Assessing the Impact of Stablecoins on Exchange Rate Volatility: Evidence from Emerging Market Economies (PDF File, 1.4 MB) (2026-02-24)

This document is a Research Memorandum from the Hong Kong Monetary Authority (HKMA) titled "ASSESSING THE IMPACT OF STABLECOINS ON EXCHANGE RATE VOLATILITY: EVIDENCE FROM EMERGING MARKET ECONOMIES". Published on 24 February 2026, it investigates the relationship between the increasing use of stablecoins, particularly USD-pegged stablecoins, and the exchange rate volatility experienced by emerging market economies (EMEs).

This research memorandum aims to provide an empirical analysis of the impact of stablecoin transactions, specifically USD Tether (USDT), on the exchange rate volatility of 12 emerging market economies (EMEs). The study highlights the rapid growth of the stablecoin market and its unique appeal to investors in EMEs due to easier access to USD-linked assets without prior conversion to USD. The core purpose is to quantify this impact and suggest potential policy considerations to mitigate adverse effects on exchange rate stability.

The memorandum begins by defining stablecoins as cryptocurrencies designed to maintain a stable value, primarily pegged to fiat currencies like the USD. It notes the dominance of USD-pegged stablecoins, accounting for approximately 99% of the market capitalization, with USDT being the largest at around 60% and USDC at 25% as of November 2025.

A key feature of the stablecoin market is the ability for users to purchase USD stablecoins using fiat currencies other than the USD. This facility is particularly attractive to investors in EMEs, where financial markets might be less developed and access to foreign assets can be constrained. This leads to substantial transaction flows between EME currencies and USD stablecoins.

The research posits that these flows, while facilitating payments and investments, ultimately involve conversions between EME currencies and the USD in the foreign exchange (FX) market. Consequently, increased FX activity driven by stablecoin transactions can lead to greater exchange rate volatility in EME currencies.

The study focuses on USD Tether (USDT) transactions against 12 EME currencies. The empirical findings indicate two primary conclusions:

1. Transaction Flows and Volatility: EME currencies with stronger transaction flows vis-à-vis USDT exhibit increased exchange rate volatility. Specifically, a one-standard-deviation increase in transaction flows is associated with a median increase in historical volatility of around 3.6% for EME currencies with high USDT flows, compared to only 0.35% for those with low flows.
2. Stablecoin Price Instability and Volatility: Instability in stablecoin prices (deviations from their peg) induces additional exchange rate volatility in EME currencies that are more exposed to stablecoins. This suggests that stress within stablecoin markets can transmit to FX markets.

The research concludes that as stablecoin adoption grows in EMEs, USD stablecoin transactions vis-à-vis EME currencies represent a significant channel that can affect exchange rate volatility. The authors recommend policy measures such as capital adequacy and reserve liquidity requirements for stablecoins to reduce their price instability and, consequently, dampen the impact of stablecoin adoption on exchange rate volatility.

This research document does not introduce new policies or requirements directly from the HKMA. Instead, it presents research findings and policy recommendations based on empirical analysis. The "new policies" are suggestions for future regulatory considerations by policymakers.

The key proposed policy considerations, derived from the findings, include:

• Measures to reduce the price instability of stablecoins: This is identified as a crucial step to dampen the impact on exchange rate volatility.
• Capital adequacy requirements for stablecoin issuers: To ensure financial resilience.
• Reserve liquidity requirements for stablecoin issuers: To ensure stablecoins can be redeemed efficiently.

• Data Collection Period: The empirical analysis uses monthly observations from February 2021 to December 2024.
• Reference Dates for Market Data: The document refers to market data and stablecoin capitalization figures as of November 2025.
• Structural Break Detection: A structural break in the USDT price deviation time series was found between January and February 2021, which influenced the start date of the empirical analysis.
• Anecdotal Evidence: U.S. banking turmoil mentioned occurred in March 2023. The collapse of Terra (UST) is noted as occurring in May 2022.

