Permanent Capital Losses after Banking Crises: Baron et al. (2026)

What this is. The paper’s core results, identification strategy, datasets, and empirical specifications: enough to know what it found and how. To replicate or extend, read the full source at the original.

TL;DR

The paper studies the mechanisms driving bank losses across historical banking crises in 46 economies and the effectiveness of policy interventions in restoring bank capitalization. It constructs several new historical datasets: a country-level panel of bank and nonfinancial equity returns (extending Baron, Verner and Xiong (2021), henceforth BVX), individual-bank-level balance sheet and income data for the 10 largest banks in 17 economies around each crisis (building on Jordà, Schularick and Taylor (2017), henceforth JST), and a comprehensive database of policy interventions extending Laeven and Valencia (2020). The central finding is that bank stock prices experience large, permanent declines at banking crisis onset, predicting commensurate long-run declines in banks’ earnings and dividends rather than elevated future equity returns. This earnings-driven pattern is inconsistent with models that posit bank losses are primarily due to temporary price dislocations or liquidity strains. Write-downs on nonperforming assets account for the bulk of realized bank losses; asset sales during panics contribute little on average. Liquidity-based interventions (central bank support, blanket liability guarantees) provide only a transient rebound in bank equity that reverses within 12-36 months. Historical government recapitalizations have been too small, delayed, and narrow to restore banking sector capitalization.

Core results

Magnitudes and significance as reported; * = 10%, ** = 5%, *** = 1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Bank equity has large, permanent abnormal declines at crisis onset; no elevated returns in years t+1 to t+5	Figure I, p. 685; Table II Panel A, p. 687	Average abnormal return: -68 log points (banks), -36 log points (nonfinancials) at crisis onset; bank cumulative abnormal return at t+3 = -0.263*** (s.e. 0.047); at t+5 = -0.043 (s.e. 0.079); nonfinancials at t+5 = +0.313*** (s.e. 0.081)
R2	Initial bank equity declines predict long-run earnings and dividend declines (earnings-driven, not discount-rate-driven)	Table III, p. 695	Country-level: coeff. on 5-yr log-change in earnings per share = 1.750*** (s.e. 0.408); dividends = 2.483*** (s.e. 0.545); bank-level: earnings = 0.981*** (s.e. 0.218); with crisis FEs = 0.792*** (s.e. 0.213)
R3	Even with perfect trough timing, bounce-back averages only ~30% of initial decline; gains reverse after ~12 months	Figure II, pp. 691-692	Peak bounce-back from trough = ~30% of initial bank equity decline; gains begin reversing after approximately one year; banks do not recover to precrisis levels by t+5
R4	Write-downs account for nearly all bank losses; trading losses from asset sales during panics are small on average	Figure IV, Table IV, pp. 703-706	Cumul. write-downs by t+5 = -0.338*** of precrisis book equity; write-downs account for approx. 100% of market-value losses by t+5; trading income = small fraction; banks with large securities portfolios show larger immediate trading losses
R5	Countries with larger bank equity declines exhibit higher subsequent NPL rates (consistent with asset quality as primary mechanism)	Figure V, p. 709	Adj. R² = 0.232 (full sample); adj. R² = 0.529 excluding USA 1990 and SWE 1991 outliers; relationship statistically significant across advanced economies
R6	Liquidity-based interventions yield only a transient ~20% bank equity rebound that reverses by months 12-36	Figure VI, pp. 711-712	Within two months of intervention, bank equity continues to decline; bank equity then rises by about ~20%, but this gain is short-lived; gains reverse between months 12 and 36; no persistent or large increase in bank capitalization from liquidity support or liability guarantees
R7	Government recapitalizations are small (~24% of book equity, ~43% of losses), delayed, and narrow	Table V, Figure VII, pp. 715-722	Mean recap size: ~24% of precrisis book equity; ~43% of market-value losses; 65% of programs narrow (few banks targeted); median delay ~8-9 months from panic start; bank market cap remains persistently lower 5 years after crisis
R8	Early liquidity interventions can avert incipient panics only before large equity declines occur; 75% of bank equity crises feature equity decline strictly preceding the panic	Section VI, pp. 725-728	Of 183 incipient liquidity shocks without prior bank equity decline: ~92 (~50%) averted by early intervention; of 76 bank equity crises: 57 (75%) show equity decline before any panic; essentially no bank equity crises averted by liquidity intervention after large equity decline has occurred

