Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
February 28, 2026European Actuarial JournalOpen Access

Expectiles as basis risk-optimal payment schemes in parametric insurance

View Full Paper
Ask AI
Bookmark
Share

Authors

MMMartin MaierTechnical University of MunichMSMatthias SchererTechnical University of Munich

Discussion

Loading...

Member takes

Implication

Analysis reveals expectiles minimize basis risk in parametric insurance, suggesting operational efficiency.

Key Points

  • The study aims to develop a payment scheme that minimizes basis risk in parametric insurance using expectiles.
  • Utilized an asymmetrically weighted mean square error framework.
  • Explored conditional expectiles related to actual losses.
  • Connected findings to stochastic orderings for better understanding.
  • Implemented regression approaches for practical application.
  • Identified conditional expectiles as basis risk-minimizing schemes for insurance contracts.
  • Demonstrated visualizations in the context of cyber risks and agricultural insurance.
  • Provided practical implementation methods that enhance operational efficiency.

Cite This Study

Maier et al. (2026) studied this question.

synapsesocial.com/papers/69a286950a974eb0d3c0199dhttps://doi.org/10.1007/s13385-026-00447-w
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1When indemnity insurance fails: Parametric coverage under binding budget and risk constraints2026 · 1 citations
  2. 2Combination of traditional and parametric insurance: calibration method based on the optimization of a criterion adapted to heavy tail losses2025
  3. 3Nonparametric Inference of Conditional Expectile Functions in Large‐Scale Time Series Data With Improved Efficiency2025
  4. 4Stackelberg equilibria in monopoly insurance markets with probability weighting2026
  5. 5Parametric insurance for hydropower: Comparing alternative schemes combining hydrologic and market data2024