Monetary Policy, Inflation, and Crises: Jimenez, Kuvshinov, Peydro & Richter (2026)

What this is. The paper’s core results, the identification strategy (trilemma IV), and the key empirical specifications with equations: enough to know what it found and how, without reading all 48 pages. To replicate or extend, read the full source at the original.

TL;DR

The paper shows that what matters for banking crisis risk is not the level of monetary policy rates, but the full path: a U-shaped path of prolonged cuts followed by hikes is associated with roughly double the unconditional crisis probability. Using long-run data for 17 advanced economies back to 1870 and Spanish loan-level administrative data (1995 to 2008), the paper finds that prolonged rate cuts fuel credit supply expansions and asset price booms (the financial red zone), and that subsequent rate hikes crystallize these vulnerabilities into crises, primarily through realized credit risk rather than interest rate risk. Neither cuts alone nor hikes alone are strongly linked to crises; it is the combination that matters.

Core results

Magnitudes and significance are as reported; */**/*** = 10%/5%/1%.

#	Result	Locator	Magnitude
R1	U-shaped monetary rate path is more than twice as frequent before banking crises as unconditionally; 100% of deep post-WWII crises preceded by U shape	Table I, p. 935	Crisis conditional frequency: U shape 55% (all crises) vs 27% unconditional; 100% for post-WWII deep crises
R2	U-shaped rate path is associated with 18% three-year crisis frequency, roughly double the 10% unconditional probability; deep and post-WWII crises show even larger gaps	Table II, p. 936	U shape: 18%*** crisis frequency vs 6-9% for other rate paths; deep crisis: 12%*** vs 1-4%
R3	OLS regression: the interaction of rate hikes with previous cuts (U-shape) significantly raises crisis probability; 1 ppt hike after cuts raises three-year crisis probability by 9 ppts (sum of coefficients)	Table III col. (2), p. 939	$\Delta_3\text{Rate} \times \text{Cut}$ interaction = 0.03** (s.e. 0.01); sum of first three coefficients approx 0.09
R4	IV result (trilemma instrument): 1 ppt rate increase over three years, after rates were cut for five years, raises three-year crisis probability by approximately 10 to 12 ppts	Table III col. (4), p. 939-940	$\Delta_3\text{Rate} \times \text{Cut}$ (IV) = 0.07** (s.e. 0.03); sum approx 0.10-0.12; Kleibergen-Paap weak ID = 27.48
R5	U-shaped rates are not associated with nonfinancial recession risk; for recessions the interaction term is small and insignificant	Table IV, p. 942	$\Delta_3\text{Rate} \times \text{Cut}$ in recession regression = 0.02 (s.e. 0.01), insignificant; rate level alone raises recession risk
R6	A residual (above-and-beyond-systematic) U-shaped monetary path raises the three-year crisis frequency to 26%; combining the residual U shape with the financial red zone raises it to 45% (all crises), versus 36% for any U-shaped path with a red zone and 22% for a systematic U shape with a red zone	Tables V and VIII, pp. 943, 951	Strong residual U crisis frequency: 26% (Table V, all crises). Table VIII: any U-shape + red zone = 36% (Panel A, 18/50); residual U-shape + red zone = 45% (Panel B, 14/30 all crises; 48% post-WWII, 11/22); systematic U-shape + red zone = 22% (3/15)
R7	Red zones (high credit and asset price growth) are strongly associated with future crises only if preceded by a U-shaped monetary rate path; monetary rate hikes while in the red zone raise crisis risk (R-zone x rate hike interaction = 0.18*** to 0.38***)	Tables VII and IX, pp. 950, 953	$\text{R-zone} \times I(\Delta_3\text{Rate} \geq 0)$ = 0.18*** (OLS, s.e. 0.05), 0.38*** (IV, s.e. 0.15); R-zones pre-raised: interaction 0.22*** (OLS, s.e. 0.08)
R8	Spain loan level: monetary rate cuts increase credit growth, especially from weaker banks to riskier firms; 1 ppt cut raises credit growth by 4.8 ppt at the bank-firm level, rising 2.8 ppt further per interquartile increase in bank NPL ratio	Table XI Panel A, p. 962	$\text{Cut}$ = 4.80** (col. 2); $\text{Cut} \times \text{Bank NPL ratio}$ = 2.62** (col. 3); triple interaction with real estate firms: up to 7.9 ppt additional (col. 6)
R9	Spain loan level: monetary rate cuts reduce firm cost of debt by 20 bps on average, with larger reductions for firms borrowing from weaker (high-NPL) banks, consistent with credit supply and mispricing	Table XI Panel B, p. 962	$\text{Cut}$ = -0.20*** (col. 1); $\text{Cut} \times \text{Bank NPL ratio}$ = -0.13*** to -0.32*** (cols. 2-5)
R10	Spain loan level: U-shaped monetary path raises loan default probability; 1 ppt rate hike after cuts raises three-year delinquency probability by 11.2% relative, with effects stronger for loans by weaker banks to real estate firms	Table XII, pp. 964-965	$\Delta_3\text{Rate}$ (col. 3) = 0.002***; $\text{Cut}$ = 0.011***; $\Delta_3\text{Rate} \times \text{Cut}$ (col. 3) = 0.005***; quadruple interaction ($\Delta_3\text{Rate} \times \text{Cut} \times \text{Bank NPL} \times \text{Real estate}$, col. 6) = 0.005***