Overall (paper’s conclusion). Bank equity crises produce permanent capital losses driven by deteriorating asset quality and eventual write-downs, not temporary price dislocations. Forceful liquidity policy can avert crises when invoked before fundamental bank equity weakness occurs, but after large equity declines have materialized, neither liquidity support nor historical recapitalization programs have reliably restored banking sector capitalization.

Theory / model

The paper has no formal structural model. It organizes the analysis around two competing theoretical views and tests their empirical predictions:

Temporary-loss view. Several prominent models posit that bank losses during crises are primarily temporary. Under this view, crises are times when assets held by financial intermediaries trade at sharp discounts due to binding borrowing constraints or temporary illiquidity (e.g., Gertler and Kiyotaki (2015); He and Krishnamurthy (2012); Dang, Gorton, and Holmstrom (2020)). The temporary-loss view predicts: (i) elevated bank equity returns in the years following a crisis, as discount rates normalize; (ii) limited long-run declines in bank dividends and earnings; and (iii) large and lasting increases in bank equity following liquidity interventions by central banks.

Permanent-loss view. An alternative view holds that banking crises give rise to permanent bank losses through deterioration in asset quality (e.g., Kaminsky and Reinhart (1999); Calomiris and Mason (2003); Schularick and Taylor (2012)). Nonperforming assets and borrower defaults lead to asset impairments, permanently lowering bank equity and earnings. Banks are slow to recognize these losses in accounting statements, but equity markets price them at crisis onset. This view predicts: (i) no elevated future bank equity returns; (ii) commensurate long-run declines in earnings and dividends; (iii) persistent rise in nonperforming loan rates; and (iv) limited effectiveness of liquidity-based interventions.

The paper defines a “bank equity crisis” as the starting year in which both: (i) the bank equity index declines by more than 30% in any year within the past five years; and (ii) a top-20 bank (ranked by total assets) fails within a 0-5 year window around that decline. This is a real-time, objective indicator based entirely on public information available to market participants, designed to avoid the retrospective look-ahead bias in narrative crisis chronologies. The sample contains 76 bank equity crises across 46 economies over 1870-2019.

Method

The primary estimator is a panel regression with country (or bank) fixed effects and Driscoll-Kraay standard errors, which allow for arbitrary serial correlation and cross-sectional dependence across countries. This builds on driscoll-kraay-regression. The event-study-style analysis (event-study) traces cumulative coefficients for each horizon $h$ relative to the crisis onset.

Cumulative abnormal returns. For each crisis, the paper traces cumulative buy-and-hold abnormal returns using equation (1) (p. 684):

$$r_{i,t-k,t+h} = \alpha_i + \beta^h \text{BankEqCrisis}_{i,t} + \varepsilon_{i,t+h} \tag{1}$$

where $r_{i,t-k,t+h}$ is the cumulative log excess total return from year $t-k$ to $t+h$ for either the bank or nonfinancial equity index in country $i$; $\text{BankEqCrisis}_{i,t}$ equals one if country $i$ enters a crisis in year $t$; and $\alpha_i$ are country fixed effects. Setting $k=1$ normalizes cumulative returns relative to the year before crisis onset. The coefficient $\beta^h$ measures abnormal returns at horizon $h$. Standard errors are Driscoll-Kraay; 95% confidence intervals for $h \in [-5, 5]$ are plotted in Figure I (p. 685) and tabulated in Table II (p. 687). The key test: if $\beta^h \leq 0$ for $h > 0$, the temporary-loss view’s prediction of elevated post-crisis returns is rejected.

