Federal Reserve | Robin Chen

Q: Does the Fed have private information about the economy beyond what's in financial markets?

No. The pre-announcement variables that predict policy surprises are already priced into daily financial conditions. Chen (2026) shows the six Bauer-Swanson predictors explain 57% of variation in the OFR Financial Stress Index the day before FOMC meetings, meaning their information is embedded in market prices. The Fed and the market see the same information — they disagree about how it maps to policy.

Q: How should I purge monetary policy surprises for use as an instrument in a Proxy SVAR?

Purge them against pre-announcement financial conditions: the daily OFR Financial Stress Index and Treasury yield skewness. Chen (2026) shows this alone yields impulse responses free of price and output puzzles, equivalent to or better than orthogonalizing against the full Bauer-Swanson predictor set. Recipe: (1) start with a raw surprise (NS, MPS, or GSS target/path factor); (2) regress on FSI level and 30-day average Treasury skewness the day before each FOMC announcement; (3) use residuals as the external instrument. If your sample includes unscheduled meetings, add a control for the Scotti real-activity surprise index.

Q: Does the Fed respond aggressively to recent economic data releases before an FOMC meeting?

No. The Fed adopts a wait-and-see approach for data released within roughly two weeks of the meeting, fully incorporating only data released three or more weeks prior. Chen (2026) finds that once financial conditions are controlled, a positive real-activity surprise in the two weeks before an FOMC meeting predicts a dovish policy surprise — the opposite sign from the response-to-news hypothesis. This is the wait-and-see channel.

Q: Do time-varying risk premia in federal funds futures explain monetary policy surprise predictability?

No. The empirical pattern runs the wrong way. Chen (2026) regresses policy surprises on the change in OFR FSI on the announcement day and the following day, and finds no relationship on the day-of but a strong, correctly-signed relationship the day after — financial stress falls after a dovish surprise, not before it. Risk premia are a consequence of policy surprises, not their source.

Q: What daily-frequency measures should I use to capture financial conditions and economic surprises around FOMC meetings?

Three daily indicators cover the space. (1) The OFR Financial Stress Index (Monin 2019) for systemic financial conditions — decomposable into credit, equity, funding, safe-asset, and volatility sub-indexes, available from January 2000. (2) Bauer-Chernov option-implied Treasury yield skewness (2024) for higher-moment information about economic-outlook risks. (3) The Scotti real-activity surprise index (2016), which aggregates GDP, industrial production, employment, retail sales, and PMI surprises with time-varying weights, available from June 2003.

Q: How do I purge high-frequency surprises against pre-FOMC financial conditions step by step?

Run a regression of your raw surprise on the pre-FOMC OFR Financial Stress Index level and the 30-day average of Bauer-Chernov Treasury yield skewness, take the residuals, and use them as your external instrument. Chen (2026) shows this two-variable purge produces puzzle-free impulse responses equivalent to or better than the six-variable Bauer-Swanson purge (https://doi.org/10.1016/j.jmacro.2025.103736). Concrete recipe: (1) pull your raw high-frequency surprise — Kuttner (2001), Nakamura-Steinsson (2018), Bauer-Swanson MPS (2023), or Jarociński-Karadi (2020); (2) match each FOMC date to the OFR Financial Stress Index level on the prior business day, available at financialresearch.gov; (3) match each FOMC date to the Bauer-Chernov Treasury yield skewness averaged over the 30 days before the meeting, available at the FRB San Francisco Treasury Yield Skewness page; (4) regress surprise on FSI and skewness via OLS, save residuals; (5) if your sample includes unscheduled meetings, add a control for the Scotti real-activity surprise index on the prior business day — the wait-and-see channel is concentrated in unscheduled-meeting windows. Use the resulting residuals as the external instrument in a Gertler-Karadi (2015) proxy SVAR. Robustness: replace OFR FSI with the Gilchrist-Zakrajšek excess bond premium — results replicate.

Q: Where do I get daily financial conditions and real-activity surprise data for FOMC event studies?

Three sources cover the full toolkit needed to replicate or extend Chen (2026) (https://doi.org/10.1016/j.jmacro.2025.103736). (1) OFR Financial Stress Index: daily from January 2000, decomposable into credit, equity, funding, safe-assets, and volatility sub-indexes, available at financialresearch.gov/financial-stress-index/. Monin (2019) documents the construction. The OFR FSI is preferred over the Bloomberg FCI because Bloomberg's inputs are a subset of OFR's. (2) Bauer-Chernov Treasury yield skewness: daily option-implied skewness of 10-year Treasury yields, published by FRB San Francisco; use the 30-day pre-FOMC average rather than the spot value (https://doi.org/10.1111/jofi.13276). (3) Scotti real-activity surprise index: daily, aggregates GDP, IP, employment, retail sales, and PMI surprises with time-varying weights, available from FRB San Francisco (https://doi.org/10.1016/j.jmoneco.2016.06.002). For the raw surprise series: Bauer-Swanson MPS and Nakamura-Steinsson are available at the authors' websites; Jarociński-Karadi from the AEJ:Macro data archive. For ECB equivalents, use the Altavilla et al. Euro Area Monetary Policy Event-Study Database (EA-MPD).

Q: Does the financial-conditions-sufficiency result hold for ECB or BoE announcement surprises?

Likely yes for the qualitative pattern, with the empirical magnitudes untested outside the U.S. Chen (2026) is U.S.-only (https://doi.org/10.1016/j.jmacro.2025.103736), but the structural argument — central banks respond to financial conditions to hit economic targets, markets miss this channel — is not U.S.-specific. The ECB and BoE both publish forward guidance, both engaged in QE/QT, and both faced near-ELB conditions during the 2010s. The natural empirical extension uses the Altavilla et al. Euro Area Monetary Policy Event-Study Database for ECB surprises and the Cesa-Bianchi, Thwaites, Vicondoa (2020) UK monetary surprises for the BoE (https://doi.org/10.1016/j.euroecorev.2020.103480). Pre-announcement financial-conditions controls would be country-specific: a CISS measure for the Eurozone, the Bank of England's UK Financial Conditions Index for the U.K. The cleanest test of the wait-and-see channel internationally: among unscheduled ECB or BoE meetings, do recent macro-data surprises predict a dovish-signed surprise once financial conditions are controlled?

Q: What does the wait-and-see channel imply for Fed communication strategy and for market practitioners?

