Macroprudential Policy in the Euro Area
What this paper finds — and why it matters
Layer 1: Overview
Research question and motivation. There is now broad consensus that monetary authorities should hold a financial-stability mandate and that macroprudential policy should be part of it, yet evidence on the macroeconomic effectiveness of these policies and their interaction with monetary policy remains thin and inconclusive. The paper addresses this gap for the euro area, a case of special interest because of its international structure and because, within the short life of the euro, member states experienced major episodes of financial instability (the great financial crisis, GFC, and the sovereign debt crisis). The contribution is twofold: (1) build a novel aggregate index of the euro-area macroprudential policy stance and document its stylized facts since 1999; (2) be the first to identify, within a structural econometric framework, both unanticipated (surprise) and anticipated (news) exogenous macroprudential policy shocks and trace their macroeconomic effects.
Data and method. The authors use MaPPED (Macro-Prudential Policies Evaluation Database), built by ECB staff and national central banks. For euro-area countries it records 1205 policy actions between 1995 and 2019 across 11 instrument types (capital buffers, lending standards, maturity mismatch tools, limits on credit growth, exposure limits, liquidity rules, loan loss provisions, minimum capital requirements and risk weights, leverage ratio, and ‘other measures’). Actions are signed (+ tightening, − loosening, 0 ambiguous) and weighted following Meuleman and Vander Vennet (2020): activation 1, change in level 0.25, change in scope 0.10, maintaining level/scope 0.05; deactivation resets the cumulative index to zero. This yields around 470 instrument-level indices, summed within each country and then aggregated across countries using GDP-share weights to form the EAMPP index. The empirical model is a seven-variable Bayesian SVAR at quarterly frequency over 1999:Q1–2019:Q2, estimated in levels with 4 lags and a Minnesota prior using the hyperparameters of Kurmann and Otrok (2013). Variables: the narrative EAMPP (which excludes countercyclical/financial-cycle-reactive policies so it is exogenous in the Romer-Romer sense), total credit to the private non-financial sector, real GDP, core CPI, inflation expectations (ZEW 6-month survey), VSTOXX, and a monetary policy rate (EONIA 1999–2009, Wu-Xia shadow rate thereafter). The surprise shock is identified by a Cholesky ordering with EAMPP first; the news shock is identified via the Barsky-Sims (2011) forecast-error-variance maximization (horizon k=0 to k=24), orthogonal to the surprise shock and not affecting EAMPP contemporaneously.
Main findings. Stylized facts: EAMPP shows a positive starting value (policies predating the euro), a small positive trend up to the GFC, a loosening on average at the start of the GFC in 2009, then a clear upward (tightening) trend over the following seven years driven by sovereign-debt-crisis concerns and Basel III/CRR-CRDIV; the level in 2016 is almost twice as tightening as pre-crisis. The largest quarterly EAMPP change occurred in 2013:Q3 (CRR/CRDIV announcements). Policy announcements averaged about 13 per quarter in 1999–2015 versus about 2 per quarter in 2016–2019. Macroprudential and monetary policy moved oppositely; their correlation is about −0.90, negative and significant. SVAR results: a tightening surprise shock persistently raises the policy index, lowers total credit (on impact, accentuating over the medium term), reduces output in a way negatively correlated with credit (lowering credit pro-cyclicality), and lowers VSTOXX over the medium term after an initial rise. The effect on core CPI is negligible and on inflation expectations insignificant, so no price-stability trade-off; the monetary policy rate declines (accommodative complement). The news shock produces a gradual, persistent tightening, reduces credit, lowers credit pro-cyclicality, has muted effect on VSTOXX, and an insignificant price effect; the policy rate first rises then turns negative over the medium term. FEV decomposition: the two shocks combine to explain about half of credit variability after 24 quarters; neither shock exceeds 12% of core-CPI forecast variance and combined they never exceed 15% of prices. News shocks explain about 20% of credit forecast variance within the first quarter. Granger-causality and serial-correlation tests support exogeneity of both shocks.
Layer 2: Deep Dive
What is the identification strategy and what are the main threats to it?
Two shocks driving non-systematic macroprudential variation are identified within a seven-variable Bayesian SVAR (1999:Q1–2019:Q2, 4 lags, Minnesota prior). The surprise (unanticipated) shock is identified by a Cholesky decomposition with EAMPP ordered first, so it can affect EAMPP contemporaneously. The news (anticipated) shock uses the Barsky-Sims (2011) forecast-error-variance maximization: it is the orthonormal column that maximizes the cumulated forecast error variance of EAMPP over horizons k=0 to k=24, subject to not affecting EAMPP contemporaneously and being orthogonal to the surprise shock. A key prior step is constructing a narrative EAMPP that drops all policies with a countercyclical design (those reacting to the financial cycle), making the remaining index exogenous in the Romer-Romer (2010) sense. The main threats are: foresight/anticipation contaminating shock identification (addressed by using announcement rather than enforcement dates and by identifying news shocks); reverse causality and contemporaneous effects that plague recursive/GMM panel approaches; and informational insufficiency (whether the series are genuine shocks), which the authors test via Granger causality against forward-looking credit-standard surveys and serial-correlation tests.
What are the main mechanisms and how are they distinguished empirically?
