Executive Summary

Central bank decisions are among the most closely watched events in global financial markets. For emerging market currencies, changes in developed market interest rates can influence capital flows, funding conditions, risk appetite and the relative attractiveness of domestic assets.

This study asks a simple question:

Do larger G10 policy rate changes systematically lead to higher emerging market currency volatility?

Using 206 G10 policy rate changes between November 2016 and July 2026, I examine five day realised volatility across six emerging market currencies: the Brazilian real, Mexican peso, South African rand, Indian rupee, South Korean won and Thai baht.

The results are less straightforward than the conventional intuition might suggest.

Across both the aggregate EM FX basket and the six individual currencies, the size of the observed policy rate change does not robustly predict subsequent five day currency volatility.

Broader global financial conditions appear more informative. In the full sample, VIX has strong positive relationships with volatility in BRL, MXN and KRW, even after correcting for multiple hypothesis testing.

However, several of these relationships weaken substantially when the extreme COVID period is removed.

The evidence therefore suggests that:

The market environment surrounding a policy decision may matter more for EM currency volatility than the absolute size of the rate change itself.

One important limitation is that the analysis uses actual policy changes rather than the unexpected component of each decision. Markets often price expectations in advance, so a widely anticipated 25 basis point increase may contain far less new information than an unexpected decision.

1. Research Question

The central research question is:

How are G10 policy rate changes related to short term volatility in emerging market currencies?

Four initial hypotheses were considered:

  1. Larger G10 rate changes would be associated with higher EM FX volatility.
  2. Federal Reserve decisions would have a stronger relationship with EM FX volatility than equivalent changes by other G10 central banks.
  3. The relationship between policy changes and EM FX volatility would strengthen during periods of elevated global risk aversion.
  4. Sensitivity would differ substantially across individual emerging market currencies.

2. Data

G10 monetary policy

Policy rate data are drawn from the Bank for International Settlements Central Bank Policy Rates dataset.

The analysis covers:

  • Federal Reserve
  • European Central Bank
  • Bank of England
  • Bank of Japan
  • Swiss National Bank
  • Bank of Canada
  • Reserve Bank of Australia
  • Reserve Bank of New Zealand
  • Riksbank
  • Norges Bank

An event is identified whenever the daily BIS policy rate series changes.

The final sample contains:

Emerging market currencies

Daily exchange rate data come from Federal Reserve H.10 series distributed through FRED.

The six currencies are:

  • BRL, Brazilian real
  • MXN, Mexican peso
  • ZAR, South African rand
  • INR, Indian rupee
  • KRW, South Korean won
  • THB, Thai baht

For each policy event, I calculate annualised realised volatility using the first five available trading day returns following the event.

I also calculate a 20 trading day measure of volatility before each event.

Global financial controls

The regressions incorporate:

  • VIX
  • the broad trade weighted US dollar index
  • the US 10 year Treasury yield
  • volatility before the policy event
  • whether the decision was a hike or a cut
  • whether the event came from the Federal Reserve

3. Methodology

The core empirical framework estimates EM FX volatility following each policy decision as a function of the size of the rate change and broader global financial conditions.

A representative specification is:

\[ Volatility_{t+5} = \alpha + \beta_1 RateChange + \beta_2 Fed + \beta_3 VIX + \beta_4 RateChange \times Fed + \beta_5 RateChange \times VIX + Controls + \epsilon \]

The policy rate change and VIX variables are centred around their respective means before constructing interaction terms.

The regressions use HC1 robust standard errors to reduce sensitivity to heteroskedasticity.

Separate regressions are also estimated for each of the six currencies.

Because several hypotheses are tested across multiple currencies, Benjamini Hochberg false discovery rate corrections are applied to reduce the risk of treating chance results as statistically meaningful.

4. Larger Rate Moves Show Only a Weak Relationship With EM FX Volatility

The raw data show only a modest positive relationship between the absolute size of a G10 rate change and subsequent EM FX volatility.

Most observations are concentrated around 25 and 50 basis point decisions, yet there is substantial variation in currency volatility even among events of the same size.

More importantly, once broader financial conditions are controlled for, the size of the policy move is not statistically significant.

The same result appears when the six currencies are analysed individually.

This suggests that the numerical size of a policy decision is not necessarily a good measure of the amount of new information entering financial markets.

A 25 basis point increase that was fully anticipated may cause less disruption than an unexpected hold, cut or change in forward guidance.

5. Global Risk Conditions Matter More in the Full Sample

The relationship between VIX and EM FX volatility is considerably stronger in the full sample.

Higher VIX levels are associated with greater volatility following G10 policy events.

This is economically intuitive. During periods of higher global risk aversion, investors may reduce exposure to riskier assets, unwind carry trades, increase demand for dollar liquidity or shift capital towards perceived safe havens.

Each of these mechanisms can affect emerging market currencies regardless of whether the underlying policy move was 25 or 50 basis points.

However, the extreme observations visible during periods of market stress raise an important question.

Is this relationship stable across different market environments, or is it being driven by crisis periods?