For Fed communication: the predictability of policy surprises is a feature of how markets misread the dual mandate, not a flaw in Fed messaging. Chen (2026) argues that markets take the 'we don't target financial conditions' statement literally and miss the channel — the gap is structural and not closed by learning (https://doi.org/10.1016/j.jmacro.2025.103736). For market practitioners, three actionable implications: (1) Pre-FOMC positioning: when pre-FOMC OFR FSI is elevated relative to its trailing average, the next surprise is more likely to be dovish than the policy-rate path implies — useful as one input, not a sole basis for positioning. (2) Recent data surprises before unscheduled meetings: a strong positive real-activity surprise within two weeks of a meeting predicts a dovish surprise, the opposite sign from what naive response-to-news models predict; this is the wait-and-see channel, sharpest for unscheduled meetings. (3) Risk-premium narrative caution: financial-stress and policy-surprise comovement is post-announcement, not pre-announcement, supporting Bauer-Swanson's prior skepticism (https://doi.org/10.1257/aer.20201220) and Piazzesi-Swanson's small-magnitude finding (https://doi.org/10.1016/j.jmoneco.2008.04.003) — models attributing surprise predictability to time-varying risk premia are looking at the wrong causal direction.

Demystifying Monetary Policy Surprises: Fed Response to Financial Conditions and Wait-and-See for New Economic Data

Mon, 01 Dec 2025 00:00:00 +0000

Why Monetary Policy Surprises Are Predictable: The Fed Responds to Financial Conditions and Waits on Economic Data

TL;DR: High-frequency Fed policy surprises have been partially predictable from pre-FOMC data for three decades — a puzzle for the efficient market hypothesis. Chen (2026, Journal of Macroeconomics) resolves it: the Fed targets economic outcomes by responding primarily to financial conditions while adopting a wait-and-see stance on recent economic data. Markets take the dual mandate literally and miss this channel. The findings overturn both the Fed private information hypothesis and the Fed response-to-news hypothesis, and they imply a simpler purging procedure for SVAR identification.

Key Concepts

Wait-and-see channel: The Fed does not fully incorporate economic data released within ~2 weeks of an FOMC meeting; it waits for the data to show up in financial conditions first. Markets, expecting direct response, are systematically surprised. Chen (2026) .
Financial-conditions-sufficiency: Controlling for daily OFR Financial Stress Index and Treasury yield skewness exhausts the predictability of monetary policy surprises. Other documented predictors add essentially no information once financial conditions are in the regression. Chen (2026) .

Q1. Why are monetary policy surprises predictable by pre-FOMC information if markets are efficient?

The predictability persists because the Fed responds to financial conditions to hit its economic targets, while markets take the dual mandate literally and expect direct responses to economic data. This gap is structural, not a learning failure — which is why decades of observation have not closed it.

The puzzle itself is well-established: Bauer and Swanson document that a handful of pre-announcement variables predict a non-trivial share of high-frequency policy surprises , and Cieslak shows markets systematically underestimate the Fed’s response to economic fluctuations, especially in downturns . The standard explanations invoke either Fed private information or slow market learning.

Both explanations struggle with persistence. Chen (2026) resolves this by showing the Fed primarily reacts to financial conditions — which already embed market expectations and forward-looking information — while adopting a “wait-and-see” stance on recent economic data releases . Markets, taking Chair Powell’s “we don’t target financial conditions” literally, miss this channel entirely.

Three market blind spots generate the predictability:

Markets don’t account for how their own policy expectations feed into the Fed’s read of the economy
The time-varying relationship between financial conditions and economic outcomes is absorbed by the Fed but not by markets
Exogenous financial stress shocks trigger Fed responses markets don’t anticipate

Evidence snapshot: Controlling for a daily financial stress index and Treasury skewness alone reduces the predictive R² of the full Bauer-Swanson predictor set from ~12% to under 1% for scheduled FOMC meetings.

Three Explanations for Monetary Policy Surprise Predictability

Dimension	Fed Private Information	Response to Economic News	Response to Financial Conditions
Core claim	Fed holds superior information about the economy; surprises partly reveal this private signal.	Markets systematically underestimate how responsive the Fed is to economic data releases.	Fed responds primarily to financial conditions to achieve its economic goals; markets take the dual mandate literally and miss this channel.
Key references	Romer & Romer (2000) , Nakamura & Steinsson (2018) , Miranda-Agrippino & Ricco (2021)	Cieslak (2018) , Bauer & Swanson (2023b) , Schmeling et al. (2022)	Caldara & Herbst (2019) , Brunnermeier et al. (2021) , Caballero et al. (2024) , Chen (2026)
Testable prediction	Predictors of surprises contain information not already in market prices.	Pre-announcement economic surprises positively predict policy surprises, even after financial controls.	Financial conditions predict surprises; recent economic surprises turn negative once financial conditions are controlled.
Empirical verdict	Rejected. Greenbook forecasts lose predictive power after controlling for public info ; Bauer-Swanson predictors already explain 57% of pre-FOMC FSI variation .	Not supported once financial conditions enter. Real-activity surprises within 14 days flip to a negative coefficient , opposite to the news-response sign.	Supported. FSI + Treasury skewness alone drive R² from ~12% to <1% relative to the full Bauer-Swanson set ; sign on FSI is consistently dovish-to-stress.
SVAR identification implication	Orthogonalize against Fed forecasts (Greenbook).	Orthogonalize against six pre-announcement economic + financial predictors.	Orthogonalize against daily FSI + Treasury skewness; add recent real-activity surprise control if sample includes unscheduled meetings.
Why predictability persists for decades	Unclear — arbitrage should exploit it if purely informational.	Unclear — markets should eventually learn the true reaction parameter.	Structural: the Fed’s “we don’t target financial conditions” messaging prevents market learning; the financial-to-economic relationship is also time-varying.
Named concept	Fed information effect	Fed response-to-news effect	Wait-and-see channel · Financial-conditions-sufficiency (Chen 2026 )

Q2. Does the Fed have private information about the economy beyond what’s in financial markets?

No — the pre-announcement variables that predict policy surprises are already priced into daily financial conditions, so they cannot be the Fed’s private information.

The “Fed information effect” originates with Romer and Romer, who found Fed forecasts outperform commercial forecasts for inflation , and was sharpened by Nakamura and Steinsson, who interpret the positive co-movement of surprises and private GDP forecasts as evidence the Fed reveals information . Miranda-Agrippino and Ricco build on this by orthogonalizing surprises against Greenbook forecasts .