Overall (paper’s conclusion). The dynamic path of monetary policy rates is crucial for financial stability. Prolonged rate cuts fuel credit and asset price booms through credit supply (including bank risk-taking and mispricing), and subsequent rate hikes crystallize these vulnerabilities into banking crises through realized credit risk. Neither the red zone alone nor U-shaped monetary rates alone are sufficient to produce a high crisis probability; their combination is what generates the largest crisis risks historically (p. 965-966). This differs from Grimm et al. (2023) on mechanism: where they emphasize loose policy, this paper uses nominal rates to show the full U-shaped path (not just the easing leg) matters, and adds administrative loan-level evidence.

Theory / model

The paper has no structural model. It tests a path-dependency hypothesis: crisis risk depends not on the current level of monetary rates but on the sequence of cuts and subsequent hikes. The economic mechanism it proposes is consistent with the theoretical framework of Boissay et al. (2023), in which a long period of monetary loosening triggers an investment and credit boom and a search for yield/risk-taking; the subsequent tightening then collapses credit markets through the fear of loan defaults.

Identification strategy. The key endogeneity concern is that central banks raise rates when the economy (and the financial sector) is overheating, so a positive correlation between rate hikes and crisis risk could reflect omitted financial-sector vulnerabilities rather than a causal effect. The paper addresses this by:

1. Controlling for contemporaneous and eight lags of country-level and global GDP growth and inflation in all specifications.
2. Residualizing monetary rate changes with respect to the main business-cycle variables (GDP growth, inflation, investment, consumption, current account, short- and long-term rates, decade fixed effects) to separate the systematic from the discretionary component.
3. Using the Mundell trilemma instrumental variable (see Method section), which exploits variation in base-country monetary policy transmitted through fixed-exchange-rate pegs and open capital accounts (Jorda, Schularick, and Taylor, 2020).

The paper shows the U-shape result is present for both raw and residualized rate changes, and that the effect is larger for the residual (discretionary) component, ruling out the possibility that the U shape merely reflects mechanical policy responses to business-cycle conditions.

Method

Crisis-window regressions (equation 1, p. 932).

$$y_{i,t+h} - y_{i,t} = \alpha_{i,h} + \alpha_{d,h} + \beta_h \cdot 1[\text{Crisis}_{i,t}=1] + \epsilon_{i,t+h}$$

• $h = -7, \ldots, 0, \ldots, 7$: years relative to crisis onset
• $y$: monetary policy rate level
• $\alpha_i$: country FE; $\alpha_d$: decade FE

This plots the average path of monetary rates around historical crisis events, with 90% confidence intervals, for different crisis definitions and subsamples.

Linear probability model for crisis risk (equation 2, p. 937-938).

$$\text{Crisis}_{i,t\text{ to }t+2} = \alpha_i + \beta_1 \cdot \Delta_3\text{Rate}_{i,t} + \beta_2 \cdot \text{Cut}_{i,t-8,t-3} + \beta_3 \cdot \Delta_3\text{Rate}_{i,t} \times \text{Cut}_{i,t-8,t-3} + \gamma \cdot X_{i,t} + u_{i,t}$$

• $\Delta_3\text{Rate}$: three-year change in monetary policy rate (ppts)
• $\text{Cut}$: 1 if monetary rates were cumulatively cut (t-8 to t-3)
• $X$: contemporaneous values and eight lags of local and global inflation and GDP growth
• SE: Driscoll-Kraay (five lags) to account for cross-country, cross-time correlation

The coefficient $\beta_3$ is the U-shape test: it captures whether rate hikes are especially crisis-inducing when preceded by prolonged cuts.

