Election Cycles and Systemic Risk: Kladakis & Skouralis (2026)

What this is. The paper’s core results, the ΔCoVaR methodology it applies (from Adrian and Brunnermeier 2016), and the panel regression specification: enough to know what it found and how, without reading the full paper. To replicate or extend it, read the full source at the original.

TL;DR

Kladakis and Skouralis examine whether national elections are associated with higher bank systemic risk using a panel of 193 banks from 22 OECD economies, covering 147 elections over 2000-2023. Systemic risk is measured by ΔCoVaR (the additional tail risk to the financial system when an institution is in distress), following Adrian and Brunnermeier (2016). The central finding is a robust, time-varying relationship: bank ΔCoVaR rises by approximately 3.57% above the overall mean in the election year, but the effect is heterogeneous across the electoral cycle. For scheduled elections, suppressed negative information and expansionary fiscal policies are associated with lower systemic risk in the pre-election period (-2.19%); snap-election risk can rise before the election. The surge occurs at election time and in the post-election period. Snap elections drive larger increases than scheduled end-of-term elections, incumbent turnover amplifies the effect while re-election dampens it, common-law countries show a stronger response than civil-law jurisdictions, and macroprudential policy tightening can partially offset the election-driven rise in systemic risk. Results are robust to alternative systemic risk measures (MES, SRISK), instrumental-variable estimation (term limits; Google Trends uncertainty index), exclusion of banking-crisis years, and monthly data.

