What the study found
The study found that basis risk-minimizing payment schemes for pure parametric and parametric index insurance contracts can be written as conditional expectiles of policyholders’ true loss, given a compensation-triggering incident. Expectiles are asymmetric averages that weight losses and gains differently.
Why the authors say this matters
The authors say this is relevant because parametric insurance is operationally efficient and cost effective, but it can create basis risk, meaning payouts may differ from actual damage. The study suggests a framework for reducing that mismatch while keeping the parametric structure.
What the researchers tested
The researchers worked in an asymmetrically weighted mean square error framework. They analyzed pure parametric and parametric index insurance contracts, connected the results to stochastic orderings, and used regression approaches to show how the ideas could be implemented in practice.
What worked and what didn't
The study reports that conditional expectiles characterize the basis risk-minimizing payment schemes in the setting they consider. It also says regression approaches allow easy implementation in practice. The results were visualized for parametric coverage in cyber risks and agricultural insurance.
What to keep in mind
The abstract does not describe empirical testing beyond the visualized examples in cyber risks and agricultural insurance. It also does not state limitations beyond the scope of the contracts and framework analyzed.
Key points
- Basis risk in parametric insurance is the gap between payouts and actual damage.
- The study links basis risk-minimizing payments to conditional expectiles.
- The framework covers pure parametric and parametric index insurance contracts.
- Regression approaches are presented as a practical way to implement the results.
- Examples are visualized for cyber risks and agricultural insurance.
Disclosure
- Research title:
- Expectiles minimize basis risk in parametric insurance payments
- Authors:
- Martin Maier, Matthias Scherer
- Institutions:
- Technical University of Munich, Technical University of Munich
- Publication date:
- 2026-02-26
- OpenAlex record:
- View
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