Trilemma IV (equation 3, p. 938).

$$\text{Trilemma IV}_{i,t} = \Delta\text{Rate}^{\text{Residual}}_{b(i),t} \times \text{PEG}_{i,t} \times \text{PEG}_{i,t-1} \times \text{KOPEN}_{i,t}$$

• $\Delta\text{Rate}^{\text{Residual}}_{b(i),t}$: residualized monetary rate change of the base country $b(i)$ (e.g. Germany for ERM members)
• $\text{PEG}$: 1 if fixed exchange rate regime
• $\text{KOPEN}$: degree of capital account openness (Quinn-Schindler-Toyoda rescaled)

The IV strategy instruments $\Delta_3\text{Rate}$ with the three-year change in the residualized trilemma variable, and the interaction with $\text{Cut}$ with the trilemma variable interacted with the cut dummy. Standard errors remain Driscoll-Kraay. First-stage Kleibergen-Paap weak ID statistics are well above conventional thresholds (27.48 to 65.68 across columns; Table III, p. 939).

Local projections for red zone interaction (equation 4, p. 954).

$$\Delta_h y_{i,t} = \alpha_{i,h} + \beta_{1,h} \cdot \Delta\text{Rate}_{i,t} + \beta_{2,h} \cdot I(\Delta_3 y_{i,t} \geq \text{Rz}) + \beta_{3,h} \cdot \Delta\text{Rate}_{i,t} \times I(\Delta_3 y_{i,t} \geq \text{Rz}) + \gamma \cdot X + \epsilon_{i,t+h}, \quad h = 1, \ldots, 10$$

• $y$: household credit, house prices, business credit, or equity prices
• $\text{Rz}$: red zone threshold (80th pctile for credit, 66.7th pctile for asset prices, following Greenwood et al. 2022)
• $\beta_{3,h}$: main coefficient: does a rate hike reverse vulnerabilities more strongly when the financial variable is already elevated?
• SE: Driscoll-Kraay with $1.5 \times h$ lags; 10% confidence intervals

Spain loan-level credit supply regression (equation 5, p. 960).

$$\Delta y_{i,j,t} = \beta_1 \cdot \text{Cut}_{t-5,t} + \beta_2 \cdot \text{Cut}_{t-5,t} \times \text{Bank risk}_{i,t-1} + \beta_3 \cdot \text{Cut}_{t-5,t} \times \text{Bank risk}_{i,t-1} \times \text{Firm risk}_{j,t-1} + \gamma_1 \cdot F_{j,t-1} + \gamma_2 \cdot B_{i,t-1} + \gamma_3 \cdot M_t + u_{i,j,t}$$

• $\Delta y$: log change in credit granted by bank $i$ to firm $j$
• $\text{Cut}$: 1 if overnight rates were below their average between t-5 and t
• $\text{Bank risk}$: bank NPL ratio (proxy for ex ante bank risk)
• $\text{Firm risk}$: 1 if firm is in construction/real estate sector
• $F$: firm-level controls (industry, location) and FE
• $B$: bank-level controls and FE
• $M$: macro controls and time FE; also firmbank and firmtime FE variants
• SE: clustered at time and bank levels

Spain loan-level default regression (equation 6, p. 963).

$$\text{Loan Default}_{i,j,t,t+3} = \beta_1 \cdot \Delta_3\text{Rate}_{t,t+3} + \beta_2 \cdot \text{Cut}_{t-5,t} + \beta_3 \cdot \Delta_3\text{Rate}_{t,t+3} \times \text{Cut}_{t-5,t} + \gamma_1 \cdot F_{j,t-1} + \gamma_2 \cdot B_{i,t-1} + \gamma_3 \cdot M_t + u_{i,j,t,t+3}$$

• $\text{Loan Default}$: 1 if loan becomes delinquent (>90 days overdue) in t+1 to t+3
• $\Delta_3\text{Rate}$: ppt change in monetary rate between t and t+3
• $\beta_3 > 0$: hikes are more crisis-inducing when preceded by cuts (U-shape test)
• SE: clustered at time and bank levels

Empirical specifications

The headline results tie to the following specification choices:

• Macro panel (R1-R7). Sample: 17 advanced economies, 1870 to 2020, 87 banking crises (Jorda, Schularick, and Taylor 2016a chronology), annual frequency. Baseline uses the narrative crisis definition of Schularick and Taylor (2012). Robustness: Baron, Verner, and Xiong (2021) crisis dates; probit vs linear probability models; one-year vs three-year crisis windows; alternative rate-path window lengths; global credit-growth controls; decade fixed effects (Table III; Internet Appendix Tables IA.VI-IA.XI).

