Hurricane Forecast Contracts as Efficient Reinsurance 

In a previous article, I explored in detail how taking the NO side on major landfalling hurricane Forecast Contracts can add substantial value to an investment portfolio by increasing its diversity and thereby reducing its downside risk. I have also previously summarized why insurance companies, municipalities, utilities, or other organizations with broad hurricane exposure in specific US counties might consider buying the YES side of these Forecast Contracts as a hedge, similar to insurance. In this article, I will pick up on that thread and explore in much more detail how purchasing YES hurricane landfall Forecast Contracts can help insurance companies hedge risk in a way that can be more efficient than traditional reinsurance.

Reinsurance functions as insurance for insurance companies: a primary insurer pays a reinsurer a premium to transfer some of its largest loss risks. This strategy reduces capital volatility and the risk of insolvency for the primary insurer, but it can come at a steep cost. Since the primary insurer values downside protection so much, it will pay for reinsurance well above its “fair” value or at an expected loss. This loss essentially funds the existence of the reinsurance company. It covers the underwriting risk but also covers costs such as staff, modeling, brokerage, capital charges, and profit margins.

As I previously discussed, the same willingness to “overpay” for downside protection should also apply to the exchange-traded Forecast Contracts market. Insurers (and other hedgers) bidding YES prices higher create an advantageous edge for NO buyers, which encourages participation on both sides of the contract. 

The example I have been using supposes that the probability of a major hurricane (Category 3 or higher) making landfall in a specific county in a given year is 10%. Therefore, the true YES value is $0.10, and the true NO value is $0.90 per contract. I assumed that those seeking to hedge risk would bid YES prices above their actual actuarial value, to $0.14 per contract (attracting NO buyers because they could be purchased at $0.87 when they were actually worth $0.90 per contract).

The critical dynamic in this example is that YES prices are driven above their actuarial value, but the market reaches an equilibrium where YES prices remain inexpensive compared to traditional reinsurance. This is possible because an exchange-traded contract doesn’t need to cover the aforementioned expenses that fund the existence and operation of the insurance company.

The rest of this article illustrates this basic concept with specific numbers in a suite of Monte Carlo simulations. I compare the financials of a primary insurer in two scenarios: one where they purchase a single “layer” of traditional reinsurance and another where they replace half of that layer with Forecast Contracts. I analyze outcomes over 10,000 simulated decades, with hurricanes and losses drawn stochastically each year. 

I base the simulation roughly on tropical storm probabilities for a highly exposed region like Miami-Dade County. I assume a ~40% chance of any named storm making landfall annually and partition the probabilities further as 20% for tropical storms, 12% for Category 1 hurricanes, 10% for Category 2 hurricanes, 6.5% for Category 3 hurricanes, 2.5% for Category 4 hurricanes, and 1% for Category 5 hurricanes (top panel of Figure 1). The sum of the last three probabilities (6.5%, 2.5%, and 1%) equals 10%, which aligns with the example I have been using for Category 3 or stronger landfalls, triggering the YES for Forecast Contracts.

I assume that the primary insurer has taken on $10 billion of Total Insurable Value (TIV) in the county. I also assume that they charge their customers 4.5% of the Total Insurable Value per year in premium, so they have a revenue (technically Gross Written Premium) of $450 million per year. 

I assume that the average Category 5 hurricane would cause the primary insurer $4 billion in losses in a year, or 40% of their Total Insured Value. Empirical evidence indicates that economic damage from tropical storms scales with the 8th power of wind speed, so I use that scaling to extrapolate insured losses to storm categories below Category 5 (middle panel of Figure 1). 

Actual insured losses, of course, will depend not just on storm category but on the idiosyncrasies of how the storm interacts with the insured properties. This means that there will be significant variability in insured losses for any given storm category. To reflect this variability, I allowed actual losses to fluctuate randomly around the aforementioned averages. Specifically, each category’s financial loss is drawn from a skewed (lognormal) distribution with a standard deviation of about 40% of its mean (error bars in the middle panel of Figure 1). This is an important aspect of the demonstration because it simulates the Basis Risk, a fundamental attribute of any parametric insurance product. To keep things simple, I assume that only one storm can strike per year and that the Total Insurable Value is constant. 

Each simulated year stochastically draws whether any storm made landfall and, if so, what its category was. Then, for that category, the simulation draws a dollar loss from the lognormal distribution. These simulated losses then pass through a simplified version of a primary insurance company’s finances.

Figure 1 | Annual hurricane landfall statistics from the Monte Carlo demonstration for a hypothetical county. Top: Black bars show the probability that a given year contains a landfalling storm of each Saffir–Simpson category; the red line is the exceedance probability (chance a year is at least that severe). Middle (log scale): Mean insured loss as a percent of Total Insurable Value (TIV) conditional on that category occurring, with red ±1σ error bars. Bottom: Expected annual loss (frequency × severity) by category, illustrating that rare but intense Cat 3–5 storms dominate the long‑run loss budget. Produced using Matlab. 

