Price Index Insurance
USDA Beef & Pork.

The insurtech needed a rigorous mathematical back-end to power a platform offering index insurance at scale — but off-the-shelf models were not designed for this use case and could not meet target loss ratio requirements.

• Derivatives don't work for most farmers. Futures and options exist for major livestock benchmarks, but they are operationally complex, require margin management, and carry significant basis risk for producers whose actual prices don't track exchange benchmarks closely.
• Smaller and mid-size commodities have no hedging tools at all. For products like avocado or almond — lower trading volumes, no liquid futures market — there is no established price risk management tool and producers are fully exposed.
• The agricultural price risk market is underserved and underbuilt. Existing models weren't designed for this use case and couldn't meet target loss ratio requirements.

The project built a risk engine to model and simulate USDA beef and pork index prices using time series analysis and machine learning methods. The engine was designed to capture the price dynamics specific to each commodity — seasonality, mean reversion, volatility clustering — and simulate forward price paths for contract pricing. Insurance contracts were then structured around these simulated distributions, with trigger, limit, and stop-loss parameters calibrated to meet the client's target mean loss ratio.

• Designed the modelling framework to capture commodity-specific price dynamics including seasonality, mean reversion, and volatility clustering.
• Simulated forward price paths to generate the loss distributions needed for contract pricing.
• Calibrated trigger, limit, and stop-loss parameters to meet the client's target mean loss ratio.
• Built the framework to be extensible to lower-volume commodities like avocado and almond where no prior risk management tool existed.
• Identified climate risk factors — particularly drought — as the primary driver of price risk.

The engagement delivered the mathematical core of the client's platform and a commercially viable, priceable product.

• Risk engine: A functioning model for USDA beef and pork index insurance, forming the mathematical core of the client's platform.
• Contract design: Structured insurance contracts meeting target loss ratio requirements, giving the insurer a commercially viable and priceable product.
• Climate risk attribution: Identified drought as the primary climate risk factor driving price events.