• U-shape path classification. An eight-year window is classified into four shapes based on the direction of the cumulative change in t-8 to t-3 and in t-3 to t. U shape = cumulative cut in the first five years followed by a raise in the last three years. This classification is used in frequency comparisons (Tables I, II, V, VIII) and interacted with rate changes in regression (equation 2).

• Financial red zone (R7, R8 mechanism). Defined following Greenwood et al. (2022) as periods when both credit growth (above the 80th percentile of the three-year change in the credit-to-GDP ratio) and asset price growth (above the 66.7th percentile of three-year real asset price changes) are simultaneously elevated. Computed separately for household and business sectors. Used as a mechanism variable to test whether the U-shape crisis effect runs through financial booms.

• Spain micro panel (R8-R10). The loan-level analysis follows the Spain CIR approach to bank risk-taking and monetary policy of Jimenez et al. (2014) and the CIR loan-level methodology for separating credit supply from credit demand of Jimenez et al. (2012). Sample: 10% random sample of Spanish nonfinancial corporate loans from the Central de Informacion de Riesgos (CIR), quarterly 1995 Q1 to 2008 Q3, matched to bank supervisory data and firm Mercantile Register data. Credit growth regressions: 1.9 million bank-firm-quarter observations. Cost of debt regressions: 1.2 million firm-year observations. Default regressions: 1.1 million loan observations (sample ends at 2011 Q3 to allow three-year default look-ahead). Fixed-effect saturation reaches firm-time and bank-time FE in the most demanding specifications (Table XI col. 6, Table XII col. 5-6).

Datasets used

Dataset	Role in paper	Wiki page
Jorda-Schularick-Taylor (JST) Macrohistory Database	Monetary policy rates, banking crisis chronology, macro controls (GDP, inflation, credit, investment) for 17 advanced economies 1870-2020	no page yet
Greenwood et al. (2022) financial red zone data	Credit-to-GDP and asset price growth series to define the financial red zone mechanism variable	no page yet
Baron, Verner & Xiong (2021) BVX crisis chronology	Alternative crisis dates using bank equity returns for robustness	no page yet
Spain Central de Informacion de Riesgos (CIR)	Loan-level monthly data on all corporate loans by Spanish banks 1984 to 2008 Q3 (10% random sample used); credit volumes, maturities, defaults	no page yet
Spain Mercantile Register (Registro Mercantil)	Annual firm balance sheet and income statement data; financial expenses over liabilities as cost-of-debt proxy	no page yet
Banco de Espana bank supervisory data	Bank-level balance sheet characteristics (total assets, capital ratio, liquidity, ROA, NPL ratio) matched to CIR	no page yet
Quinn-Schindler-Toyoda (2011) KOPEN index	Rescaled capital account openness measure for trilemma IV construction	no page yet

Sample scope: macro panel covers 17 advanced economies (Australia, Belgium, Canada, Denmark, Finland, France, Germany, Italy, Japan, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, UK, US), annually 1870 to 2020 (77 crisis observations after data availability filters). Spain panel covers quarterly 1995 to 2008 Q3 (boom period) and defaults through 2011 Q3.

When to read the full paper

Read the original if you are: studying banking crisis predictors and want the full robustness battery (30+ Internet Appendix tables); extending the trilemma IV to other contexts; building on the financial red zone mechanism to study credit supply dynamics; analyzing the 2022-2025 rate-hiking cycle as a potential U-shape episode; or using the Spanish CIR administrative data methodology for loan-level identification of credit supply. Tables III and XII contain the key specifications; Figures 2 and 3 show the event-study path estimates.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 81(2). This distillation was extracted by an LLM on 2026-06-01 and is not human-verified or independently reproduced. The CC BY 4.0 licence permits mirroring; the verbatim PDF is not hosted in this batch.

Attribution (CC BY 4.0). Jimenez, Gabriel, Dmitry Kuvshinov, Jose-Luis Peydro, and Bjorn Richter. “Monetary Policy, Inflation, and Crises: Evidence from History and Administrative Data.” The Journal of Finance 81, no. 2 (April 2026): 923-970. DOI: 10.1111/jofi.70023. (C) 2026 The Author(s). Licensed under CC BY 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.