I assume that the primary insurer has purchased Excess of Loss Reinsurance with an upper limit, which can be visualized as a tower with different pricing rates for various “layers” of the tower. The primary insurer is responsible for losses up to a certain amount, known as the retention layer. This layer is roughly equivalent to the deductible for an individual purchasing primary insurance, as it is the portion the policyholder is responsible for before coverage takes effect. Above the retention layer, the reinsurer typically covers losses up to a fixed limit. To keep things simple, I assume only a single reinsurance layer (Figure 2). 

The price the reinsurer charges the primary insurance company for this coverage is usually described by the “Rate on Line,” which is the ratio of premium paid to recoverable loss. The Rate on Line embeds all the reinsurer’s aforementioned overhead and profit.

The demonstration compares the primary insurer’s financial situation under the two protection tower scenarios. In the Re-only scenario, the primary insurer covers the catastrophe layer through reinsurance-only. Conversely, in the Hybrid scenario, the insurer still secures the lower half of the layer from a reinsurer but reduces the maximum reinsurance limit and reallocates the cost savings (on a dollar for dollar basis) toward purchasing YES Category 3+ hurricane landfall Forecast Contracts priced at $0.14 per contract (above their true value of $0.10 per contract). 

Figure 2 | Left: “Protection tower” for the two scenarios. Grey = insurer retention (up to the attach rate); blue = reinsurance layer; red = YES Forecast Contracts occupying the top half of the layer in the Hybrid case. Dashed horizontal lines mark the mean loss levels conditional on different storm categories (exceedence) and their approximate return periods. Right: Simulated annual gross‑loss distribution, shown as a histogram (log‑probability x‑axis) with the red exceedance curve (1–the cumulative probability function). Produced using Matlab.

Figure 3 illustrates how the primary insurance company’s income statement compares under the two scenarios (columns), both when a major hurricane strikes and when it does not (rows).

The upper-left panel illustrates the situation during years when there is no major hurricane landfall in the reinsurance-only scenario. In this case, there is some loss from storms below Category 3, but it’s minimal. There is also some reinsurance payout, but it’s even smaller. Here, the primary insurer cedes part of the premium to the reinsurer and keeps some of it, which ultimately results in a net profit with low variability (black bar).

The upper-right panel shows the situation during years when there is no major hurricane landfall in the hybrid scenario where Forecast Contracts are purchased. In this case, half the money spent on reinsurance is redirected to buying Forecast Contracts, so Re prem  is halved and it is reallocated to FC Cost. Since no major storm occurs, there is no Forecast Contract payout (FC payout), so this money is lost. The overall result is that the net profit remains the same in both scenarios when a major storm does not occur (cf. black bars in top row). 

Figure 3 | Annual cash‑flow components expressed as a percent of Total Insurable Value (TIV), split by scenario (Re‑only vs. Hybrid) and by whether a Category 3+ storm occurred. Bars show the mean of Monte Carlo years in each bucket; whiskers mark the interquartile range. Blue bars are positive inflows for the primary insurer, and red bars are outflows. The black bar is net profit. Produced using Matlab.

The lower left panel of Figure 3 summarizes what happens in years where a Category 3+ storm makes landfall under the Reinsurance-only scenario. In these years, there are substantial losses with large variability (storm loss bar). Reinsurance covers part of the losses (shown by the blue “Re payout” bar), but the primary insurer is responsible for the retention layer and losses above the upper limit (which are substantial, see towers in Figure 2). The result is that the primary insurer absorbs a large hit and makes a negative net profit in those years.

The lower right panel of Figure 3 shows the scenario where a Category 3+ storm makes landfall under the hybrid scenario (the scenario with Forecast Contracts). In this case, reinsurance covers a smaller part of the losses (as shown by the smaller blue “Re payout” bar on the lower right compared to the lower left). However, the Forecast Contracts trigger and payout at a ratio of $1.00 per $0.14 spent. This payout adds on to the reinsurance and effectively turns the negative net profit in the reinsurance-only case into a positive net profit on average. What we are seeing is that, since the YES contracts are bought closer to their actuarial value than reinsurance, the hedge is more efficient.

Figure 4 shows how total capital accumulates in the two scenarios. The reinsurance-only case (black line) rises but is accompanied by a large, concerning fan below it—the 5–95% envelope includes the zero line signifying a substantial risk of insolvency. The hybrid scenario (blue line) rises more quickly, with a noticeably lower chance of insolvency.

Figure 4 | Left: Median capital accumulation trajectories (solid) with 5–95% bands over 10,000 Monte Carlo simulated decades. Right: Distribution of capital in year 10. The hybrid structure both raises the average ending capital and materially trims the downside risk. Produced using Matlab.

Whether these results apply to specific companies will depend on the details, but this demonstration shows that the use of Forecast Contracts can yield better results for a primary insurer than purchasing reinsurance alone. Fundamentally, traditional reinsurance is priced with a significant margin to cover reinsurer overhead, other operational expenses, and profit. YES major hurricane landfall Forecast Contracts come with basis risk and are also likely to be bid higher than their actuarial value. However, when basis risk is manageable and when the mispricing is relatively small, these contracts can act as a more efficient hedge than reinsurance, offering an attractive means of transferring risk.