Insurers' investment behaviour and the coronavirus (COVID-19) pandemic
Bibliographic record
Abstract
This research explores two aspects of European insurers' investment behaviour related to crises. While they are often considered as financial market stabilisers and long-term investors, there is currently a lack of knowledge about insurers' investment behaviour in crises under the regulatory Solvency II regime implemented in 2016. With assets of nearly €9 trillion and bond holdings of more than €3 trillion in Q2 2022, European insurers are important financial intermediaries and finance European economies. With an empirical study, we investigate their reaction to the asset price shock at the onset of the coronavirus (COVID-19) pandemic in the first quarter of 2020 and explore cyclical investment behaviour by replicating Timmer's (2018) study with fixed effects panel regressions. We use a large cross-country dataset, with the novelty of exploiting cross-country heterogeneity for European countries with 458,758 security-level observations from 2017 to 2022. Overall, our findings are very relevant from a policy perspective as they suggest active and heterogeneous cyclical investment behaviour in the European insurance market with differences across issuer and holder countries of domicile.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".