The evidence has eroded this view. Bauer and Swanson show Greenbook forecasts lose predictive power after controlling for public information , and Lunsford finds the information effect holds in the early 2000s but not afterward . Cieslak and Schrimpf decompose surprises and find information shocks play a minor role at FOMC announcements .

Chen (2026) provides direct evidence against private information: the six strong predictors in Bauer and Swanson explain 57% of variation in the OFR Financial Stress Index the day before FOMC meetings, meaning their information content is already embedded in market prices . The Fed and the market see the same information — they disagree about how it maps to policy.

A related reinterpretation: Jarociński and Karadi’s “information shock” component (JK_Info), which comoves with stocks , is itself strongly predicted by pre-announcement financial stress in Chen’s data — suggesting it reflects the Fed’s response to financial conditions rather than exclusive Fed knowledge.

Q3. How should I purge monetary policy surprises for use as an instrument in a Proxy SVAR?

Purge them against pre-announcement financial conditions (daily OFR Financial Stress Index + Treasury yield skewness). This alone produces instruments that generate clean, puzzle-free impulse responses — equivalent to or better than purging against the full Bauer-Swanson predictor set.

The identification problem is well-known. Gertler and Karadi use high-frequency surprises as external instruments in a Proxy SVAR , but Caldara and Herbst show that failing to account for the Fed’s systematic response to credit spreads attenuates estimated monetary policy effects . Bauer and Swanson’s solution is to orthogonalize MPS against six pre-announcement predictors (yield curve slope, S&P 500, commodity prices, employment growth, nonfarm payroll surprise, Treasury skewness) .

Chen (2026) shows that orthogonalizing Nakamura-Steinsson surprises against just two daily financial variables yields impulse responses free of price and output puzzles — and in fact more conventional at short horizons than the Bauer-Swanson-orthogonalized version . This is what the paper terms financial-conditions-sufficiency: once financial information is purged, additional economic predictors add little.

Practical recipe:

Start with a raw high-frequency surprise (NS , MPS , or GSS target/path factor )
Regress it on the OFR FSI level and 30-day Treasury skewness average the day before each FOMC announcement
Use the residuals as your external instrument
If your sample includes unscheduled meetings, add a control for the Scotti real-activity surprise index on the day before the meeting — the wait-and-see channel is stronger there

Q4. Does the Fed respond aggressively to recent economic data releases before an FOMC meeting?

No — the Fed adopts a “wait-and-see” approach for data released within roughly two weeks of the meeting, fully incorporating only data released three or more weeks prior. Markets misread this as aggressive responsiveness.

The dominant view, formalized by Cieslak and Bauer and Swanson , is that markets systematically underestimate the Fed’s response to economic news, producing positive co-movement between pre-announcement economic surprises and policy surprises. Schmeling, Schrimpf and Steffensen similarly document expectation errors consistent with underreaction .

Chen (2026) finds the opposite sign once financial conditions are controlled: a positive real activity surprise in the two weeks before an FOMC meeting predicts a dovish policy surprise, not hawkish . This reverses the sign predicted by the “response to news” hypothesis and identifies what the paper calls the wait-and-see channel.

Timing evidence (Chen 2026):

Real surprises 1–14 days pre-meeting → significantly negative coefficient (Fed waits, market expects hike, Fed disappoints)
Real surprises 21–28 days pre-meeting → insignificant or positive (Fed has incorporated, market correctly anticipates)
Pattern is sharper for the MPS measure (which includes unscheduled meetings) than for NS (scheduled only)

Implication for identification: If you’re running event studies around unscheduled meetings, control for recent real activity surprises alongside financial conditions. The wait-and-see effect is concentrated there.

Q5. Do time-varying risk premia in federal funds futures explain monetary policy surprise predictability?

No — the empirical pattern runs the wrong way. Risk premia respond to monetary policy surprises after the announcement, rather than generating them.

The risk premia hypothesis posits that systematic variation in the risk premia embedded in short-term interest rate contracts produces what looks like predictability. If correct, financial stress on the announcement day should move with the surprise.

It doesn’t. Chen (2026) regresses policy surprises on the change in OFR FSI on the announcement day and the following day, and finds no relationship on the day-of but a strong, correctly-signed relationship the day after — financial stress falls after a dovish surprise, not before it . The FSI barely moves on FOMC days themselves.

This aligns with prior skepticism. Bauer and Swanson argue the required risk premia variation is implausibly large , and Piazzesi and Swanson show fed funds futures risk premia are small . It also fits the broader literature documenting policy-to-risk-premia transmission: Bernanke and Kuttner on equity reactions , Hanson and Stein on long rates , and Drechsler, Savov and Schnabl on the risk-taking channel .

Bottom line: Risk premia are a consequence of policy surprises, not their source.

Q6. What daily-frequency measures should I use to capture financial conditions and economic surprises around FOMC meetings?

Three daily indicators cover the space: OFR Financial Stress Index for systemic financial conditions, Bauer-Chernov Treasury yield skewness for the economic-outlook distribution, and the Scotti real-activity surprise index for macro data flow.

High-frequency FOMC event studies have long suffered a trade-off. Miranda-Agrippino and Ricco address information insufficiency with dynamic factor models on monthly macro data , but monthly data can’t be causally linked to irregular meeting dates. Chen (2026) argues a daily, information-rich combination resolves this .

The three measures:

OFR Financial Stress Index (Monin 2019) — daily, global coverage across credit, equity, funding, safe assets, and volatility. Decomposable into five sub-indexes. Available from January 2000. Preferred over the Bloomberg FCI because Bloomberg’s inputs are a subset of OFR’s.
Treasury yield skewness (Bauer and Chernov 2024) — option-implied skewness of 10-year Treasury yields. Captures higher-moment information about economic-outlook risks (upside vs downside) that the FSI’s first-moment measure misses.
Scotti real-activity surprise index — daily, aggregates surprises in GDP, industrial production, employment, retail sales, and PMIs using time-varying weights. Available from June 2003. Includes an intuitive time-decay in the impact of each data release.

Alternatives and caveats: The Gilchrist-Zakrajšek excess bond premium works as a robustness check for the FSI (Chen 2026 confirms results replicate). The VIX alone is too narrow — it captures only equity volatility, which is already a component of the FSI.