Earnings and dividends predictability. Equations (2a) and (2b) (p. 694) regress the log-change in real dividends or earnings per share from year $t-1$ to $t+5$ on the bank log excess total return in the crisis year, estimated conditional on the start of a bank equity crisis. At the country level:

$$\Delta y_{i,t-1,t+5} = \alpha_i + \beta r_{i,t-1,t} + \varepsilon_{i,t} \tag{2a}$$

At the individual-bank level (banks indexed by $b$):

$$\Delta y_{i,b,t-1,t+5} = \alpha_b + \beta r_{i,b,t-1,t} + \varepsilon_{i,b,t} \tag{2b}$$

A coefficient $\beta \approx 1$ means a 1 log-point initial equity decline predicts an approximately equal 1 log-point long-run decline in earnings or dividends, supporting the permanent-loss view. Standard errors are Huber-White at the country level (Panel A) and clustered by crisis episode at the bank level (Panel B), reported in Table III (p. 695).

Heterogeneity by market-to-book ratio. Equation (3) (p. 697) sorts banks into five bins by their market-to-book (M/B) ratio at crisis onset to assess cross-bank heterogeneity:

$$\Delta y_{i,b,t,t+5} = \alpha_i + \sum_k \beta_k \left(\frac{M}{B}\right)_{(\text{lower}_k,\, \text{upper}_k),\, i,b,t} + \varepsilon_{i,b,t} \tag{3}$$

The five bins are M/B ratios 0-0.2, 0.2-0.4, 0.4-0.6, 0.6-0.8, and above 0.8. Country fixed effects $\alpha_i$ are included. Results in Online Appendix Table A.10 show monotonically worse five-year outcomes for more distressed (low M/B) banks.

Book income decomposition. Cumulative abnormal book income is computed relative to each bank’s average precrisis income (years $t-4$ to $t-1$), normalized by aggregate precrisis book equity, then averaged across crises in 17 advanced economies. Income is decomposed into: (i) write-downs (revaluations of balance-sheet assets: loan loss provisions, impairments, goodwill write-downs); (ii) trading income (realized gains and losses from securities trading and all asset sales); and (iii) all other book income. Results in Table IV (p. 705) and Figure IV (p. 703).

Empirical specifications

All main regressions use annual data conditional on the start of a bank equity crisis. The primary country-level sample is 76 bank equity crises across 46 economies, 1870-2019; individual-bank analyses cover the 17 JST economies.

Returns analysis (R1). Equation (1) is estimated with $k=0$ (cumulative returns after crisis onset) and $k=1$ (normalized to the year before onset). Table II (p. 687) Panel A reports bank returns and Panel B nonfinancial returns for $h \in [1, 5]$, with panels C-D repeating the analysis using real total returns in place of excess returns. Driscoll-Kraay standard errors are used throughout; the within-R² is reported as a measure of explanatory power.

Earnings and dividends predictability (R2). Specifications (2a) and (2b) are estimated by OLS, with the sample restricted to country-years (or bank-years) at the onset of bank equity crises. The dependent variable is the log-change from year $t-1$ to $t+5$ in real earnings per share (columns 1-2 in Table III) or real dividends per share (columns 3-4). The independent variable is the past one-year bank log excess total return. For bank-level regressions (Panel B), even-numbered columns add crisis fixed effects, so that the coefficient captures within-crisis cross-bank heterogeneity (banks harder hit in a given crisis also show larger long-run earnings declines).

Income decomposition (R4). Cumulative abnormal income components are averaged across 37 crisis episodes for the write-down vs. all-other split (Panel A) and 21 crisis episodes for the three-way split (Panel B). Banks are sorted by the ratio of securities to total assets in year $t-1$ to form top and bottom quartile subsamples (Panels C and D of Figure IV, p. 703), examining whether banks with large tradeable-securities holdings experience more immediate trading losses during panics.