Core results

Magnitudes and significance are as reported; \*\*/\*\*\* = 5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Elections raise bank systemic risk in the election year	Table 3 Model (3), p. 11; text p. 8	ELECTIONS: 0.062*** (SE 0.012); the election year is associated with ΔCoVaR 3.57% above the overall mean (mean = 1.737%)
R2	Pre-election period: systemic risk falls; post-election period: systemic risk rises	Table 4 Models (1)(4), p. 12	PRE (year before election): -0.032*** (SE 0.009); POST (year after election): +0.037*** (SE 0.010)
R3	Snap elections drive larger systemic risk increases than end-of-term elections	Table 3 Models (4)(5), p. 11; text p. 9	SNAP: 0.084*** (SE 0.023); END-OF-TERM: 0.053*** (SE 0.013); snap elections increase ΔCoVaR by 4.83% vs 3.05% for end-of-term (relative to mean)
R4	Incumbent turnover amplifies effect; re-election dampens it	Table 3 Model (6), p. 11	NEW GOV: 0.084*** (SE 0.017); RE-ELECTED: 0.043** (SE 0.018); new government coefficient is roughly twice that under re-election
R5	Effect extends to all financial institutions (banks, insurers, investment funds, and other financial firms)	Table 6 Model (1), p. 14	ELECTIONS: 0.043*** (SE 0.006); regression sample of 693 institutions; snap: 0.056***, end-of-term: 0.042***
R6	Common-law countries show a stronger elections-systemic risk link than civil-law countries	Table 9 Model (1), p. 17	LEGAL ORIGIN × ELECTIONS: 0.049*** (SE 0.022); ELECTIONS main coefficient: 0.043*** (SE 0.014)
R7	Macroprudential tightening is associated with a weaker election-risk link	Table 10 Model (2), p. 17	MP TIGHTENING × ELECTIONS: -0.121*** (SE 0.026); the interaction is negative, but the combined marginal election effect is not separately tested
R8	2SLS with term limits IV confirms positive election effect, addressing reverse causality	Table 13 Models (1)-(4), p. 19	2SLS ELECTIONS: 0.045*** (SE 0.011); first stage: ELECTIONS on TERM LIMITS 0.685*** (SE 0.007); consistent with OLS
R9	Market volatility amplifies the election-risk association	Table 8 Model (1), p. 16	VIX × ELECTIONS: 0.073*** (SE 0.025); VIX: 0.730*** (SE 0.038); ELECTIONS: -0.037 (SE 0.026)
R10	Price informativeness moderates the election-risk association	Table 8 Model (2), p. 16	PRICE_INFO × ELECTIONS: -0.001*** (SE 0.000); PRICE_INFO: 0.002*** (SE 0.001)
R11	Economic growth buffers election-related systemic risk	Table 8 Model (3), p. 16	GDP growth × ELECTIONS: -0.040*** (SE 0.005); GDP growth: -0.019*** (SE 0.005)
R12	Government expenditure expansion weakens election-related systemic risk	Table 8 Model (4), p. 16	GOV. EXP. × ELECTIONS: -0.002* (SE 0.001); GOV. EXP.: 0.018*** (SE 0.005)
R13	Trust in government mitigates the election-risk effect	Table 8 Model (5), p. 16	TRUST × ELECTIONS: -0.287*** (SE 0.063); TRUST: -0.423*** (SE 0.079)
R14	Legal-origin interaction is robust to correlated random effects	Table 9 Models (3)-(4), p. 17	COMMON LAW × ELECTIONS: 0.060** (SE 0.038) for banks; 0.047*** (SE 0.012) for all financials
R15	Macroprudential tightening lowers risk; expansion is not consistently significant	Table 10 Models (1)-(4), p. 17	MP TIGHTENING: -0.131*** (SE 0.029) for banks; -0.061*** (SE 0.013) for all financials; MP EXPANSION: 0.014 (SE 0.057), not significant
R16	Election effects persist across alternative systemic-risk measures	Table 11 Models (1)-(4), p. 18	MES: 0.064** (SE 0.028) for banks and 0.049*** (SE 0.014) for all financials; SRISK: 0.014** (SE 0.005) for banks and 0.010* (SE 0.005) for all financials
R17	Election effect remains with EPU controls, while high EPU amplifies it	Table 12 Models (1)-(7), p. 18	ELECTIONS: 0.048*** (SE 0.007) in Model (1); HIGH EPU × ELECTIONS: 0.080*** (SE 0.015) in Model (6); lagged lnEPU: 0.002 (SE 0.021), not significant
R18	The election-risk estimate remains positive after banking-crisis years are removed	Table 14 Models (1)-(4), p. 21	ELECTIONS: 0.016** (SE 0.007) with Harvard crisis data and controls; 0.011* (SE 0.006) with Metrick-Schmelzing data and controls
R19	Monthly estimates retain a positive election effect and show distinct pre/post dynamics	Table 15 Models (3)-(4), p. 21	ELECTIONS: 0.113*** (SE 0.014); PRE (3 months): -0.028** (SE 0.006); POST (3 months): 0.094*** (SE 0.009)
R20	Google Trends instrument specifications confirm the election effect	Table 13 Models (5)-(8), p. 19	ELECTIONS: 0.063*** (SE 0.011) without controls; 0.060*** (SE 0.011) with controls; GT Political Uncertainty first stage: 0.356*** (SE 0.008)
R21	Snap-election monthly dynamics are stronger; post-election re-election interaction is null	Table 15 Models (5)-(6), p. 21	PRE × REELECTED: -0.023*** (SE 0.008); POST × REELECTED: 0.016 (SE 0.011), not significant; PRE × SNAP: 0.065*** (SE 0.014); POST × SNAP: 0.093*** (SE 0.019)
R22	In the extended financial-institution sample, incumbent re-election has no significant association while turnover does	Table 6 Model (4), p. 14	RE-ELECTED: 0.008 (not significant); NEW GOVT: 0.093*** (SE 0.011)

Overall (paper’s conclusion). Elections are associated with a robust and time-varying increase in bank systemic risk. The pre-election decline is clearest for scheduled elections; snap-election systemic risk is also elevated beforehand. The hump-shaped trajectory peaks around six months after elections and gradually declines over 12 months. Its persistence is driven primarily by turnover episodes: in re-elected cases the post-election rise is smaller and shorter-lived. The paper attributes the pre-election decline to suppressed negative information and expansionary fiscal policies, while stock-market volatility is associated with greater election-period risk. Macroprudential tightening, strong economic growth, and high public trust in government partially buffer the effect.