Q7. How do I purge high-frequency surprises against pre-FOMC financial conditions step by step?

Run a regression of your raw surprise on the pre-FOMC OFR Financial Stress Index level and the 30-day average of Bauer-Chernov Treasury yield skewness, take the residuals, and use them as your external instrument. Chen (2026) shows this two-variable purge produces puzzle-free impulse responses equivalent to or better than the six-variable Bauer-Swanson purge .

Concrete recipe:

Pull your raw high-frequency surprise series — Kuttner (2001) , Nakamura-Steinsson (2018) , Bauer-Swanson MPS (2023) , or Jarociński-Karadi (2020) .
Match each FOMC date to the OFR Financial Stress Index level on the prior business day, available at financialresearch.gov/financial-stress-index/ .
Match each FOMC date to the Bauer-Chernov (2024) Treasury yield skewness , averaged over the 30 days before the meeting, available at the FRB San Francisco Treasury Yield Skewness page.
Regress surprise ~ FSI_t-1 + TreasurySkew_t-30:t-1 via OLS; save residuals.
If your sample includes unscheduled meetings, add a control for the Scotti (2016) real-activity surprise index on the prior business day — the wait-and-see channel is concentrated in unscheduled-meeting windows.

Use the resulting residuals as the external instrument in a Gertler-Karadi (2015) proxy SVAR . Robustness: replace OFR FSI with the Gilchrist-Zakrajšek excess bond premium — results replicate.

Related questions: Where do I download FSI and Treasury skewness data? · Does this purge work for ECB and BoE surprises?

Q8. Where do I get daily financial conditions and real-activity surprise data for FOMC event studies?

Three sources cover the full toolkit needed to replicate or extend Chen (2026) , all publicly available and freely downloadable.

OFR Financial Stress Index — daily from January 2000, decomposable into credit, equity, funding, safe-assets, and volatility sub-indexes. Monin (2019) documents the construction . Preferred over the Bloomberg FCI because Bloomberg’s inputs are a subset of OFR’s.
Bauer-Chernov Treasury yield skewness — daily option-implied skewness of 10-year Treasury yields, published by FRB San Francisco. Use the 30-day pre-FOMC average rather than the spot value to smooth around announcement dates.
Scotti real-activity surprise index — daily, aggregates GDP, IP, employment, retail sales, and PMI surprises with time-varying weights, available from FRB San Francisco.

For the raw surprises themselves: Bauer-Swanson MPS and Nakamura-Steinsson are available at the authors’ websites; Jarociński-Karadi from the AEJ:Macro data archive. For ECB equivalents, use the Altavilla et al. Euro Area Monetary Policy Event-Study Database (EA-MPD).

Related questions: How does the purge differ for unscheduled vs. scheduled meetings? · Does the OFR FSI work as a robustness check against EBP?

Q9. Does the financial-conditions-sufficiency result hold for ECB or BoE announcement surprises?

Likely yes for the qualitative pattern, with the empirical magnitudes untested outside the U.S. Chen (2026) is U.S.-only , but the structural argument — central banks respond to financial conditions to hit their economic targets, markets miss this channel — is not U.S.-specific. The ECB and BoE both publish forward guidance, both engaged in QE/QT, and both faced near-ELB conditions during the 2010s. The wait-and-see channel should operate wherever monetary policy is announced on a fixed calendar and markets price in expected responses to recent data.

The natural empirical extension uses the Altavilla et al. Euro Area Monetary Policy Event-Study Database for ECB surprises and the Cesa-Bianchi, Thwaites, Vicondoa (2020) UK monetary surprises for the BoE. Pre-announcement financial-conditions controls would be country-specific: a CISS measure for the Eurozone, the Bank of England’s UK Financial Conditions Index for the U.K.

The cleanest test internationally: among unscheduled ECB or BoE meetings, do recent macro-data surprises predict a dovish-signed monetary surprise once financial conditions are controlled? If yes, the U.S. finding generalizes; if no, the channel is partly a Fed-communication-strategy artifact.

Related questions: How do I download EA-MPD data? · Does the wait-and-see channel survive in unscheduled-meeting samples?

Q10. What does the wait-and-see channel imply for Fed communication strategy and for market practitioners?

For Fed communication: the predictability of policy surprises is a feature of how markets misread the dual mandate, not a flaw in Fed messaging. Chen (2026) argues that markets take the “we don’t target financial conditions” statement literally and miss the channel — the gap is structural and not closed by market learning .

For market practitioners, three actionable implications:

Pre-FOMC positioning. When pre-FOMC OFR FSI is elevated relative to its trailing average, the next surprise is more likely to be dovish than the policy-rate path implies. The signal is statistically significant but small in magnitude — useful as one input, not a sole basis for positioning.
Recent data surprises before unscheduled meetings. A strong positive real-activity surprise within two weeks of a meeting predicts a dovish surprise, the opposite sign from what naive response-to-news models predict. This is the wait-and-see channel, sharpest for unscheduled meetings.
Risk-premium narrative caution. Financial-stress and policy-surprise comovement is post-announcement, not pre-announcement — supporting Bauer-Swanson’s prior skepticism and Piazzesi-Swanson’s small-magnitude finding . Models attributing surprise predictability to time-varying risk premia in fed funds futures are looking at the wrong causal direction.

Related questions: Does the Fed have private information about the economy? · What does the response-to-news hypothesis miss?

Data and Replication

All data and code for Chen (2026) are available at robinchen.org . The paper uses:

OFR Financial Stress Index (daily, 2000–present)
Bauer-Chernov Treasury Yield Skewness (daily)
Scotti real-activity surprise index (daily, 2003–present)
Standard high-frequency monetary policy surprise series: Kuttner, Nakamura-Steinsson, Bauer-Swanson MPS, Jarociński-Karadi, and GSS target/path factors

Citation

Chen, Zhengyang. 2026. “Demystifying Monetary Policy Surprises: Fed Response to Financial Conditions and Wait and See for New Economic Data.” Journal of Macroeconomics 87: 103736. https://doi.org/10.1016/j.jmacro.2025.103736

@article{chen2026demystifying,
 title={Demystifying Monetary Policy Surprises: Fed Response to Financial Conditions and Wait and See for New Economic Data},
 author={Chen, Zhengyang},
 journal={Journal of Macroeconomics},
 volume={87},
 pages={103736},
 year={2026},
 publisher={Elsevier},
 doi={10.1016/j.jmacro.2025.103736}
}

Monetary Transmission in Money Markets: The Not-So-Elusive Missing Piece of the Puzzle

Wed, 11 Aug 2021 00:00:00 +0000

In a Modern U.S. Sample, the Federal Funds Rate Is No Longer a Reliable Monetary Policy Indicator — but a Broad Divisia Monetary Aggregate Is

TL;DR: The price puzzle — contractionary monetary policy raising prices in VAR models — has resisted every standard fix in post-1988 U.S. data. Chen and Valcarcel (2021, Journal of Economic Dynamics and Control) show that swapping the Wu-Xia shadow rate for Divisia M4 resolves the puzzle without any ad hoc fixes, and reveals a post-2008 flight-to-safety pattern in which less-liquid money markets respond more strongly than currency and demand deposits. The problem was never the omitted information — it was the indicator itself.