NPL cross-section (R5). Figure V (p. 709) plots the unlevered cumulative log excess total return of the bank equity index (from $t-1$ to $t+5$) against the peak NPL rate (maximum from $t$ to $t+5$) across crises in advanced JST economies with available NPL data. Unlevered returns (returns divided by banking sector book leverage) measure the implied market value of asset losses. A line of best fit with adjusted R² is shown.

Liquidity intervention event study (R6). Using monthly BVX equity index returns, equation (1) is re-estimated with the event month $t$ set to the first month of extraordinary central bank liquidity support or blanket bank liability guarantee announcement (whichever occurs first) for each crisis. Cumulative abnormal monthly returns are traced over a $\pm 60$ month window with Driscoll-Kraay standard errors (Figure VI, p. 711).

Crisis aversion probit (R8). A probit regression on the full sample of historical banking events (Online Appendix Table A.17) estimates predictors of crisis aversion using four binary variables: (i) small bank equity decline preceding the panic (below 30%), (ii) early liquidity intervention within one month of the panic, (iii) outbreak of war, and (iv) run initially focused on a single institution (Online Appendix Table A.18).

Datasets used

Dataset	Role in paper	Wiki page
BVX equity index data (Baron, Verner and Xiong (2021))	Country-level bank and nonfinancial equity index total returns for 46 economies, annual 1870-2016 and monthly; primary source for abnormal return analysis and crisis crisis dating	No page yet
JST Macrohistory Database (Jordà, Schularick and Taylor (2017))	Individual-bank balance sheets, income statements, and equity returns for the 10 largest banks in each of 17 economies around each crisis; source for write-down decomposition and bank-level regressions	JST Macrohistory
BSZ database (Baron, Schularick and Zimmermann (2024))	Identities and annual balance sheets of the top-20 banks in 17 economies since 1870; used to identify bank failures and implement the real-time crisis definition	No page yet
Policy interventions database (new, this paper)	Monthly starting dates of extraordinary central bank liquidity support, blanket liability guarantees, and government recapitalizations across all 76 bank equity crises; extends Laeven and Valencia (2020) and Metrick and Schmelzing (2024)	No page yet
Ari, Chen and Ratnovski (2021) NPL data	Peak nonperforming loan rates for advanced economies used in the cross-crisis NPL scatter (Figure V)	No page yet

Sample: 46 economies, annual, 1870-2019 for the main returns analysis; 17 JST economies for individual-bank income decompositions and policy-effectiveness analyses. BVX monthly data are used for short-horizon bounce-back (Figure II) and intervention event studies (Figure VI). Replication data are available at Harvard Dataverse: https://doi.org/10.7910/DVN/NCUHLW.

When to read the full paper

Read the original if you are:

• Building or calibrating a structural model of banking crises: Tables II-IV provide empirical targets for bank equity dynamics, earnings declines, and write-down timing at both the country and individual-bank levels.
• Assessing the effectiveness of lender-of-last-resort policy vs. recapitalization programs: Sections V and VI cover both types of intervention with historical evidence; Table V documents recapitalization size, speed, and breadth across all 17 JST economies since 1870.
• Studying heterogeneity in crisis outcomes: Sections III.D-E compare banks sorted by M/B ratio; Section VI contrasts bank equity crises to panic-only crises and averted crises.
• Using the historical banking crisis or policy intervention database: Online Appendix Table A.1 lists all 76 bank equity crisis episodes with policy intervention dates; Table V lists individual government recapitalization programs with size, timing, and breadth statistics.

Attribution and rights

Source: peer-reviewed, The Quarterly Journal of Economics (2026), pp. 667-732. This distillation was extracted by an LLM on 2026-06-28 and is not human-verified or independently reproduced. The work is paywalled; reproduction is extract-only.

Baron, Matthew, Luc Laeven, Julien Pénasse, and Yevhenii Usenko. “Permanent Capital Losses after Banking Crises.” The Quarterly Journal of Economics (2026): 667-732. DOI: 10.1093/qje/qjaf052.