Cryptocurrency risks in emerging markets are everywhere!

Cryptocurrency adoption in emerging markets presents unique systemic risks, especially as these economies integrate digital assets like Bitcoin (BTC) into their financial systems. Macroeconomic models consistently highlight that cryptocurrencies, particularly BTC, exhibit much higher volatility and systemic risk potential compared to traditional assets, warranting caution for investors in 2025.

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Systemic Risk Assessment in Macroeconomic Models

Modeling Approaches: Macroeconomic models such as single-index models, GARCH-based volatility models, and systemic risk measures (e.g., Value-at-Risk, Conditional Value-at-Risk) are used to quantify the risk that cryptocurrencies pose to financial systems. These models assess how shocks in crypto markets, especially BTC, can spill over into traditional financial markets, potentially destabilizing them (Soepriyanto, Havidz and Handika, 2023; Fang, Cao and Egan, 2023; Huang et al., 2024; Farroukh, Metzger and Mzoughi, 2024).
Findings in Emerging Markets: In Asia-Pacific, BTC’s systemic risk to broader financial markets is found to be relatively weak, but contagion is more pronounced in foreign exchange markets than in stock indices. Technological advancements and regulatory improvements can help stabilize these markets, but the risk of individual investor losses remains high (Soepriyanto, Havidz and Handika, 2023).

Volatility: Bitcoin vs. Traditional Assets

High Volatility: BTC and other major cryptocurrencies display significantly higher volatility than traditional assets like stocks or bonds. Value-at-Risk (VaR) analyses show that BTC’s risk profile is much broader, with frequent and severe price swings, making it a riskier asset for portfolios (Huang et al., 2024).
Market Disruption: BTC’s volatility can de-anchor investor expectations and disrupt key macroeconomic factors, especially during market downturns or “crash times.” This contrasts with the relative stability of traditional assets, which are less prone to such extreme fluctuations (Bojaj et al., 2022; Huang et al., 2024).

Systemic Risk Commonality and Contagion

Interconnected Risks: Cryptocurrencies tend to move together, amplifying systemic risk during downturns. This commonality means that diversification within crypto assets offers limited protection, unlike diversification among traditional assets (Boubaker et al., 2024; Gunay et al., 2023; Rahman et al., 2024).
Sectoral Risks: Certain sectors, such as smart contracts and major coins like BTC and Ethereum, are primary sources of systemic risk. During extreme market losses, the interconnectedness of these assets can reach up to 40%, increasing the risk of contagion (Gunay et al., 2023; Rahman et al., 2024).

Macroeconomic Predictors and Policy Implications

Macroeconomic Variables: Variables such as consumer confidence, inflation, and money supply are increasingly effective in forecasting crypto volatility, especially post-COVID-19. These predictors help enhance risk models but also highlight the sensitivity of crypto markets to broader economic shifts (Tzeng and Su, 2024).
Regulatory Focus: Models suggest that regulators in emerging markets should prioritize technological and accounting standards to mitigate systemic risks, rather than focusing solely on crypto-specific regulations (Soepriyanto, Havidz and Handika, 2023; Bojaj et al., 2022).

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Conclusion

Macroeconomic models reveal that cryptocurrencies, especially BTC, introduce higher systemic risks and volatility compared to traditional assets in emerging markets. While the direct threat to entire financial systems may be limited, the potential for contagion, rapid market swings, and investor losses is significant. Investors should exercise caution, recognizing that crypto assets behave fundamentally differently from traditional investments and require robust risk management strategies.

References

• Soepriyanto, G., Havidz, S., & Handika, R., 2023. Crypto goes East: analyzing Bitcoin, technological and regulatory contagions in Asia–Pacific financial markets using asset pricing. International Journal of Emerging Markets. https://doi.org/10.1108/ijoem-07-2022-1127
• Boubaker, S., Karim, S., Naeem, M., & Rahman, M., 2024. On the prediction of systemic risk tolerance of cryptocurrencies. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2023.122963
• Bojaj, M., Muhadinovic, M., Bracanovi?, A., Mihailovic, A., Radulovi?, M., Jolicic, I., Milosevic, I., & Milacic, V., 2022. Forecasting macroeconomic effects of stablecoin adoption: A Bayesian approach. Economic Modelling. https://doi.org/10.1016/j.econmod.2022.105792
• Gunay, S., Alt?nkeski, B., Çevik, E., & Goodell, J., 2023. Quantifying systemic risk in the cryptocurrency market: A sectoral analysis. Finance Research Letters. https://doi.org/10.1016/j.frl.2023.104586
• Fang, S., Cao, G., & Egan, P., 2023. Forecasting and backtesting systemic risk in the cryptocurrency market. Finance Research Letters. https://doi.org/10.1016/j.frl.2023.103788
• Tzeng, K., & Su, Y., 2024. Can U.S. macroeconomic indicators forecast cryptocurrency volatility?. The North American Journal of Economics and Finance. https://doi.org/10.1016/j.najef.2024.102224
• Rahman, M., Naeem, M., Yarovaya, L., & Mohapatra, S., 2024. Unravelling systemic risk commonality across cryptocurrency groups. Finance Research Letters. https://doi.org/10.1016/j.frl.2024.105633
• Huang, Y., Wang, H., Chen, Z., Feng, C., Zhu, K., Yang, X., & Yang, W., 2024. Evaluating Cryptocurrency Market Risk on the Blockchain: An Empirical Study Using the ARMA-GARCH-VaR Model. IEEE Open Journal of the Computer Society, 5, pp. 83-94. https://doi.org/10.1109/OJCS.2024.3370603
• Farroukh, A., Metzger, M., & Mzoughi, H., 2024. Assessing the influence of cryptocurrencies on financial market stability. Eurasian Economic Review. https://doi.org/10.1007/s40822-024-00284-w