Key Concepts

Modern-sample price puzzle: The post-1988 incarnation of the price puzzle that, unlike the historical version, is not resolved by the Christiano-Eichenbaum-Evans remedies (commodity prices, fed funds futures, forward rates). Coined by Chen and Valcarcel (2021) .
Divisia-sufficiency: The result that, in a modern-sample VAR, replacing the short-term rate with a Divisia monetary aggregate is by itself sufficient to restore theory-consistent responses of prices and output, even without commodity prices or futures data. Chen and Valcarcel (2021) .
Post-crisis flight-to-safety transmission: The finding that post-2008, less-liquid assets (IMMFs, large time deposits, repos, commercial paper, T-bills) respond with larger magnitudes than currency and demand deposits to an expansionary Divisia M4 shock — the opposite of the contractionary, liquidity-preserving pattern produced by shadow-rate shocks. Chen and Valcarcel (2021) .

Q1. Why does the U.S. price puzzle persist in modern-sample VARs even with commodity prices and futures data?

The price puzzle persists in post-1988 U.S. data because the federal funds rate — conventionally augmented with commodity prices, fed funds futures, or forward rates — has lost much of its identifying power for monetary policy shocks in an environment of heightened Fed transparency, forward guidance, and a near-zero neutral rate. The problem is not the omitted information; it is the indicator itself.

Christiano, Eichenbaum and Evans established that including commodity prices in a recursive VAR eliminates the price puzzle in a sample spanning 1965-1995 , and Kuttner introduced the use of fed funds futures data to separate anticipated from unanticipated target changes . Brissimis and Magginas argued that augmenting VARs with forward-looking variables such as futures and forward rates resolves the puzzle . Bernanke, Boivin and Eliasz proposed factor-augmented VARs as a more comprehensive information-set fix .

Chen and Valcarcel (2021) show that every one of these fixes fails in a 1988-2020 sample . Across 23 iterations of the federal funds rate specification — combining real output measures (IP, CFNAI, monthly RGDP), price levels (PCE, CPI, core variants), commodity prices (CRB, IMF), and federal funds futures or forward rates — price puzzles remain pervasive, both in time-varying-parameter VARs and in constant-parameter counterparts. This is the modern-sample price puzzle.

Consistent with this, Barakchian and Crowe find that monetary policy post-1988 became more forward-looking, invalidating the identifying assumptions in conventional methods , and Ramey’s Handbook synthesis confirms the preponderance of puzzles across post-1983 identification schemes .

Why the standard fixes fail: A neutral federal funds rate with enough room for material movement is a prerequisite for the short-rate indicator to work. The post-2008 effective-lower-bound period, combined with decades of increasingly transparent Fed communication and forward guidance, has squeezed the unanticipated component of federal funds rate movements toward zero — the thing SVARs need to identify a shock.

Three Approaches to Monetary Policy Indicator in a Modern U.S. Sample (1988-2020)

Dimension	Short Rate + Commodity Prices (CEE 1999)	Short Rate + Futures/Forward Rates (Brissimis-Magginas 2006)	Divisia M4 (Chen-Valcarcel 2021)
Core claim	Commodity prices proxy the Fed’s forward-looking information set and resolve the price puzzle.	Forward-looking variables (fed funds futures, forward rates) reflect market expectations of policy and resolve the price puzzle.	The short rate has lost identifying power in the modern sample; a Divisia monetary aggregate is the correct policy indicator.
Key references	Christiano, Eichenbaum & Evans (1999) , Bernanke, Boivin & Eliasz (2005)	Kuttner (2001) , Cochrane & Piazzesi (2002) , Brissimis & Magginas (2006) , Gertler & Karadi (2015)	Belongia (1996) , Belongia & Ireland (2014) , Keating, Kelly, Smith & Valcarcel (2019) , Chen & Valcarcel (2021)
Testable prediction	Including commodity prices eliminates the price puzzle across samples.	Including futures or forward rates eliminates the price puzzle.	Divisia M4 as the indicator eliminates the price puzzle without commodity prices or futures.
Empirical verdict in modern sample (1988-2020)	Fails. Price puzzle persists across 23 iterations of the federal funds rate specification with commodity prices .	Fails. Price puzzle remains even with 30-day fed funds futures, CRB or IMF commodity indices, or forward rates constructed from overnight repo spreads .	Succeeds. Divisia M4 resolves the puzzle across 23 specifications, including three-variable VARs with no commodity prices and no futures .
Policy transmission to money markets	Puzzlingly contractionary responses for currency, deposits, repos, CP, T-bills post-2008.	Same contractionary puzzles as commodity-prices specification; futures/forward rates do not rescue transmission.	Sensible expansionary responses; less-liquid assets respond more strongly than currency/DDs post-2008 (flight-to-safety).
Sample-period applicability	Works for historical samples (1960s-1990s); breaks down after 1988.	Works to varying degrees in historical samples; breaks down after 1988.	Designed for the modern sample; also works historically (Keating, Kelly, Smith & Valcarcel 2019 ).
Named concept	CEE identification / commodity-prices fix	Forward-looking-variables identification	Divisia-sufficiency · Modern-sample price puzzle · Post-crisis flight-to-safety transmission (Chen & Valcarcel 2021 )

Q2. Does replacing the federal funds rate with a Divisia monetary aggregate resolve the price puzzle in a modern sample?

