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 all 26 pages. 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. In the pre-election period, suppressed negative information and expansionary fiscal policies push systemic risk downward (-2.19%); 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	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. 9-11	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. 10-11	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 + insurance + investment trusts)	Table 6 Model (1), p. 14	ELECTIONS: 0.043*** (SE 0.006); sample of 697 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 policy tightening mitigates election-related systemic risk	Table 10 Model (2), p. 17	MP TIGHTENING × ELECTIONS: -0.121*** (SE 0.026); effect remains positive in tightening years but is significantly reduced
R8	2SLS with term limits IV confirms positive election effect, addressing reverse causality	Table 13 Models (2)(4), pp. 18-19	2SLS ELECTIONS: 0.045*** (SE 0.011); first stage: ELECTIONS on TERM LIMITS 0.685*** (SE 0.007); consistent with OLS

Overall (paper’s conclusion). Elections are associated with a robust and time-varying increase in bank systemic risk. The hump-shaped trajectory (lower pre-election, peak around election time and first two post-election quarters, gradual decline over 12 months) is driven primarily by turnover episodes: in re-elected cases the post-election rise is smaller and shorter-lived. Multiple transmission channels are evidenced: stock market volatility (VIX interaction), suppressed stock price informativeness (reduced transparency), expansionary fiscal policies before elections, and declining trust in government. 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).

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):

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

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):

$$\Delta\text{CoVaR}^{si} = \widehat{\text{CoVaR}}^{si}_{q=0.05} - \widehat{\text{CoVaR}}^{si}_{q=0.5} \tag{8}$$

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:

$$\text{SRISK}_{i,t} = k \cdot \text{DEBT}_{i,t} - (1 - k) \cdot W_{i,t} \cdot (1 - \text{LRMES}_{i,t}) \tag{15}$$

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:

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

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 $c$. $X_{L,t-1}$ 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). Pre- and post-election dynamics are examined by replacing ELECTIONS with PRE (year before elections) and POST (year after) in Table 4.

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.

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), confirming that market-sentiment amplifies election effects; (2) PRICE_INFO (stock price informativeness, measured as the country average bid-ask spread, lower = more informative): 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), confirming strong economic growth mitigates the effect; (4) GOV.EXP × ELECTIONS: -0.002* (SE 0.001), fiscal expansion partially buffers. These results tie back to the hypotheses in Section 2 and establish that multiple 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 by an LLM on 2026-06-25 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). 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.