Theory / model

The paper proposes no formal economic model. It develops six testable hypotheses grounded in the prior literature on political uncertainty and financial markets.

Competing hypotheses on election-period systemic risk.

• H1a: Election periods are associated with increased systemic risk (via heightened policy uncertainty, reduced information disclosure, and amplified market volatility).
• H1b: Election periods are associated with reduced systemic risk (via uncertainty resolution, improved investor confidence when a competent government is expected, and credible policy commitments).
• H2: Snap elections are associated with increased systemic risk relative to scheduled elections (owing to their unexpected nature and greater uncertainty about outcomes).
• H3: Re-election of the incumbent is associated with reduced systemic risk (via continuity and reduced policy risk).
• H4a/H4b: Systemic risk is increased/reduced in the pre-election period (depending on whether anticipatory political risk or information suppression and fiscal stimulus dominate).
• H5: The impact of elections on systemic risk is stronger in common-law countries (where more market-based financial systems transmit political shocks more directly through asset prices and intermediaries).
• H6: Macroprudential policy can mitigate election-related systemic risk (by strengthening system resilience against political-economic shocks).

Identification logic. The core identification challenge is that systemic risk may itself influence election timing (reverse causality: distressed governments may call early elections, or delay elections to avoid political punishment). The prior literature documents that Bialkowski et al. (2008) find country-specific stock market volatility roughly doubles in the week around a national election, and Matousek et al. (2020) show that policy uncertainty exerts a significant and persistent impact on bank capital shortfall, peaking around 11 months after elections, providing direct motivation for the systemic-risk focus here. The paper addresses this via (i) the argument that national election schedules in parliamentary democracies are largely exogenous to individual bank risk (particularly for scheduled end-of-term elections); (ii) a 2SLS approach using term limits (Jens 2017) as an instrument for election occurrence, which is predetermined and uncorrelated with contemporaneous financial conditions; and (iii) an additional instrument based on Google Trends election search intensity. The authors also exclude years in which banking crises occurred (Harvard Global Crisis Data; Metrick and Schmelzing 2021) and run a separate sub-sample restricted to US presidential elections, which occur at fixed intervals.

Method

The paper applies the ΔCoVaR methodology of Adrian and Brunnermeier (2016) to construct the systemic risk measure. The estimation has three steps (Eqs. 4-8, p. 6). The underlying tail-risk definitions are Eqs. 1-3 (p. 5):

$$P(R^i_t < \text{VaR}^i_t) = q \tag{1}$$ $$P(R^s_t < \text{CoVaR}^{s|i}_t \mid R^i_t = \text{VaR}^i_t) = q \tag{2}$$ $$\Delta\text{CoVaR}^{s|i}_t = \text{CoVaR}^{s|i}_{q=0.05} - \text{CoVaR}^{s|i}_{q=0.5} \tag{3}$$

Step 1: institution-level VaR. For each financial institution $i$, run a quantile regression of weekly returns $R^i_t$ on state variables $S_{t-1}$ (stock market returns, short-term government bond yield change, and the 10Y-to-short-term yield spread) at the distress quantile $q = 0.05$ (Eq. 4, p. 6):

$$R^i_t = a_q + \beta_q S_{t-1} + \varepsilon_{q,t} \tag{4}$$ $$\widehat{\text{VaR}}^i_{q,t} = \hat{a}_q + \hat{\beta}_q S_{t-1} \tag{5}$$

