Parametric insurance: what the theory gets wrong about practice

Parametric insurance: what the theory gets wrong about practice

A big strand of recent academic work models parametric insurance as a very general indemnity function of many indices – weather variables, satellite metrics, macro factors, you name it.

Mathematically, that’s elegant:

• You get a rich state space

• You can define an optimal contract in terms of basis risk

• You can use all the toys of convex / linear / even non-convex optimisation and ML

But as a description of real parametric products, this framing is often misleading. It abstracts away the very features that make parametric insurance commercially viable.

Let me highlight a few reasons why.

1. The superpower of parametrics is simplicity of the trigger

In practice, parametric contracts almost always have:

• One simple trigger (or at most a very simple double-trigger), and

• A transparent, discrete payout schedule

Not a high-dimensional function of 7 climate indices + 3 macro factors + a latent neural-net feature.

Once you allow an unconstrained “function of many indices”, you’re optimising over contracts that are:

• Hard to sell

• Hard to explain

• Hard to operate and audit

In other words: “optimal” on paper, but non-marketable in reality.

2. Cognitive load is not a rounding error

Try explaining this to a farmer, SME, or even a busy corporate treasurer:

“Your payout is a piecewise non-linear function of 6 indices with interactions.”

Now compare it with:

“If seasonal rainfall at this station falls below X mm, you receive €Y.”

The second one:

• Drives demand

• Eases regulatory approval

• Enables distribution through brokers and banks

The first one might win a theoretical basis-risk contest, but it loses the human comprehension contest – and that’s the one that actually matters at scale.

3. Data, governance, and points of failure

Each component of a multi-index contract can come from a different data source:

• Reanalysis products

• In-situ gauges

• Satellite datasets

• IoT sensors

Add indices → multiply:

• Oracles

• Points of failure

• Governance headaches

Many practitioners, therefore, deliberately cap the number of indices to keep the data pipeline:

• Robust

• Auditable

• Defensible in front of regulators, reinsurers, and counterparties

4. The tail is where insurance lives – and where data is thinnest

High-dimensional, flexible indemnity functions are usually justified by basis risk minimisation: more indices ⇒ better fit.

The problem:

• Basis risk matters most in the tail

• The tail is exactly where we have the least data

• Fitting a complex function in that regime is an invitation to overfitting

You can easily end up with:

• Lower basis risk in the sample

• Worse performance out of sample than a coarse, one- or two-trigger design

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5. The world is non-stationary; models rarely are

Most theoretical models assume a known, stable joint distribution of (indices, loss).

Reality disagrees:

• The climate is changing

• Technology and farming practices evolve

• Regulations shift

In such an environment, simpler, physically interpretable triggers tend to be more robust than clever high-dimensional constructions.

And remember: reducing basis risk is not the only objective. Real products must also optimise:

• Speed and cost of settlement

• Regulatory clarity and classification

• Reinsurance compatibility

• Customer comprehension and willingness to pay

A contract with slightly higher basis risk but much better performance on these dimensions can be economically superior for both sides.

6. Internal models vs external contracts

In practice, sophisticated teams often do this:

• Use a high-dimensional model internally to understand risk

• Then distil that into a contract written on one scalar index with a simple trigger

The academic “I(X)” literature frequently conflates these two layers:

• Internal risk model

• External legal contract

That confusion leads to designs that look mathematically elegant but are institutionally fragile.

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7. The regulatory and legal perimeter matters

Parametric products sit in a grey zone between:

• Insurance

• Derivatives

• Contingent contracts

Supervisors are much more comfortable with designs where:

• The trigger is one clearly observable parameter

• The payout is defined by a simple schedule

• A reasonable policyholder can understand when they will and won’t be paid

A general high-dimensional payout rule starts to look like a bespoke derivative, raising questions about:

• Licensing

• Conduct rules

• Suitability (especially in retail/smallholder markets)

These frictions are usually absent from expected-utility models – but they dominate in practice.

So what is a more grounded agenda?

If we model parametric insurance as a fully general function of a high-dimensional index vector, we risk:

• Misrepresenting the actual product space

• Underestimating transparency, governance, and operational robustness

• Overstating how much basis risk we can realistically remove

• Proposing “optimal” contracts that are commercially unsellable and institutionally fragile

A more useful research and product agenda would start from the opposite end:

Take simple, human-understandable triggers as the primitive, and ask:
how far can we improve risk transfer within that low-dimensional, explainable design space?

That’s where parametric insurance actually lives.

Happy to discuss/challenge this view with people working on parametric products, climate risk, and index design – both from the academic and market side.

#parametricinsurance #insurtech #climaterisk #reinsurance #actuarialscience