The mechanism is that a tightening macroprudential stance curbs total credit to the private non-financial sector, which is the most robust predictor of financial crises, thereby moderating systemic risk and the build-up of excess credit during booms. Crucially, output responds in a way negatively correlated with credit, so the policy lowers the pro-cyclicality of credit (the key financial-stability gain). Surprise and news shocks are distinguished by their dynamics and by the FEV decomposition: news shocks dominate at short horizons (agents react quickly to signals, ~20% of credit forecast variance in the first quarter), while surprise shocks build gradually to a comparable share at medium-to-long horizons. The monetary-policy interaction is read off the policy-rate response: it moves accommodatively (declines) after a surprise tightening, complementing macroprudential policy without a price trade-off.
What heterogeneity or differences across shock types are documented?
The two shock types differ. The surprise shock causes an immediate credit drop that accentuates over the medium term and an accommodative (declining) monetary policy rate; VSTOXX first rises then falls below baseline. The news shock causes a gradual, persistent policy tightening, a credit decline that moderates before dropping again over the medium term, a muted VSTOXX response, and a monetary policy rate that first increases (complementing the tightening and reflecting a small initial price rise) then turns negative over the medium term. Core prices show a small initial increase under the news shock before declining, whereas the surprise shock barely affects core CPI. Both shocks ultimately lower credit pro-cyclicality and have insignificant effects on price stability.
What robustness checks are run?
Several. (1) Alternative macroprudential target variables replacing total credit: a systemic-risk index (CISS) — results barely change; bank credit — results similar, with a more pronounced decline in bank credit; household credit — results similar but the household-credit decline is stronger, while under the surprise shock the credit decline becomes insignificant and output rises initially. (2) Replacing VSTOXX with VDAX (German analogue) — qualitatively the same. (3) Longer FEV truncation horizons k=30 and k=40 — quantitatively and qualitatively similar. (4) Including policies with missing announcement dates (182 of 1205 actions) in the empirical analysis — results barely change. (5) Granger-causality tests: the identified shocks are regressed on up to 3 principal components (explaining ~98.4% of variance) of seven forward-looking loan-officer credit-standard surveys; the null of no Granger causality cannot be rejected at any reasonable level (p-values range roughly 0.37–0.99). (6) Serial-correlation test regressing each shock on its own two lags: p-values 0.47 (surprise) and 0.77 (news), so no serial correlation.
How does this paper relate to and differ from closely related prior work?
It relates to (a) empirical work on macroprudential effectiveness and its monetary-policy interaction (Cerutti et al., Alam et al., Akinci and Olmstead-Rumsey, Kuttner and Shim, Budnik and Kleibl, etc.), most of which uses cross-country panels with GMM and cannot make clean causal claims; and (b) the SVAR/news-shock identification literature robust to foresight (Barsky and Sims 2011; Leeper et al. 2013; Kurmann and Otrok 2013; Ben Zeev et al. 2019). The two prior SVAR studies extracting exogenous macroprudential variation are Kim and Mehrotra (2017, four Asia-Pacific countries) and Klingelhofer and Sun (2019, China), both using recursive Cholesky orderings. Like Klingelhofer and Sun, the authors find macroprudential shocks explain a meaningful share of credit but little of prices. Unlike those studies, they find a strong macroprudential-monetary link (EAMPP-policy-rate correlation about −0.90, versus roughly +0.25 for Asia-Pacific in Bruno et al. 2017), and they are the first to identify both surprise and news macroprudential shocks. The narrative exclusion of cyclically-reactive policies follows Romer and Romer (2010), Richter et al. (2019), and Rojas et al. (2020).
What are the policy implications and their scope conditions?
Macroprudential policy in the euro area effectively safeguards financial stability over the medium term by reducing credit growth, credit pro-cyclicality, and systemic risk, without a significant trade-off against price stability (the ECB’s primary target). Because more than one objective cannot be met with one instrument, monetary policy complements macroprudential policy: it can move accommodatively to offset output/credit declines, yielding an effective overall policy mix. Scope conditions: the conclusions are specific to the euro area over 1999:Q1–2019:Q2, a sample dominated by the GFC and sovereign debt crisis and by deflationary pressures (which is why the strong, negative macroprudential-monetary correlation may not generalize, e.g., to Asia-Pacific where the correlation is positive); the narrative EAMPP only captures proactive, long-run-financial-stability-motivated policies; and price-stability effects, while insignificant overall, carry wide estimate uncertainty.
Why does the paper use announcement dates rather than enforcement dates?
Because foresight problems arise from inside and outside lags (Leeper et al. 2013): about 54% of euro-area policy tools in MaPPED experience a delay between announcement and implementation. Using the enforcement date would contaminate the identification of an ‘unanticipated’ shock, since agents would already know about the policy from its announcement, making the shock no longer exogenous. The authors assume agents react from the announcement moment.
Are there notable caveats about the index and impulse responses?
The first EAMPP value is not zero because 185 of 1205 policy actions were implemented before 1995, and MaPPED does not provide announcement dates for 182 of 1205 actions (assumed equal to enforcement dates only for the stylized-facts section; removed in the empirical analysis). GDP-share weights use the 2008–2015 average; time-varying weights have very limited impact since GDP shares are stable. Impulse responses report median with 16th and 84th posterior percentiles. The EONIA-shadow-rate splice is justified by a 0.98 correlation between the two over 2004:Q4–2008:Q4.