Step 2: system CoVaR. For the country-level financial system index (returns $R^{\text{system}}_t$), run a second quantile regression conditioning on institution $i$‘s return (Eq. 6, p. 6):

$$R^{\text{system}}_t = a_q^{\text{system}|i} + \beta_q^{\text{system}|i} S_{t-1} + \gamma_q^{\text{system}|i} R^i_t + \varepsilon_{q,t} \tag{6}$$ $$\widehat{\text{CoVaR}}^{\text{system}|i}_{q,t} = \hat{a}_q^{\text{system}|i} + \hat{\beta}_q^{\text{system}|i} S_{t-1} + \hat{\gamma}_q^{\text{system}|i} \widehat{\text{VaR}}^i_{q,t} \tag{7}$$

Step 3: ΔCoVaR. The systemic importance measure is the difference between the system’s CoVaR when institution $i$ is at its distress level ($q = 0.05$) and when it is at its median ($q = 0.5$) (Eq. 8, p. 6):

$$\Delta\text{CoVaR}^{\text{system}|i}_{q,t} = \widehat{\text{CoVaR}}^{\text{system}|i}_{q=0.05,t} - \widehat{\text{CoVaR}}^{\text{system}|i}_{q=0.5,t} \tag{8}$$

The system index $R^{\text{system}}_t$ is the return of the DS Financials country index from Thomson Reuters EIKON Datastream, which includes large listed financial institutions in each country. All data are weekly. The resulting annual average of ΔCoVaR is the dependent variable in the panel regressions.

The paper also uses two alternative systemic risk measures for robustness (Table 11, p. 18): Marginal Expected Shortfall (MES) from Acharya et al. (2017), and SRISK from Brownlees and Engle (2017). MES is the expected equity loss of institution $i$ when the market experiences an extreme loss. SRISK is the expected capital shortfall conditional on a systemic event:

MES is defined conditionally on market distress at the fifth percentile (Eq. 10, p. 15), and the authors also express it as the marginal contribution to system expected shortfall (Eq. 11, p. 15):

$$\text{MES}_{i,t} = E[R_{i,t} \mid R^M_t \leq C = \text{VaR}^M] \tag{10}$$ $$\text{ES}_{i,t}(C) = \frac{\partial \text{ES}_{m,q}(C)}{\partial w_{i,t}} = E_{t-1}[R_{i,t} \mid R_{m,t} \leq C] \tag{11}$$

The SRISK construction begins from firm capital shortfall (Eq. 12, p. 15), then takes its expectation conditional on a systemic event (Eq. 13) and separates debt and equity components (Eq. 14):

$$\text{Capital Shortfall}_{i,t} = k A_{i,t} - \text{MCap}_{i,t} \tag{12}$$ $$\text{SRISK}_{i,t} = E_t[\text{Capital Shortfall}_{i,t} \mid R_{t+1:t+h} < T] \tag{13}$$ $$\text{SRISK}_{i,t} = k E_t[D_{i,t+h} \mid R_{t+1:t+h} < T] - (1-k)E_t[W_{i,t+h} \mid R_{t+1:t+h} < T] \tag{14}$$ $$\text{SRISK}_{i,t} = k \cdot \text{DEBT}_{i,t} - (1 - k) \cdot W_{i,t} \cdot (1 - \text{LRMES}_{i,t}) \tag{15}$$

where $k = 0.08$ is the prudential capital fraction, $W_{i,t}$ is market capitalization, and $\text{LRMES}_{i,t}$ is the long-run marginal expected shortfall (p. 15, Eq. 15).

Empirical specifications

Benchmark panel regression. The headline specification (Eq. 9, p. 7) is a panel fixed- effects regression of annual ΔCoVaR on an election dummy and controls:

$$\Delta\text{CoVaR}^{s|i}_t = \beta_0 + \beta_1 \text{ELECTIONS}_{c,t} + \beta_2 X_{i,t-1} + \beta_3 M_{c,t-1} + \alpha_t + \alpha_i + \varepsilon_{i,t} \tag{9}$$

where subscripts $t$, $i$, $c$, and $s$ refer to year, firm, country, and financial- system index. $\text{ELECTIONS}_{c,t} = 1$ in years when national elections occurred in country $c$. $X_{i,t-1}$ is a vector of lagged firm controls: log total assets (size), VaR (idiosyncratic risk), leverage (total debt to market-cap ratio), and ROE (profitability). $M_{c,t-1}$ is a vector of lagged country-level controls: GDP growth, inflation, real house price growth, and credit growth to non-financials. $\alpha_i$ and $\alpha_t$ are firm and year fixed effects. Standard errors are clustered at the firm level.

