Interest rate sensitivity of traditional, green, and stable cryptocurrencies: A comparative study across market conditions

Francisco Jareño; María de la O González; José M. Almansa; Francisco Jareño; María de la O González; José M. Almansa

doi:10.3934/QFE.2025004

Quantitative Finance and Economics

2025, Volume 9, Issue 1: 100-130. doi: 10.3934/QFE.2025004

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Research article

Interest rate sensitivity of traditional, green, and stable cryptocurrencies: A comparative study across market conditions

Department of Economics and Finance, Universidad de Castilla-La Mancha, Plaza de la Universidad, 1, 02071, Albacete, Spain

Received: 26 October 2024 Revised: 27 January 2025 Accepted: 18 February 2025 Published: 21 February 2025
JEL Codes: C21, C22, C51, F21, G12, G32, H12

Background
This study examines the impact of interest rate fluctuations on the returns of traditional, "green", and "stable" cryptocurrencies from April 2019 to April 2023. Bitcoin, Cardano, and Tether represent these categories due to their market significance.
Methods
Using quantile regression (QR), the study analyzes the impact of interest rate shocks on cryptocurrency returns during bullish and bearish market periods. It also decomposes nominal interest rates into real interest rates and inflation expectations. The sample period is divided into stable and rising interest rate sub-periods for robustness.
Results
The results show that cryptocurrency returns are more sensitive to interest rate fluctuations in both bullish and bearish periods. The sensitivity varies across cryptocurrency types, with Cardano acting as a hedge against inflation risk during bearish periods.
Conclusions
The results support the research hypotheses and provide insights into the behavior of cryptocurrencies under different market conditions. These findings help portfolio managers and policymakers to make informed decisions in a digital financial environment. Future research should explore the interactions between cryptocurrencies and other financial markets.
- interest rates,
- inflation expectations,
- cryptocurrency returns,
- quantile regression,
- market conditions
Citation: Francisco Jareño, María de la O González, José M. Almansa. Interest rate sensitivity of traditional, green, and stable cryptocurrencies: A comparative study across market conditions[J]. Quantitative Finance and Economics, 2025, 9(1): 100-130. doi: 10.3934/QFE.2025004

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Abstract

Background

This study examines the impact of interest rate fluctuations on the returns of traditional, "green", and "stable" cryptocurrencies from April 2019 to April 2023. Bitcoin, Cardano, and Tether represent these categories due to their market significance.

Methods

Using quantile regression (QR), the study analyzes the impact of interest rate shocks on cryptocurrency returns during bullish and bearish market periods. It also decomposes nominal interest rates into real interest rates and inflation expectations. The sample period is divided into stable and rising interest rate sub-periods for robustness.

Results

The results show that cryptocurrency returns are more sensitive to interest rate fluctuations in both bullish and bearish periods. The sensitivity varies across cryptocurrency types, with Cardano acting as a hedge against inflation risk during bearish periods.

Conclusions

The results support the research hypotheses and provide insights into the behavior of cryptocurrencies under different market conditions. These findings help portfolio managers and policymakers to make informed decisions in a digital financial environment. Future research should explore the interactions between cryptocurrencies and other financial markets.