The primary impact scope of this research and its suggested policy implications is on:

• Emerging Market Economies (EMEs): The study specifically focuses on the FX implications for these economies.
• Central Banks and Financial Regulators in EMEs: They are the primary audience for the findings and recommendations, as they manage exchange rate stability and financial stability.
• Stablecoin Issuers and Operators: The suggested policy measures (capital adequacy, reserve liquidity) directly affect their operations.
• Cryptocurrency Exchanges and Market Makers: Especially those operating in or facilitating transactions with EME currencies.
• Investors and Traders in EMEs: Who utilize stablecoins for payments and investments.

The degree of impact is expected to be significant for EMEs experiencing substantial transaction flows with USD stablecoins. The research highlights a quantifiable linkage between stablecoin activity and exchange rate volatility, suggesting a need for proactive policy responses.

This research memorandum does not impose immediate compliance requirements on financial institutions. It is a research paper providing analysis and recommendations. However, the suggested policy measures, if implemented by regulators, would lead to new compliance obligations for stablecoin issuers, such as:

• Meeting Capital Adequacy Ratios: Demonstrating sufficient capital to absorb potential losses.
• Maintaining Specific Reserve Liquidity Levels: Ensuring immediate convertibility of stablecoins into fiat currency.
• Enhanced Reporting: Potentially requiring more detailed reporting on reserve composition, asset management, and transaction volumes.
• Adherence to New Regulatory Frameworks: Depending on the specific policies adopted, stablecoin issuers might need to obtain licenses or operate under specific regulatory frameworks.

For financial institutions in EMEs, compliance would relate to how they interact with or facilitate stablecoin transactions, potentially requiring due diligence on stablecoin partners or adherence to guidelines on providing services related to crypto assets.

• Stablecoins: Cryptocurrencies designed to maintain a stable value, primarily pegged to fiat currencies.
• USD Stablecoins: Stablecoins pegged to the US dollar.
• Emerging Market Economies (EMEs): Countries with developing financial markets and potential constraints on foreign asset access.
• USDT (USD Tether): The largest USD-linked stablecoin, accounting for approximately 60% of total stablecoin market capitalization as of November 2025.
• USDC (USD Coin): The second-largest USD-linked stablecoin, representing around 25% of market capitalization as of November 2025.
• Foreign Exchange (FX) Market: The market where currencies are traded.
• Exchange Rate Volatility: The degree of fluctuation in the exchange rate of one currency against another. Measured in the study as the standard deviation of daily exchange rate returns for a given month.
• Transaction Flows: The volume of money transferred through stablecoin transactions.
• USDT Transaction Flows vis-à-vis EME Currencies: The volume of USDT transactions conducted using the fiat currencies of EME countries.
• Price Instability of Stablecoins: Deviations of a stablecoin's market price from its intended peg. Measured by the monthly average of the daily absolute deviation of USDT's price from its 1 USD peg value, in basis points.
• Panel Regression Analysis: A statistical method used to analyze data that has both cross-sectional (across different currencies) and time-series (over time) dimensions.
• Model Variables:
• Dependent Variable: $FX\_vol_{i,t}$ (Monthly realized exchange rate volatility for currency $i$ in month $t$).
• Key Independent Variable: $USDDT\_flow_{i,t-1}^{detrended}$ (Lagged and de-trended USDT transaction flows vis-à-vis currency $i$, measured in billions of USD). This variable is detrended by subtracting the 12-month trailing average.
• Control Variable: $BTC\_flow_{i,t-1}^{detrended}$ (Lagged and de-trended Bitcoin transaction flows vis-à-vis currency $i$).
• Stablecoin Price Instability Variable: $USDDT\_deviation_t$ (Monthly average of the daily absolute deviation of USDT's price from its peg).
• Exposure Dummy: $ExpCur_{i,t}$ (Dummy variable: 1 if currency $i$'s cumulative historical average USDT transaction flows exceed the sample median in month $t$, 0 otherwise).
• Interaction Term: $USDDT\_deviation_t * ExpCur_{i,t}$ (To test if price instability has a stronger impact on exposed currencies).
• Fixed Effects: Month-fixed effects ($FE_t$) and currency-quarter fixed effects ($FE_{i,q}$).
• Monthly Common Factors (in Equation 2): Log changes in the USD Index, log changes in the VIX Index, and bitcoin return volatility.
• Monthly Currency-Specific Controls ($Control_{i,t}$): Inflation differential with the U.S., capital flows (equity market fund flows), currency's bid-ask spread, and FX market intervention by authorities.
• Key Findings Quantified:
• A one-standard-deviation increase in transaction flows leads to a median increase in EME currency volatility of 3.6% of historical volatility for high-flow currencies, vs. 0.35% for low-flow currencies.
• The coefficient on $USDDT\_flow_{i,t-1}^{detrended}$ in the baseline model (Column 1, Table 1) is 0.13*.
• The coefficient on the interaction term $USDDT\_deviation_t * ExpCur_{i,t}$ (Column 2, Table 1) is 3.55*, indicating that for exposed currencies, price instability significantly amplifies exchange rate volatility.
• The sum of coefficients for $USDDT\_deviation_t$ and its interaction term (total effect on exposed currencies) is 3.56*.
• Data Sources: CryptoCompare (transaction volumes), Coingecko (stablecoin prices), Bloomberg and Reuters (exchange rate data), International Monetary Fund (inflation), EPFR (capital flows), and Adler et al. (2021) (FX intervention).
• Sample: 12 EME currencies: Argentine peso, Brazilian real, Colombian peso, Indonesian rupiah, Nigerian naira, Polish złoty, Romanian leu, Russian ruble, Thailand baht, Turkish lira, Ukrainian hryvnia, and South African rand.
• Data Period for Empirical Analysis: February 2021 – December 2024.

