancial Risks from Crypto Assets. IMF Working Paper No. 2023/214, http://dx.doi.org/10.5089/9798400257117.001.
25. Hacker, R. S.; Hatemi-J, A. (2006). Testsforcausalitybetweenintegratedvariablesusingasymptoticandbootstrapdistributions: Theoryandapplication. Applied Economics, 38(13), 1489-1500.
26. Hamilton, J. D. (1994). Time Series Analysis; Princeton University Press.
27. Johansen, S. (1988). Statistical analysis of cointegrationvectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254.
28. Johansen, S. (1991). EstimationandHypothesisTesting of CointegrationVectors in GaussianVectorAutoregressiveModels. Econometrica, 59(6), 1551–1580.
29. Khan, M. S.; Agénor, P. R. (1992). Foreign Currency Deposits and the Demand for Money in Developing Countries. IMF Working Papers 1992/ 001, https://doi.org/10.5089/9781451931303.001.
30. Kokabian, P. (2025). The M2-Bitcoin elasticity: A cointegration analysis (2015–2025). Preprints, https://doi.org/10.20944/preprints202506.1963.v1
31. Kristoufek, L. (2015). What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis. PLoS ONE, 10(4), e0123923. https://doi.org/10.1371/journal.pone.0123923
32. Levulytė, L.; Šapkauskienė, A. (2021). Cryptocurrency in context of fiat money functions. The Quarterly Review of Economics and Finance, 82, 44–54.
33. Liu, J.; Valcarcel, V. J. (2024). Hedging inflation expectations in the cryptocurrency futures market. Journal of Financial Stability, 70, 101205. https://doi.org/10.1016/j.jfs.2023.101205.
34. Llanos, D. R.; Perote, J.; Vicente-Lorente, J. D. (2026). Liquidity, scarcity, and long-run anchoring in Bitcoin demand (2010–2025). International Review of Economics & Finance, 109, 105320. https://doi.org/10.1016/j.iref.2026.105320
35. Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis; Springer.
36. Mankiw, N. G. (2021). Macroeconomics, 10thEdition; Worth Publishers.
37. Marmora, P. (2021). Currency substitution in the shadow economy: International panel evidence using local Bitcoin trade volume. Economics Letters, 205, 109926.
38. Mavrotas, G.; Kelly, R. (2001). Oldwine in newbottles: Testingcausalitybetweensavingsandgrowth. The Manchester School, 69(S1), 97-105.
39. Menger, K. (1892). On the origin of money. The Economic Journal, 2(6), 239–255.
40. Mishkin, F. S. (2019). The economics of money, banking, and financial markets, 12th Edition; Pearson.
41. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. (Online) https://bitcoin.org/bitcoin.pdf
42. Narayan, P. K. (2005). The Saving and Investment Nexus for China: EvidencefromCointegrationTests. Applied Economics, 37(17), 1979–1990.
43. Pesaran, M. H.;Shin, Y. (1999). An Autoregressive Distributed-LagModellingApproachtoCointegration Analysis. In S. Strom (Ed.), EconometricsandEconomicTheory in the 20th Century (pp. 371–413). Cambridge UniversityPress.
44. Pesaran, M. Shin Y., Smith, R. (2001). Bounds testing approaches to the analysisof level relationships. Journal of Applied Econometrics, 16(3), 289-326.
45. Pietrzak, M. (2023). What can monetary policy tell us about Bitcoin? Annals of Finance, 19, 545–559. https://doi.org/10.1007/s10436-023-00432-3
46. Rambaldi, A. N.; Doran, H. E. (1996). Testingfor Granger non-causality in cointegratedsystemsmadeeasy. WorkingPapers in EconometricsandAppliedStatistics, No. 88, University of New England.
47. Rodriguez, H.; Colombo, J. (2025). Is Bitcoin an inflation hedge? Journal of Economics and Business, 133, 106218. https://doi.org/10.1016/j.jeconbus.2024.106218
48. Roubini, N. (2018). Exploring the cryptocurrency and blockchain ecosystem. Testimony before the Committee on Banking, Housing, and Urban Affairs, United States Senate.
49. Sakariyahu, R.; Lawal, R.; Adigun, R.; Paterson, A.; Johan, S. (2024). One crash, too many: Global uncertainty, sentiment factors and cryptocurrency market. Journal of International Financial Markets, Institutions and Money, 94, 102028. https://doi.org/10.1016/j.intfin.2024.102028
50. Shahzad, S. J. H.; Bouri, E.; Roubaud, D.; Kristoufek, L.; Lucey, B. (2019). Is Bitcoin a better safe-haven investment than gold and commodities? International Review of Financial Analysis, 63, 322–330.
51. Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48.
52. Sims, C. A.; Stock, J. H.; Watson, M. W. (1990). Inference in linear time series models with some unit roots. Econometrica, 58(1), 113–144.
53. Smales, L. (2024). Cryptocurrency as an alternative inflation hedge? Accounting and Finance, 64(2), 1589–1611.
54. Sovbetov, Y. (2018). Factors influencing cryptocurrency prices: Evidence from Bitcoin, Ethereum, Dash, Litecoin, and Monero. Journal of Economics and Financial Analysis, 2(2), 1–27.
55. Stock, J. H.; Watson, M. W. (2001). Vector autoregressions. Journal of Economic Perspectives, 15(4), 101–115.
56. Stock, J. H.; Watson, M. W. (2011). Introduction to Econometrics; Addison-Wesley.
57. Toda, H. Y.;Yamamoto, T. (1995). Statistical Inference in VectorAutoregressionswithPossiblyIntegratedProcesses. Journal of Econometrics, 66(1–2), 225–250.
58. Woodford, M. (2003). Interest and prices: Foundations of a theory of monetary policy; Princeton University Press.
59. Yermack, D. (2013). Is Bitcoin a real currency? NBER Working Paper Series, No. 19747.
60. Zapata, H. O.;Rambaldi, A. N. (1997). Monte Carlo evidence on cointegrationandcausation. Oxford Bulletin of Economics and Statistics, 59(2), 285-298.