How Credit Cycles across a Financial Crisis: Krishnamurthy & Muir (2025)

What this is. The paper’s core results, the theoretical framework it tests (the FZ model of financial crises), and the estimating specifications with exact source locators: enough to know what it found and how, without reading all 40 pages. To replicate or extend it, read the full source at the original.

TL;DR

This paper documents how credit spreads and credit growth co-evolve across financial crises in 17 countries from 1869 to 2022. Four stylized facts emerge: (i) crises begin with a sharp spike in spreads, signaling a sudden shift in expectations; (ii) the severity of the recession depends on the size of the spread spike (losses to financial intermediaries) interacted with precrisis credit growth (financial sector fragility); (iii) precrisis spreads are unusually low (“frothy”), not high, despite high credit growth; and (iv) the combination of low spreads and high credit growth is a meaningful predictor of future crises. These facts support fragility-amplification (FZ) theories of financial crises where credit supply expansions precede collapses, but the low-precrisis-spread finding challenges standard FZ models that predict rising spreads as fragility builds, pointing instead toward belief-formation models.

Core results

Magnitudes and significance are as reported; locators point into the source PDF. Standard errors (Driscoll-Kraay with 8 lags) in parentheses.

#	Result	Locator	Magnitude
R1	Spread spikes at crisis onset predict worse output: a 1-sigma spread increase in the crisis year reduces three-year cumulative GDP growth by ~2.2% beyond the average crisis path	Table IV col 2, p. 1354	$\Delta\hat{s}_{i,t} \times \mathbf{1}_{\text{crisis}}$ coeff: -2.21 (s.e. 0.74) at 3-yr; -1.48 (s.e. 0.60) at 5-yr
R2	Fragility amplifies spread shocks: one-sigma credit growth increases the output loss from a spread spike by ~2.1pp (3-yr) and ~2.6pp (5-yr)	Table IV col 3, p. 1354	$\Delta\text{cred}_{i,t} \times \mathbf{1}_{\text{crisis}}$ coeff: -2.06 (s.e. 0.95) at 3-yr; -2.65 (s.e. 1.10) at 5-yr
R3	Spread changes fatten the left tail of output: predictive power is concentrated in bad outcomes; coefficient nearly triples from 75th to 25th quantile	Table V, p. 1357	Quantile regression coefficient on $\Delta\hat{s}$: -0.36 at 75th pctile; -1.12 at 25th pctile
R4	Precrisis spreads are ~0.28-0.45 sigma too low: in the three to six years before a crisis, spreads are significantly below the country average; not observed before ordinary recessions	Table VII col 3-4, p. 1361	$\mathbf{1}_{t-6,t-4}$ coeff: -0.26 (s.e. 0.14) without year FE; -0.44 (s.e. 0.15) with year FE
R5	Low spreads + high credit growth predict crises: the HighFroth x HighCredit interaction raises the five-year crisis probability by 27pp in-sample, but weakens to 14pp out-of-sample	Table VIII col 3, p. 1363; Table X col 3, p. 1367	HighFroth x HighCredit OLS: 0.27 (s.e. 0.09) full sample; 0.14 (s.e. 0.07) OOS; froth does not predict nonfinancial recessions
R6	Results hold in postwar data: postwar-only (post-1950) estimates are economically similar to full-sample estimates, albeit with larger standard errors	Table XI col 2, p. 1368	$\Delta\hat{s} \times \mathbf{1}_{\text{crisis}}$ postwar: -1.43 (s.e. 0.53) vs. full-sample -2.21 (s.e. 0.74) at 3-yr
R7	Results robust to crisis dating chronology: coefficients on spread-change x crisis are economically and statistically similar across JST, Reinhart-Rogoff (2009), and Baron-Verner-Xiong (2021) crisis dates	Table XV, p. 1371	JST: -2.21 (s.e. 0.74); BVX: -2.47 (s.e. 0.71); RR: -2.36 (s.e. 0.48)

Overall (paper’s conclusion). The recessions surrounding financial crises are longer and deeper than nonfinancial recessions. The severity depends on the product of the spread spike (losses) and precrisis credit growth (fragility). Crises are preceded by frothy credit markets, indicating credit supply expansions, not demand. Standard FZ amplification models match the crisis-period facts but miss the low-precrisis-spread evidence. Models with time-varying beliefs (Moreira and Savov (2017); Krishnamurthy and Li (2025)) are better positioned to simultaneously fit both sets of stylized facts. The two prior-work pillars for this paper are Schularick and Taylor (2012), who established that credit booms predict crises and worse recoveries, and Baron and Xiong (2017), who showed that credit expansion coupled with an indicator of investor neglected crash risk predicts crises; this paper adds the credit-spread dimension to both. The crisis-dating framework relies on Jorda, Schularick, and Taylor (2011) (JST), and the risk-premium interpretation of spreads builds on Muir (2017), who documented that risk premia - not default probabilities - drive spread spikes in financial crises.