The sample is 193 banks from 22 OECD countries, yielding 3,827 firm-year observations in the full-control specification (Table 3 Model 3, p. 11). The ELECTIONS dummy is split into SNAP (elections called before end of term) and END-OF-TERM (within six months of term limit) to test H2, and into RE-ELECTED and NEW GOV based on the electoral outcome to test H3 (Table 3, Models 4-7, p. 11). Pre- and post-election dynamics are examined by replacing ELECTIONS with PRE (year before elections) and POST (year after) in Table 4. Table 8 tests the proposed channels with one interaction at a time, adding the channel variable and its product with elections:

$$\Delta\text{CoVaR}_{i,t} = \beta_0 + \beta_1 \text{ELECTIONS}_{c,t} + \beta_2 Z_{c,t} + \beta_3 (Z_{c,t} \times \text{ELECTIONS}_{c,t}) + \boldsymbol{\gamma}'X_{i,t-1} + \boldsymbol{\delta}'M_{c,t-1} + \alpha_i + \alpha_t + \varepsilon_{i,t}$$

The channel variable $Z$ is VIX, PRICE_INFO, GDP growth, government expenditure, or trust in government, estimated in separate specifications. Tables 8-10 use firm and year fixed effects and robust standard errors clustered by firm; samples range from 3,827 to 12,431 firm-years, depending on the subsample and available controls. Table 9 omits firm fixed effects to estimate legal-origin levels, then reports random-effects and correlated-random-effects specifications.

2SLS instrumental-variable specification. To address reverse causality (Table 13, p. 19), ELECTIONS is instrumented by two variables. The first is TERM LIMITS: a dummy equal to one if the country’s constitution or law prohibits the incumbent government from seeking re-election; Jens (2017) shows term limits are strongly correlated with election timing but unrelated to financial conditions. The second is a Google Trends political uncertainty index (GT Political Uncertainty dummy): the equally-weighted sum of standardized search volumes for election-related terms, equal to one if the index exceeds the upper quartile of its country distribution. First-stage coefficient on TERM LIMITS: 0.685*** (SE 0.007); on GT Political Uncertainty dummy: 0.356***. Second-stage ELECTIONS coefficient: 0.045*** (SE 0.011, Table 13 Model 2), close to the OLS estimate of 0.062.

The first and second stages use the same firm and year fixed effects and firm-clustered robust standard errors (Table 13, p. 19):

$$\text{ELECTIONS}_{c,t} = \pi_0 + \pi_1 Z_{c,t} + \pi_2 X_{i,t-1} + \pi_3 M_{c,t-1} + \alpha_i + \alpha_t + u_{i,t}$$ $$\Delta\text{CoVaR}^{s|i}_t = \beta_0 + \beta_1 \widehat{\text{ELECTIONS}}_{c,t} + \beta_2 X_{i,t-1} + \beta_3 M_{c,t-1} + \alpha_i + \alpha_t + \varepsilon_{i,t}$$

Here the instruments are TERM LIMITS in Models 1-4 and TERM LIMITS together with the Google Trends Political Uncertainty dummy in Models 5-8. Table 13 reports 693 financial institutions without controls, and 691 institutions in the controlled first and second stages; robust standard errors are clustered at the firm level.