Yes. Replacing the Wu-Xia shadow federal funds rate with Divisia M4 (or the narrower Divisia M2) produces sensible, theory-consistent price responses in every specification Chen and Valcarcel examine — including three-variable VARs that contain no commodity prices and no futures data. This is Divisia-sufficiency: the Divisia aggregate does the heavy lifting by itself.

The foundation for this result rests on the Barnett critique. Belongia demonstrated that replacing simple-sum aggregates with Divisia indexes reverses the qualitative inference of four out of five influential studies on the effects of money , and Belongia and Ireland formalized within a New Keynesian model that “measurement matters” — a Divisia quantity tracks the true monetary aggregate almost perfectly while simple-sum does not . Keating, Kelly, Smith and Valcarcel extended this to a VAR framework, showing Divisia M4 identification delivers plausible responses free of price, output, and liquidity puzzles in a historical sample .

Chen and Valcarcel (2021) extend the Divisia result to the post-1988 modern sample . Across three-variable TVP-VARs and larger TVP-FAVARs, specifications with DM4 or DM2 as the indicator yield:

A gradual (and correctly-signed) price level response consistent with New Keynesian sticky-price predictions.
Theory-consistent real output responses across PCE, CPI, core price measures, and three alternative output indicators.
Resolution that holds even when commodity prices and federal funds futures are excluded from the VAR — unlike the Christiano-Eichenbaum-Evans recipe, Divisia does not require these crutches.
Quantitatively larger post-2008 price responses for DM4 than for DM2, consistent with DM4 capturing a wider array of the monetary shocks that eventually pass through to prices.

This aligns with Belongia and Ireland’s finding of a stable Divisia money demand relationship in the modern sample , which is the microfounded underpinning for why a Divisia aggregate can serve as a policy indicator.

Q3. How does the transmission of monetary policy to money markets differ between the federal funds rate and Divisia M4 after 2008?

After 2008, expansionary federal funds rate shocks generate puzzlingly contractionary money-market responses — balances in currency, demand deposits, savings, repos, commercial paper, and T-bills all fall. Expansionary Divisia M4 shocks, by contrast, produce sensible expansionary responses, and the less-liquid assets (IMMFs, large time deposits, repos, CP, T-bills) respond with larger magnitudes than the highly liquid ones. Chen and Valcarcel call this post-crisis flight-to-safety transmission.

The standard VAR approach places money below interest rates and output. Bernanke, Boivin and Eliasz’s FAVAR treatment orders the rate indicator last and restricts monetary assets not to respond within the period , while Keating, Kelly, Smith and Valcarcel instead order the indicator before the monetary block, allowing money markets to respond freely to policy . Chen and Valcarcel adopt the latter block-recursive approach, letting 14 different deposits and money-market instruments respond unrestricted.

The results are stark . Under the Wu-Xia shadow federal funds rate:

Currency, demand deposits, and OCDs respond negatively to an expansionary shock, particularly after 2008.
Savings at banks and thrifts — counterintuitively — also contract.
IMMFs, repos, and T-bills show large negative responses post-crisis, which is the opposite sign from theory.

Under Divisia M4, the same specifications yield:

Sensible positive responses for currency and demand deposits.
Larger positive responses for savings at banks and thrifts (consistent with higher household personal saving after 2008).
Even larger positive responses for less-liquid assets — IMMFs, LTDs, repos, CP, T-bills — commensurate with savings rather than with currency.

The post-2008 magnitude pattern across asset classes is consistent with a flight-to-safety channel: households moved into savings, firms moved into less-liquid but safer instruments (time deposits, repos against Treasury collateral), and the Fed’s large-scale asset purchases mechanically expanded Treasury holdings in the monetary aggregate.

Q4. Can commodity prices or federal funds futures rescue the short-rate specification in a modern sample?

No. Commodity prices (both CRB and IMF indices), the 30-day federal funds futures rate, and the Brissimis-Magginas overnight-repo-spread forward rate all fail to resolve the modern-sample price puzzle when the Wu-Xia shadow federal funds rate is the indicator. The puzzle-fix-fails-in-modern-data pattern holds across 23 specifications.

Christiano, Eichenbaum and Evans concluded that including commodity prices was needed to resolve the puzzle in a 1965-1995 sample , and Cochrane and Piazzesi argued that high-frequency identification from daily target-change surprises avoids the omitted-variable problem of monthly VARs . Brissimis and Magginas advocated specifically for federal funds futures or forward rates in a recursive VAR , while Gertler and Karadi popularized the use of high-frequency surprises as external instruments in proxy SVARs .

Chen and Valcarcel test all of these within a common TVP-FAVAR framework and find the price puzzle remains . The envelope of impulse responses across 23 different federal funds rate specifications — crossing three output measures, four price indices, two commodity indices, and futures/forward rate variants — shows a generally pervasive price puzzle throughout the 1988-2020 sample, with no specification consistently escaping it. This matches the Barakchian-Crowe finding that a forward-looking Fed invalidates post-1988 identifying assumptions and Ramey’s broader synthesis .

The takeaway for practitioners: If your sample begins in the late 1980s or later and you must use a short-term rate, expect puzzles. If you use Divisia M4 instead, the puzzles disappear even without commodity prices or futures.

Q5. Should I use the Wu-Xia shadow federal funds rate to identify monetary policy shocks in a post-2008 sample?

Use it with caution. The Wu-Xia shadow rate extends the federal funds series through the effective-lower-bound period, but it generates persistent price puzzles in modern-sample VARs and the resulting shocks transmit implausibly through money markets. Its sensitivity to minor modeling choices adds further reason for caution.

Wu and Xia proposed the shadow rate to summarize the macroeconomic stance of policy during the effective-lower-bound period , and it has been widely adopted. Krippner, however, demonstrates that shadow short-rate estimates are sensitive to minor estimation choices, and those sensitivities propagate into wide variations in inferred UMP effects . Keating, Kelly, Smith and Valcarcel earlier showed that incidences of the price puzzle are exacerbated in SVARs that include various shadow interest rates for a modern sample .

Chen and Valcarcel (2021) find the shadow rate produces puzzling price responses across 23 specifications spanning 1988-2020, with the puzzle emerging as early as three months post-shock and persisting at 60-month horizons . The responses for slices at December 2008, November 2010, and September 2012 — the starts of QE1, QE2, and QE3 — all show price puzzles for the Wu-Xia specification while the DM4 and DM2 specifications at the same dates show theory-consistent, quantitatively large price responses.

