Cyber risk, market fear and financial instability: hunting down risk through contagion analysis
Bibliographic record
Abstract
Purpose This study examines how cyber risk propagates through the financial system and contributes to systemic financial instability. It further explores the role of market sentiment, particularly fear triggered by cyber incidents, as a key channel amplifying the contagion of cyber risk across different financial segments. Design/methodology/approach We apply a quantile vector autoregressive (QVAR) connectedness approach to capture the asymmetric and state-dependent transmission of cyber risk shocks under varying market conditions (bearish, normal, bullish). The analysis uses daily data from January 2, 2020, to May 31, 2022, covering four financial instability indicators from the US Office of Financial Research (OFR), a Twitter-based global cyber risk index and CNN’s Fear and Greed Index. To ensure robustness, we complement the quantile analysis with a Time-Varying Parameter VAR (TVP-VAR) model, which confirms the stability of the identified spillover patterns over time. Findings The results show that cyber risk is both a transmitter and an amplifier of financial instability. Its impact is particularly strong during bearish conditions, where it exacerbates funding liquidity stress and banking vulnerability. Market sentiment emerges as a powerful transmission mechanism that intensifies the contagion of cyber risk across financial sectors. Cyber risk also reacts to instability and fear, reinforcing systemic feedback loops. Practical implications Our findings highlight the urgent need to integrate cyber risk into macroprudential frameworks and financial stability monitoring. Policymakers should develop international coordination mechanisms, including shared cyber risk assessment protocols, cross-border crisis management procedures and dedicated platforms for real-time information exchange. Regulatory authorities must also consider sentiment-sensitive liquidity support tools to limit the amplification of shocks during periods of heightened cyber anxiety. Originality/value This paper is among the first to examine the dynamic relationship between cyber risk, financial instability and market sentiment using a quantile connectedness framework. It highlights the dual role of cyber risk, both as a transmitter and a receiver of shocks within the financial system depending on prevailing market conditions. Furthermore, the study offers novel policy insights into managing cyber-induced contagion under different market regimes, particularly by integrating behavioral and systemic dimensions into financial stability frameworks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".