• Chart 1: The market capitalisation of stablecoins: Depicts the market capitalization of fiat currency-pegged stablecoins, showing the dominance of USD-pegged stablecoins and the growth trend.
• Chart 2: Illustration of transactions between USD stablecoins and non-USD local currencies: A schematic diagram explaining how direct purchases of USD stablecoins using EME fiat currencies necessitate FX conversion and can thus induce exchange rate volatility.
• Chart 3: USDT transaction flows vis-à-vis EME currencies: Shows the total monthly USDT transaction flows vis-à-vis sampled EME currencies in USD billion and as a share of all reported currencies, highlighting that EME flows constitute a notable portion (around 20%) of total USDT flows.
• Chart 4: The price instability of USDT and aggregate USDT transaction flows vis-à-vis sampled EME currencies: A scatter plot showing the correlation between USDT price instability and aggregate transaction flows. It demonstrates a stronger positive relationship in the latter period (April 2023 – July 2025, correlation coefficient 0.7) compared to the earlier period (January 2021 – March 2023, correlation coefficient 0.18).
• Chart 5: Exchange rate volatility of EME currencies when USDT significantly deviates from its peg value (i.e., 1 USD): Compares the median monthly exchange rate volatility for EME currencies grouped by their historical transaction flows to USDT during periods of significant USDT price deviation. It shows higher volatility for currencies with high transaction flows.
• Table 1: Estimation relationships between exchange rate volatility, USDT transaction flows, and the price instability of USDT: Presents the results of the panel regression analyses. It details the coefficients for USDT flows, USDT price deviation, the interaction term, and other control variables across four model specifications.
• Table A1: Definition and data source of variables: Provides a clear definition and the source for each variable used in the empirical analysis.
• Table A2: Summary statistics: Presents descriptive statistics (observations, mean, median, standard deviation) for all variables used in the study.