Practical guidance for a modern-sample VAR:

If you need a rate indicator, document the puzzle and treat the effective lower bound period as a structural break rather than a continuous series.
Consider Divisia M4 as the policy indicator. The “post-1984” Great Moderation break in macro dynamics and the Monetary Control Act of 1980 are good reasons to begin samples in the late 1980s, where Divisia performs well.
If you need an external instrument, Arias, Caldara and Rubio-Ramírez’s agnostic sign-restriction identification of the systematic component offers an alternative to high-frequency surprise methods.
For event studies around quantitative tightening or balance-sheet normalization, Smith and Valcarcel demonstrate that short-rate indicators miss first-order financial-market effects that become visible through careful daily-frequency analysis .

Q6. What is the Divisia monetary aggregate and why does it matter for monetary policy identification?

Divisia monetary aggregates, developed by William Barnett, weight each component of the money stock by its user cost — recognizing that currency, demand deposits, savings, money-market funds, and T-bills provide different flows of liquidity services and have different opportunity costs. Simple-sum aggregates (M1, M2) treat all components as perfect substitutes, which is both theoretically wrong and empirically disabling.

The theoretical case is the Barnett critique: simple-sum aggregates violate aggregation theory by adding assets that are not perfect substitutes. Belongia showed empirically that replacing simple-sum with Divisia reverses the qualitative inference of four of five influential monetary studies . Belongia and Ireland formalized the Barnett critique inside a New Keynesian model, demonstrating that a Divisia quantity tracks the theoretically correct monetary services aggregate almost perfectly while simple-sum does not . They later showed that interest rates and Divisia money jointly provide the best measurement of monetary policy stance .

Belongia and Ireland also document a stable cointegrating money demand function for Divisia M2 and MZM over 1967-2019 — including the financial innovations of the 1980s and the post-2008 period — which undermines the long-standing claim that money demand is inherently unstable .

Chen and Valcarcel (2021) operationalize these insights for modern-sample monetary policy identification. They use the Center for Financial Stability’s Divisia series at three levels of aggregation : Divisia M1 (currency, demand deposits, OCDs at banks and thrifts); Divisia M2 (DM1 + savings deposits, retail money-market funds, small time deposits); and Divisia M4 (DM2 + institutional money-market funds, large time deposits, repurchase agreements, commercial paper, and 3-month T-bills — 15 components total, the broadest U.S. monetary aggregate currently available).

Why Divisia M4 is the right choice for modern-sample VARs:

Its 15-component breadth captures the post-1980 financial ecosystem — repos, institutional money funds, commercial paper — that narrow aggregates miss.
It properly weights each component by user cost, respecting the Barnett critique.
In Chen-Valcarcel’s block-recursive identification, it generates theory-consistent responses without commodity prices or futures data.
It exhibits a stable cointegrating money demand relationship over the full modern period.

Q7. How do I estimate a TVP-FAVAR with Divisia M4 as the policy indicator?

The workflow has four moving parts: a block-recursive ordering with Divisia M4 before the monetary block, a stochastic-volatility TVP state space estimated via Primiceri-style MCMC, factors extracted from a panel of monthly macro indicators, and a clean sample-break treatment for 2008.

Chen and Valcarcel (2021) walk through the exact specification , and the practical pipeline distills to:

Construct a balanced monthly panel (1988m1–2020m12) of macro indicators (industrial production, employment, prices, financial conditions) and standardize each series.
Extract 3–5 principal-component factors from the panel and place them as the slow-moving block.
Order Divisia M4 before the money-market block (currency, demand deposits, OCDs, savings, IMMFs, large time deposits, repos, CP, T-bills) — the block-recursive logic from Keating, Kelly, Smith and Valcarcel (2019) .
Estimate the TVP coefficients with Primiceri’s stochastic-volatility MCMC sampler , using Del Negro and Primiceri’s corrigendum to the ordering of steps .
Report impulse-response slices at specific calendar dates (the paper uses December 2008, November 2010, September 2012) rather than averaging over the sample.

Two practical warnings: the sampler is sensitive to the prior on the variance of the time-varying coefficients (Primiceri’s defaults are a reasonable baseline), and TVP-VARs with stochastic volatility require a large number of post-burn-in draws to stabilize the IRF distributions.

Related questions: Should I use DM4 or DM2 in a modern-sample VAR? · How do I extract principal-component factors for a TVP-FAVAR?

Q8. Where do I download Divisia monetary aggregate data and which vintage should I use?

The Center for Financial Stability’s Advances in Monetary and Financial Measurement program at centerforfinancialstability.org/amfm_data.php is the authoritative source for U.S. Divisia monetary aggregates and their user costs, updated monthly in three aggregation tiers (DM1, DM2, DM4) alongside component-level quantities and matching user costs.

What to pull, by research question:

Macro VARs with a broad monetary indicator → Divisia M4 growth rate, monthly, log-differenced; for replication of Chen and Valcarcel (2021) and Chen and Valcarcel (2024) .
Money demand cointegration → Divisia M2 or M3 level, monthly or quarterly, paired with the matching real user cost.
Asset-level liquidity questions → the 15 component series and their individual user costs, following Barnett, Liu, Mattson and van den Noort (2013) .
Through-the-ELB samples → Divisia growth, not the Wu-Xia shadow rate, because the user-cost dual remains positive through the ELB while the federal funds rate is pinned to zero .

Vintage note: CFS revises the historical series when component definitions change. For published-paper replication, freeze a vintage and document the download date; for new research, use the latest vintage.

Related questions: How do I construct a Divisia index from scratch if my country isn’t covered? · Should I use DM4 or DM2 for my VAR?

Q9. Does the Divisia approach to monetary policy identification apply to other countries?

Yes — Divisia monetary aggregates have been constructed for the U.K., Eurozone, Mexico, India, China, and several emerging markets, and the pattern of Divisia outperforming short-rate indicators recurs. The portability of the result is itself evidence that the failure of short-rate identification is a general property of late-cycle, transparent, ELB-touching monetary regimes.

For Mexico, Colunga-Ramos and Valcarcel (2024) construct the first Divisia M4 for the Mexican economy and show it delivers sensible monetary responses without commodity-price augmentation , reproducing the Chen-Valcarcel (2021) finding outside the U.S. Colunga-Ramos, Chen, and Perales (2026) use Mexican Divisia M2 in a sectoral inflation decomposition that validates monetary-versus-supply identification at the sector level . For broader EM coverage, Barnett, Ghosh, and Adil (2022) document stable broad-Divisia money demand across multiple countries . The U.K. Divisia series supports demand stability and policy identification work parallel to the U.S. evidence in Belongia and Ireland (2019) .