Theory / model

The paper does not develop a new formal model. It instead tests a class of theoretical models of financial crises, which the authors label the “FZ model” (fragility-amplification), encompassing Kiyotaki and Moore (1997), Gertler and Kiyotaki (2010), He and Krishnamurthy (2013), Brunnermeier and Sannikov (2014), and Moreira and Savov (2017).

The FZ structure. Denote credit losses (p. 1341) as $z_{i,t}$, with $\mathbb{E}_t[z_{i,t}] = 0$, for country $i$ at time $t$. Denote the financial sector fragility as $\mathcal{F}_{i,t}$. The severity of the crisis depends on $\mathcal{F}_{i,t} \times z_{i,t}$: a large shock to a fragile sector triggers bank runs, credit contraction, and deep recession. Credit spreads proxy for both losses and financial sector assets:

$$s_{i,t-1} = \gamma_{i,0} + \gamma_1 \text{Prob}^{\mathcal{Q}}(z_{i,t} > \underline{z}) \times \mathbb{E}_t^{\mathcal{Q}}[\text{Loss}_{i,t} | \text{crisis}] + \gamma_2 \text{Prob}^{\mathcal{Q}}(z_{i,t} \leq \underline{z}) \times \mathbb{E}_t^{\mathcal{Q}}[\text{Loss}_{i,t} | \text{no-crisis}] \tag{4}$$

(p. 1359), where $\text{Loss}_{i,t}$ is increasing in $\mathcal{F}_{i,t}$. Precrisis, as $\mathcal{F}_{i,t}$ rises, spreads should rise - but the data show they fall. The reconciliation offered is that $\text{Prob}^{\mathcal{Q}}(z_{i,t} > \underline{z})$, the risk-neutral probability of a large loss, falls as credit growth rises, more than offsetting the fragility effect on spreads. This is consistent with models in which agents underestimate crisis likelihood during credit booms (Moreira and Savov (2017); Gennaioli, Shleifer, and Vishny (2013); Bordalo, Gennaioli, and Shleifer (2018)).

The spread decomposition in a crisis. In a crisis, illiquidity/fire-sale effects cause $l_{i,t}$ (an illiquidity component of spreads) to spike, leading to unexpected losses:

$$s_{i,t} = \bar{\gamma}_i + \gamma_1 \mathbb{E}_t[\text{Loss}_{i,t}] + l_{i,t} \tag{\text{crisis}}$$

Outside crises, spreads are better represented without the liquidity spike component and are passive forecasters of output, consistent with existing findings in the literature (Friedman and Kuttner (1992); Gertler and Lown (1999); Gilchrist and Zakrajsek (2012)).

Method

The paper is a descriptive empirical study. The primary estimator is panel regression with Driscoll-Kraay standard errors allowing arbitrary serial correlation and cross-sectional dependence (8 lags), applied to an annual country-year panel. The paper also uses quantile regressions (Parente and Silva (2016)) and Logit specifications for crisis prediction.

Spread normalization. Raw spreads differ in level across countries (junk vs. investment grade). The paper normalizes by dividing by the country’s unconditional mean spread (p. 1349, equation 2):

$$\hat{s}_{i,t} \equiv \text{Spread}_{i,t} / \overline{\text{Spread}}^i \tag{2}$$

This mean normalization exploits the homogeneity assumption that the sensitivity of spreads to the cycle is proportional to the average spread. The normalization is validated by showing that the coefficient on spread/mean is similar across pre-1940 (IMM bond data, mean spread 5.21%) and post-1940 (multiple sources, mean spread 1.13%) subsamples (Table III, cols 6-7).

Fragility indicator. The high-credit-growth variable $\text{HighCredit}_{i,t}$ counts the number of years in the past three years in which annual credit growth exceeded the full-sample median (divided by 3, so range 0 to 1); when $\text{HighCredit}_{i,t} = 1$ credit has been above median in each of the last three years. The froth indicator $\text{HighFroth}_{i,t}$ takes the residual of spread normalized on lagged GDP growth and five-year lagged spreads, averages the below-median dummy over the past five years (p. 1362-1363).