6. Currency Sensitivity Is Highly Uneven

The six currencies respond very differently to changes in global risk conditions.

In the full sample, the strongest positive VIX coefficients are observed for:

The strongest positive VIX relationships are observed for BRL, MXN and KRW. These relationships remain statistically significant after correcting for multiple hypothesis testing.

This variation is important because emerging markets should not be treated as a single homogeneous asset class.

Currency behaviour can differ because of factors including:

  • external financing requirements
  • commodity exposure
  • domestic interest rate structures
  • foreign exchange reserve adequacy
  • trade relationships
  • investor positioning
  • market liquidity
  • sensitivity to global dollar funding conditions

The same global shock can therefore affect Brazil, Mexico, South Korea and Thailand in very different ways.

7. Crisis Conditions Matter

The most important robustness exercise removes observations between March 2020 and June 2021.

This period contained an extraordinary global shock, emergency monetary policy responses and unusually high market volatility.

Once these observations are excluded, several of the full sample VIX relationships weaken materially.

The interaction between the size of the rate change and VIX also loses statistical significance.

After applying the multiple testing correction, none of the individual currency results in the reduced sample remains statistically significant at the conventional 5% level.

This changes how the full sample results should be interpreted.

The evidence does not support the claim that VIX has an equally strong and stable relationship with EM FX volatility across normal and crisis conditions.

Instead, periods of severe global stress appear to account for a meaningful share of the relationship observed in the full sample.

8. The Federal Reserve Result

Another initial hypothesis was that Federal Reserve decisions would have a disproportionately large relationship with emerging market currency volatility.

The results provide little evidence that the size of the Fed’s rate change has a stronger effect than equivalent changes by other G10 central banks.

Across both the aggregate model and the individual currency models, the interaction between the size of the policy move and a Federal Reserve event is generally statistically insignificant.

This does not imply that the Federal Reserve is unimportant to emerging markets.

The dollar, US interest rates and expectations about Federal Reserve policy remain central to global asset pricing.

The result is narrower. It suggests that the absolute size of the observed Fed policy change is not, on its own, a reliable measure of the shock reaching emerging market currencies.

9. Why Monetary Policy Surprises May Matter More

The main limitation of this analysis also points towards the most natural extension.

Suppose markets expect the Federal Reserve to raise rates by 25 basis points and the Fed delivers exactly 25 basis points.

The dataset records a 25 basis point increase.

But if the decision was completely expected, the amount of genuinely new information may be very small.

Now consider a second meeting where investors again expect a 25 basis point increase, but the Fed unexpectedly raises rates by 50 basis points.

The two meetings contain very different amounts of new information.

Financial markets therefore react not only to:

What did the central bank do?

but also to:

What did the central bank do relative to what investors had already priced?

A stronger extension of this project would therefore replace actual rate changes with measures of monetary policy surprises around announcement windows.

This could potentially distinguish between:

  • target rate surprises
  • changes in forward guidance
  • balance sheet surprises
  • quantitative easing or tightening signals

10. Model Fit Across Individual Currencies

The explanatory power of the model differs considerably across currencies.

The highest full sample explanatory power is observed for MXN, followed by BRL and INR, while the models explain considerably less variation in some of the other currencies.

This again reinforces the conclusion that emerging market currencies should not be treated as one uniform group.

Different currencies respond to global financial conditions through different economic and market channels.

11. Limitations

Several limitations should be emphasised.

First, policy changes are not policy surprises.
The model does not directly observe what markets expected immediately before each decision.

Second, the BIS series records effective policy rate changes.
The effective date is not necessarily identical to the precise announcement time.

Third, monetary policy is endogenous.
Central banks adjust rates in response to economic conditions that may themselves affect currency volatility.

Fourth, crisis periods are influential.
The COVID robustness exercise shows that extreme observations have a material effect on several results.

Fifth, the event window is deliberately short.
Five day realised volatility captures immediate market behaviour but may miss slower transmission channels.

Sixth, the analysis covers six emerging market currencies.
The findings should not automatically be generalised to the entire emerging market universe.

12. Conclusion

This project began with an intuitive hypothesis: larger G10 monetary policy changes should generate greater volatility across emerging market currencies.

The data provide little robust evidence for that proposition.

Across 206 G10 policy changes between 2016 and 2026, the size of the observed rate move does not consistently predict five day EM FX volatility, either at the aggregate level or across the six individual currencies.

Broader global risk conditions show stronger relationships in the full sample, particularly for BRL, MXN and KRW. However, several of these effects weaken materially once the extreme COVID period is excluded.

The central conclusion is therefore more nuanced:

For emerging market currencies, the environment surrounding a central bank decision appears more informative than the absolute size of the rate change itself.

For investors, this suggests that analysing monetary policy requires more than simply tracking whether a central bank moved by 25, 50 or 75 basis points.

The more useful questions may be:

  • What had markets already priced?
  • How surprising was the decision?
  • What is happening to global risk appetite?
  • What is happening to dollar funding conditions?
  • Which currencies are most exposed to those conditions?

The natural next stage of the research is therefore to move from policy changes to policy surprises.