References

[1]	Adão LFS, Silveira D, Ely RA, et al. (2022) The impacts of interest rates on banks' loan portfolio risk-taking. J Econ Dyn Control 144: 104521. https://doi.org/10.1016/j.jedc.2022.104521 doi: 10.1016/j.jedc.2022.104521
[2]	Aharon DY, Umar Z, Vo XV (2021) Dynamic spillovers between the term structure of interest rates, bitcoin, and safe-haven currencies. Financial Innovation 7: 59. https://doi.org/10.1186/s40854-021-00274-w doi: 10.1186/s40854-021-00274-w
[3]	Ampudia M, Van den Heuvel SJ (2022) Monetary Policy and Bank Equity Values in a Time of Low and Negative Interest Rates. J Monetary Econ 130: 49–67. https://doi.org/10.1016/j.jmoneco.2022.05.006 doi: 10.1016/j.jmoneco.2022.05.006
[4]	Bats JV, Giuliodori M, Houben ACFJ (2023) Monetary policy effects in times of negative interest rates: What do bank stock prices tell us? J Financ Intermed 53: 101003. https://doi.org/10.1016/j.jfi.2022.101003 doi: 10.1016/j.jfi.2022.101003
[5]	Buchinsky M (1998) Recent advances in quantile regression models: a practical guideline for empirical research. J Hum Resour 33: 88–126. https://doi.org/10.2307/146316 doi: 10.2307/146316
[6]	Bullock M (2023) How well placed are households for interest rate increases. Econ Anal Policy 77: 222–230. https://doi.org/10.1016/j.eap.2022.11.004 doi: 10.1016/j.eap.2022.11.004
[7]	Caggese A, Pérez-Orive A (2022) How stimulative are low real interest rates for intangible capital? Eur Econ Rev 142: 103987. https://doi.org/10.1016/j.euroecorev.2021.103987 doi: 10.1016/j.euroecorev.2021.103987
[8]	Cao J, Dinger V, Gómez T, et al. (2023) Monetary policy spillover to small open economies: Is the transmission different under low interest rates? J Financ Stabil 65: 101116. https://doi.org/10.1016/j.jfs.2023.101116 doi: 10.1016/j.jfs.2023.101116
[9]	Díaz CO, Mayorga RM, Amaya AJ, et al. (2022) The importance of cryptocurrencies and their impact on international financial Markets based on the evolution of bitcoin. Realidad Empresarial 14: 10–25. https://doi.org/10.51378/reuca.v1i14.7525 doi: 10.51378/reuca.v1i14.7525
[10]	Eldomiaty T, Khaled N (2024) Cryptocurrency volatility and Egyptian stock market indexes: A note. Modern Financ 2: 121–130. https://doi.org/10.61351/mf.v2i1.138 doi: 10.61351/mf.v2i1.138
[11]	Escribano A, Jareño F, Cano JA (2023) Study of the leading European construction companies using risk factor models. Int J Financ Econ 28: 3386–3402. https://doi.org/10.1002/ijfe.2598 doi: 10.1002/ijfe.2598
[12]	Esparcia C, Fakhfakh T, Jareño F (2024) The green, the dirty and the stable: Diversifying equity portfolios by adding tokens of different nature. N Am J Econ Financ 69: 102020. https://doi.org/10.1016/j.najef.2023.102020 doi: 10.1016/j.najef.2023.102020
[13]	Gimeno R, Ibáñez A (2018) The eurozone (expected) inflation: An option"s eyes view. J Int Money Financ 86: 70–92. https://doi.org/10.1016/j.jimonfin.2018.03.018 doi: 10.1016/j.jimonfin.2018.03.018
[14]	González MO, Jareño F (2019) Testing extensions of Fama & French models: A quantile regression approach. Q Rev Econ Financ 71: 188–204. https://doi.org/10.1016/j.qref.2018.08.004 doi: 10.1016/j.qref.2018.08.004
[15]	González MO, Jareño F, Skinner FS (2020) Nonlinear autoregressive distributed lag approach: An application on the connectedness between bitcoin returns and the other ten most relevant cryptocurrency returns. Mathematics 8: 810. https://doi.org/10.3390/MATH8050810 doi: 10.3390/MATH8050810
[16]	González MO, Jareño F, Skinner FS (2021) Asymmetric interdependencies between large capital cryptocurrency and Gold returns during the COVID-19 pandemic crisis. Int Rev Financ Analy 76: 101773. https://doi.org/10.1016/j.irfa.2021.101773 doi: 10.1016/j.irfa.2021.101773
[17]	Haq IUI, Bouri E (2022) Sustainable versus Conventional Cryptocurrencies in the Face of Cryptocurrency Uncertainty Indices: An Analysis across Time and Scales. J Risk Financ Manag 15: 101773. https://doi.org/10.3390/jrfm15100442 doi: 10.3390/jrfm15100442
[18]	Iwanicz-Drozdowska M, Rogowicz K (2022) Does the choice of monetary policy tool matter for systemic risk? The curious case of negative interest rates. J Int Financ Mark I 79: 101608. https://doi.org/10.1016/j.intfin.2022.101608 doi: 10.1016/j.intfin.2022.101608
[19]	Jareño F, Navarro E (2010) Stock interest rate risk and inflation shocks. Eur J Oper Res 201: 337–348. https://doi.org/10.1016/j.ejor.2009.03.025 doi: 10.1016/j.ejor.2009.03.025