Empirical specifications

Main crisis-interaction regression (produces R1, R2, R6, R7). Run on a panel of country-year observations with crisis and non-crisis dates (p. 1352, equation 3):

$$\ln\!\left(\frac{y_{i,t+k}}{y_{i,t}}\right) = a_i + a_t + \mathbf{1}_{\text{crisis},i,t} \times b_{\text{crisis}}' Z_{i,t} + \mathbf{1}_{\text{no-crisis},i,t} \times b_{\text{no-crisis}}' Z_{i,t} + c' x_t + \varepsilon_{i,t+k} \tag{3}$$

where $y_{i,t}$ is real per-capita GDP, $k \in \{3, 5\}$ years, $Z_{i,t}$ includes the normalized spread $\hat{s}_{i,t}$, the spread change $\Delta\hat{s}_{i,t}$, and credit growth $\Delta\text{cred}_{i,t}$, and $x_t$ includes two lags of GDP growth. Fixed effects: country ($a_i$) and year ($a_t$). Standard errors: Driscoll-Kraay with 8 lags. The key coefficient of interest is $b_{\text{crisis}}$ on $\Delta\hat{s}_{i,t}$ and its interaction with $\Delta\text{cred}_{i,t}$ (Table IV). Sample: 826 observations, 15 country groups.

Quantile regression (produces R3). Quantile regression of one-year output growth on $\Delta\hat{s}_{i,t}$ and $\Delta\text{Credit}_{i,t}$ at quantiles 90th, 75th, 50th, 25th, 10th (Table V, p. 1357). Controls: two lags of GDP growth, early-bond-data dummy, country and year fixed effects. Standard errors clustered by year. Sample: 826 observations, 826 at each quantile.

Precrisis-spread regression (produces R4). Regression of normalized spreads on crisis-time dummies from $t - 6$ to $t + 5$ (Table VII, p. 1361):

$$\hat{s}_{i,t} = a_i + \sum_{\tau=-6}^{5} \delta_\tau \mathbf{1}_{t=\tau} + \text{controls} + \varepsilon_{i,t}$$

with country fixed effects, early-bond-data dummy, and five-year lagged spread (controls for slow level changes). Standard errors: Driscoll-Kraay with 8 lags. The precrisis window $t-6$ to $t-4$ is combined into a single dummy in columns (3)-(4) to summarize the “too low” result.

Crisis prediction (produces R5). OLS and Logit specifications predicting the cumulative crisis indicator over a five-year horizon (whether any JST-mod crisis occurs in the next five years), on $\text{HighFroth}_{i,t}$, $\text{HighCredit}_{i,t}$, and their interaction (Tables VIII-IX, pp. 1363-1365). Country fixed effects, no time fixed effects (crisis prediction uses only pre-date information). Standard errors: Driscoll-Kraay with 8 lags (OLS), double-clustered by country and year (Logit). Out-of-sample version (Table X) constructs froth and credit variables in a rolling manner from 20 years after sample start.

All regressions exclude war periods (both world wars) because government intervention distorts bond prices and spread information.

Datasets used

Dataset	Role in paper	Wiki page
Investors Monthly Manual (IMM), London Stock Exchange bond data, 1869-1929	Corporate and sovereign bond yields for 17 countries; primary source for pre-1930 credit spreads	No page yet
Moody’s Baa-Aaa spread (US, 1920-2014)	US credit spread in the modern period	No page yet
Global Financial Data (GFD)	Corporate and government bond yields for Australia, Belgium, Canada, Germany, Norway, Sweden, UK, Korea	No page yet
Datastream	Bond yields for Ireland, Portugal, Greece (2000-2014); European spreads	No page yet
Banque de France nonfinancial corporate spreads (Germany, France, Italy, Spain, 1999-2022)	European corporate credit spreads relative to German Bunds	No page yet
Swiss National Bank (SNB) data	Switzerland spreads from 2001	No page yet
JST Macrohistory Database (Jorda, Schularick, Taylor (2017))	Crisis dates, credit-to-GDP, real GDP per capita for 17 advanced economies	JST Macrohistory (no page yet)
Barro-Ursua Macroeconomic Database	Real per-capita GDP; long historical series for advanced economies	Barro-Ursua

Sample: 17 countries; spread data 1869-2022 (by country); 1,006 country-year observations total for spreads; 40 financial crisis episodes with spread, credit, and output data available (the “JST mod.” crises). Annual frequency.

When to read the full paper

Read the original if you are: building a quantitative model of financial crises that needs empirical targets for spread dynamics and output losses; calibrating a fragility or amplification model to the interaction of losses x fragility; studying the credit-cycle predictor literature and want the international evidence on low-spread precrisis conditions; or interested in why standard FZ models with forward-looking spreads cannot match the frothy precrisis stylized fact. The tables are comprehensive: Table IV for the crisis-interaction results, Table VII for precrisis spread dynamics, Tables VIII-X for crisis prediction.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(3), June 2025. Published under Wiley terms and conditions (paywalled; not open access). This distillation was extracted by an LLM on 2026-06-06 and is not human-verified or independently reproduced. Redistribution: extract-only.

Krishnamurthy, Arvind, and Tyler Muir. “How Credit Cycles across a Financial Crisis.” The Journal of Finance 80, no. 3 (June 2025): 1339–1378. DOI: 10.1111/jofi.13431. © 2025 the American Finance Association.