According to a new working paper by catastrophe modelling specialist Reask and specialist insurance-linked securities investment manager LGT ILS Partners, a climate-conditioned Industry Loss Warranty (ILW) strategy can materially outperform traditional static modeling approaches.
“Catastrophe models used across insurance, reinsurance, and capital markets are built around long-term stationary climatologies. They treat historical averages as a reliable guide to current and future risk,” Ian Bolliger, Principal Quantitative Engineer at Reask explained in the published case study.
He added: “For ILS portfolio managers, this creates a structural blind spot: pricing and capital allocation decisions typically rely on static loss distributions that ignore predictable, year-to-year variations in hurricane activity driven by seasonal weather patterns.”
Bolliger noted that both firms were observing to see whether seasonal weather information that is already well-developed in the forecast community, could possibly be translated into dynamically updated loss distributions that would materially improve portfolio performance.
With this, Francesco Comola, Head of Analytics at LGT ILS Partners, along with colleagues at the firm whilst working alongside Bolliger and Reask’s CEO, Jamie Rodney, developed a climate-conditioned catastrophe modelling framework that combined two key components.
Most relevant to our readers is where the teams simulated an investment strategy in a portfolio of 36 regional ILWs, covering a 40-year period (1985-2024).
According to Bolliger, each year’s capital allocation was guided by Reask’s UTC model forced with European Centre for Medium-Range Weather Forecasts – ECMWF’s SEAS5 seasonal weather forecasts.
A static view of risk was then adjusted by combining the model outputs with Reask’s Climate-Based Risk Adjuster (CBRA) tool.
The analysis also used real-world data such as stochastic loss tables from a widely used commercial catastrophe model, historical insured hurricane losses from Property Claim Services, and broker-quoted ILW prices.
“We then evaluated how the portfolio performed, using actual recorded industry losses. We benchmarked this performance against a separate strategy that used capital allocations guided by a traditional, static long-term climatology approach,” Bolliger said in the case study.
The findings from the research indicated that the climate-conditioned ILW strategy consistently surpassed the static benchmark throughout the entire 40-year evaluation period.
Average returns reached as much as 30% higher, with the most significant increases observed at moderate levels of risk aversion, while the compound annual growth rate was as much as 35% higher for profiles with low-to-mid risk aversion.
Additionally, risk-adjusted returns were up to 40% greater, demonstrating enhanced returns coupled with diminished volatility, while average drawdowns during high-loss years were up to 50% lower, contingent upon the level of risk aversion.
“Working with the Reask team on this paper gave us a way to quantify something the ILS market has long debated: whether seasonal climate forecasts are actually actionable for portfolio decisions. The 40-year back-test suggests they are, and by a meaningful margin,” commented Francesco Comola, LGT ILS Partners in the published case study.
The research from both firms clearly shows that a climate-conditioned approach can outperform traditional static models by specifically accounting for seasonal climate signals, such as El Niño, that traditional models can sometimes ignore.