For monthly estimates (Table 15 and Figs. 5-6, pp. 21-22), the fixed-effects design uses monthly systemic risk, monthly industrial production and inflation controls, and firm and time effects where specified. Table 15 reports a three-month pre-election window and a three-month post-election window, with interactions for re-election and snap elections. Figures 5-6 plot cumulative windows from three quarters before through six quarters after the election.

The monthly table covers 162,684 observations without controls and 135,692 in the dynamic specifications with controls (Table 15, p. 21); robust standard errors are clustered by firm. Its outcome and election-type specifications interact the pre/post windows with REELECTED and SNAP, respectively.

Transmission-channel tests. Table 8 (p. 16) introduces four transmission-channel interaction terms one at a time, each interacted with ELECTIONS: (1) VIX (stock market volatility index): positive and significant interaction (VIX × ELECTIONS: 0.073***, SE 0.025), consistent with market volatility amplifying election effects; (2) PRICE_INFO (the year-on-year change in the country average bid-ask spread; higher spreads indicate lower informativeness): negative interaction (PRICE_INFO × ELECTIONS: -0.001***, SE 0.000), consistent with reduced informativeness dampening the election-risk link; (3) GDP growth × ELECTIONS: -0.040*** (SE 0.005), consistent with strong growth buffering the effect; (4) GOV.EXP × ELECTIONS: -0.002* (SE 0.001), fiscal expansion partially buffers. These interaction estimates are consistent with the proposed channels, but the paper estimates them separately and does not establish that all channels operate simultaneously.

Datasets used

Dataset	Role in paper	Wiki page
Thomson Reuters EIKON Datastream (DS Financials index)	Weekly stock return data for all financial institutions; country financial system index; firm-level total assets, ROE, leverage, VaR	no page yet
OECD database	Country-level GDP growth and inflation (year-on-year)	no page yet
BIS	Real residential property price growth; credit growth to non-financial sector	no page yet
iMaPP (IMF / Alam et al. 2019)	Macroprudential policy indicators: countercyclical capital buffer, LTV, LTD, DSTI, stress tests, SIFI measures; annual aggregate at country level	no page yet
National election databases (22 OECD countries)	Date, type (snap vs. end-of-term), and outcome (re-elected vs. new government) of 147 national elections, 2000-2023; collected by the authors from national sources	no data: tag (hand-collected)
Harvard Global Crisis Data (Reinhart-Rogoff 2014)	Banking crisis dates for banking-crisis robustness exclusion	no page yet
Metrick-Schmelzing (2021) banking crisis dataset	Alternative banking crisis dates for robustness	no page yet
Baker et al. (2016) EPU index	Economic Policy Uncertainty index; robustness subsample of 12 OECD countries	no page yet

Sample: 2000-2023 (annual), 22 OECD countries, 193 banks in the main sample (3,827 firm-years). ΔCoVaR estimation uses weekly returns. Extended sample includes 697 financial institutions (banks, insurance companies, financial services companies, investment trusts). Macroeconomic controls are from OECD (GDP, inflation) and BIS (house prices, credit).

When to read the full paper

Use the original if you are: studying how political events transmit into tail risk measures (Tables 3-5 give the full decomposition by election type and outcome); building a stress-testing framework that incorporates election cycles (Section 4.7 / Table 10 on the macroprudential buffer channel is the most policy-relevant section); extending the ΔCoVaR approach to other political events; or investigating legal-origin heterogeneity in political-financial transmission (Table 9 and Section 4.6). Figure 5 (p. 22) shows the monthly impulse-response of systemic risk around elections and Figure 6 replicates it on US data alone.

Attribution and rights

Source: peer-reviewed, Journal of Banking and Finance 187 (2026) 107676. This distillation was extracted and checked against the PDF by LLMs on 2026-06-25 and 2026-10-04; it 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). Kladakis, George, and Alexandros Skouralis. “Election cycles and systemic risk.” Journal of Banking and Finance 187 (2026) 107676. DOI: 10.1016/j.jbankfin.2026.107676. © 2026 The Author(s). Published by Elsevier B.V. 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.