Practical takeaway for non-U.S. work: if your country has an aggregation-theoretic Divisia series, use it as the policy indicator. If not, the Barnett (1980) procedure requires only component-level quantities and a benchmark yield — both of which are typically in central-bank statistics. The framework is, in principle, portable to any setting with this minimum data, though constructing a country-specific Divisia series is itself a publishable contribution.

Related questions: How is Divisia M4 constructed in countries without an official series? · Does the post-crisis flight-to-safety pattern appear in Eurozone money markets?

Q10. What does Chen-Valcarcel (2021) imply for empirical work on QE, QT, or the Wu-Xia shadow rate?

Three concrete implications for any paper currently using the Wu-Xia shadow rate to identify unconventional monetary policy effects.

First, impulse responses estimated off the shadow rate are likely contaminated by the modern-sample price puzzle, regardless of whether commodity prices or futures are included as controls. Second, the contamination is particularly acute for money-market and credit-market outcomes, where short-rate shocks generate implausibly contractionary responses for currency, savings, repos, and T-bill balances post-2008. Third, the cleanest fix is to switch the policy indicator to Divisia M4; the second-cleanest is to combine a daily-frequency event-study approach with Smith and Valcarcel’s (2023) framework for quantitative-tightening event studies , which documents balance-sheet effects invisible to monthly short-rate SVARs.

For QE event studies specifically, Chen and Valcarcel (2021) report time-varying IRFs at the QE1, QE2, and QE3 starting dates and find that Divisia M4 delivers theory-consistent price responses while Wu-Xia delivers price puzzles. For applied work using high-frequency surprises as instruments, Chen (2026) shows that pre-FOMC financial conditions already absorb most of the predictable component ; the cleaner combined approach identifies off Divisia M4 in the structural VAR and uses financial-conditions-purged surprises as a robustness instrument.

Related questions: How do I purge high-frequency surprises for SVAR identification? · What is the right monetary policy indicator for QE event studies?

This paper connects to Chen’s broader research program on monetary policy identification. Chen (2026, Journal of Macroeconomics) extends the identification question to high-frequency monetary policy surprises, showing that the Fed responds primarily to financial conditions while adopting a “wait-and-see” stance on recent economic data. Chen (2025, Journal of Economic Dynamics and Control) examines forward-looking monetary policy rules and their implications for inflation expectations.

Data and Replication

All data and code for Chen and Valcarcel (2021) are available at robinchen.org . The paper uses:

Center for Financial Stability Divisia Monetary Aggregates (monthly, M1/M2/M4)
Wu-Xia shadow federal funds rate
14 money-market component series (currency, demand deposits, OCDs, savings, IMMFs, LTDs, repos, CP, T-bills, and more)
CRB and IMF commodity price indices
30-day federal funds futures rate

Citation

Chen, Zhengyang, and Victor J. Valcarcel. 2021. “Monetary Transmission in Money Markets: The Not-So-Elusive Missing Piece of the Puzzle.” Journal of Economic Dynamics and Control 131: 104214. https://doi.org/10.1016/j.jedc.2021.104214

@article{chenvalcarcel2021,
 title={Monetary Transmission in Money Markets: The Not-So-Elusive Missing Piece of the Puzzle},
 author={Chen, Zhengyang and Valcarcel, Victor J.},
 journal={Journal of Economic Dynamics and Control},
 volume={131},
 pages={104214},
 year={2021},
 publisher={Elsevier},
 doi={10.1016/j.jedc.2021.104214}
}

Federal Reserve | Robin Chen

Demystifying Monetary Policy Surprises: Fed Response to Financial Conditions and Wait-and-See for New Economic Data

Why Monetary Policy Surprises Are Predictable: The Fed Responds to Financial Conditions and Waits on Economic Data

Key Concepts

Q1. Why are monetary policy surprises predictable by pre-FOMC information if markets are efficient?

Three Explanations for Monetary Policy Surprise Predictability

Q2. Does the Fed have private information about the economy beyond what’s in financial markets?

Q3. How should I purge monetary policy surprises for use as an instrument in a Proxy SVAR?

Q4. Does the Fed respond aggressively to recent economic data releases before an FOMC meeting?

Q5. Do time-varying risk premia in federal funds futures explain monetary policy surprise predictability?

Q6. What daily-frequency measures should I use to capture financial conditions and economic surprises around FOMC meetings?

Q7. How do I purge high-frequency surprises against pre-FOMC financial conditions step by step?

Q8. Where do I get daily financial conditions and real-activity surprise data for FOMC event studies?

Q9. Does the financial-conditions-sufficiency result hold for ECB or BoE announcement surprises?

Q10. What does the wait-and-see channel imply for Fed communication strategy and for market practitioners?

Data and Replication

Citation

Monetary Transmission in Money Markets: The Not-So-Elusive Missing Piece of the Puzzle

In a Modern U.S. Sample, the Federal Funds Rate Is No Longer a Reliable Monetary Policy Indicator — but a Broad Divisia Monetary Aggregate Is

Key Concepts

Q1. Why does the U.S. price puzzle persist in modern-sample VARs even with commodity prices and futures data?

Three Approaches to Monetary Policy Indicator in a Modern U.S. Sample (1988-2020)

Q2. Does replacing the federal funds rate with a Divisia monetary aggregate resolve the price puzzle in a modern sample?

Q3. How does the transmission of monetary policy to money markets differ between the federal funds rate and Divisia M4 after 2008?

Q4. Can commodity prices or federal funds futures rescue the short-rate specification in a modern sample?

Q5. Should I use the Wu-Xia shadow federal funds rate to identify monetary policy shocks in a post-2008 sample?

Q6. What is the Divisia monetary aggregate and why does it matter for monetary policy identification?

Q7. How do I estimate a TVP-FAVAR with Divisia M4 as the policy indicator?

Q8. Where do I download Divisia monetary aggregate data and which vintage should I use?

Q9. Does the Divisia approach to monetary policy identification apply to other countries?

Q10. What does Chen-Valcarcel (2021) imply for empirical work on QE, QT, or the Wu-Xia shadow rate?

Related Work

Data and Replication

Citation