[20]	Jareño F, González MO, Tolentino M, et al. (2020) Bitcoin and gold price returns: A quantile regression and NARDL analysis. Resour Policy 67: 101666. https://doi.org/10.1016/j.resourpol.2020.101666 doi: 10.1016/j.resourpol.2020.101666
[21]	Just M, Echaust K (2024) Cryptocurrencies against stock market risk: New insights into hedging effectiveness. Res Int Bus Financ 67: 102134. https://doi.org/10.1016/j.ribaf.2023.102134 doi: 10.1016/j.ribaf.2023.102134
[22]	Kamal JB, Hassan MK (2022) Asymmetric connectedness between cryptocurrency environment attention index and green assets. J Econ Asymmetries 25: e00240. https://doi.org/10.1016/j.jeca.2022.e00240 doi: 10.1016/j.jeca.2022.e00240
[23]	Koenker R, Bassett G (1978) Regression Quantiles. Econometrica 46: 33–50. https://doi.org/10.2307/1913643 doi: 10.2307/1913643
[24]	Koenker R, Hallock K (2001) Quantile regression. J Econ Perspect 15: 143–156. https://doi.org/10.1257/jep.15.4.143 doi: 10.1257/jep.15.4.143
[25]	Li L (2023) The risks of trading on cryptocurrencies: A regime-switching approach based on volatility jumps and co-jumping behaviours. Appl Econ 56: 779–795. https://doi.org/10.1080/00036846.2023.2170970 doi: 10.1080/00036846.2023.2170970
[26]	Lobão J (2022) Herding Behavior in the Market for Green Cryptocurrencies: Evidence from CSSD and CSAD Approaches. Sustainability 14: 12542. https://doi.org/10.3390/su141912542 doi: 10.3390/su141912542
[27]	Mokni K, Youssef M, Ajmi AN (2022) COVID-19 pandemic and economic policy uncertainty: The first test on the hedging and safe haven properties of cryptocurrencies. Res Int Bus Financ 60: 101573. https://doi.org/10.1016/j.ribaf.2021.101573 doi: 10.1016/j.ribaf.2021.101573
[28]	Naeem MA, Karim S, Farid S, et al. (2022) Comparing the asymmetric efficiency of dirty and clean energy markets pre and during COVID-19. Econ Anal Policy 75: 548–562. https://doi.org/10.1016/j.eap.2022.06.015 doi: 10.1016/j.eap.2022.06.015
[29]	Okorie DI, Bouri E, Mazur M (2024) NFTs versus conventional cryptocurrencies: A comparative analysis of market efficiency around COVID-19 and the Russia-Ukraine conflict. Q Rev Econ Financ 95: 126–151. https://doi.org/10.1016/j.qref.2024.03.001 doi: 10.1016/j.qref.2024.03.001
[30]	Ren B, Lucey B (2022) A clean, green haven?—Examining the relationship between clean energy, clean and dirty cryptocurrencies. Energ Econ 109: 105951. https://doi.org/10.1016/j.eneco.2022.105951 doi: 10.1016/j.eneco.2022.105951
[31]	Schelling T, Towbin P (2022) What lies beneath—Negative interest rates and bank lending. J Financ Intermed 51: 100969. https://doi.org/10.1016/j.jfi.2022.100969 doi: 10.1016/j.jfi.2022.100969
[32]	Sevillano MC, Jareño F (2018) The Impact of International Factors on Spanish Company Returns: A Quantile Regression Approach. Risk Manage 20: 51–76. https://doi.org/10.1057/s41283-017-0027-7 doi: 10.1057/s41283-017-0027-7
[33]	Umar Z, Jareño F, González M de la O (2021) The impact of COVID-19-related media coverage on the return and volatility connectedness of cryptocurrencies and fiat currencies. Technol Forecast Social Change 172: 121025. https://doi.org/10.1016/j.techfore.2021.121025 doi: 10.1016/j.techfore.2021.121025
[34]	Urdaneta AJ, Borgucci EV, Campuzano JA, et al. (2019) Hechos estilizados del mercado de criptomonedas desde el enfoque de la eficiencia marginal del capital. Visionario Digital 3: 228–244. https://doi.org/10.33262/visionariodigital.v3i2.421 doi: 10.33262/visionariodigital.v3i2.421
[35]	Wang GJ, Ma X, Wu H (2020) Are stablecoins truly diversifiers, hedges, or safe havens against traditional cryptocurrencies as their name suggests? Res Int Bus Financ 54: 101225. https://doi.org/10.1016/j.ribaf.2020.101225 doi: 10.1016/j.ribaf.2020.101225
[36]	Xiao H, Sun Y (2020) Forecasting the Returns of Cryptocurrency: A Model Averaging Approach. J Risk Financ Manag 13: 278. https://doi.org/10.3390/jrfm13110278 doi: 10.3390/jrfm13110278
[37]	Yousaf I, Jareño F, Esparcia C (2022) Tail connectedness between lending/borrowing tokens and commercial bank stocks. Int Rev Financ Anal 84: 102417. https://doi.org/10.1016/j.irfa.2022.102417 doi: 10.1016/j.irfa.2022.102417
[38]	Zaremba A, Cakici N, Bianchi RJ, et al. (2023) Interest rate changes and the cross-section of global equity returns. J Econ Dyn Control 147: 104596. https://doi.org/10.1016/j.jedc.2023.104596 doi: 10.1016/j.jedc.2023.104596

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