Alternative Risk Transfer

Imagine an insurer of coastal properties concerned about the next hurricane season. A severe storm happens once every few decades; if the severe storm hits this year, she’ll have to pay out most of her money in claims. To help remain solvent, she could buy reinsurance. Alternatively, she could issue a catastrophe (cat) bond, which would pass the risk on to an investor. An investor could buy the bond valued at, say, $100,000; over time, the insurer would repay the bond with, say, 15% interest. If no hurricane hits during the year, the investor makes 15% on his investment. The insurer also turns a profit because she continues to collect premiums. But if this low-probability severe hurricane does hit, then the investor loses his $100,000, which is used by the insurer to pay claims.

A cat bond triggers payments based on the occurrence of a specified catastrophic event. Most cat bonds to date have been linked to hurricanes and earthquakes, but some have been issued to respond to mortality events. Capital raised by issuing a cat bond is invested in a safe security like a treasury bill, which is held by a special-purpose vehicle (SPV). A SPV is often a company created to execute specific financial transactions. The bond issuer holds a call option on the bond principal (option to buy all or part of the principal) in the SPV with triggers that are specified in the bond contract.

The triggers can be defined in terms of the insurance company's total losses from the catastrophe or some hazard event characteristic. If the defined catastrophic event occurs, the bond issuer can withdraw bond funds from the SPV to pay claims, and part or all of the interest and principal payments are forgiven. If the catastrophe does not occur the investor receives the principal plus interest equal to the risk-free rate (e.g., London Inter-Bank Offered Rate--LIBOR), plus a spread above LIBOR. Cat bond maturity is typically on the order of 1 to 5 years.

Limitation of risk models in a changing climate

Hurricane risk models used by the insurance industry rely on a catalog of storms that represent the historical data in some way or another. While useful for estimating aggregate portfolio losses from a hypothetical worse case scenario, these catalogs are not easily suited for anticipating losses based on a changing climate. At the core of the catalog is a set of synthetic storms and a way to assign a probability to each. But each synthetic storm in the catalog is a composite of size, track, and intensity so it is difficult to estimate risk at a particular point location. Additionally, it is important to consider how climate influences hurricane risk, but it is not obvious how to condition the multidimensional storm event on climate. Perhaps most importantly, the assigned return rates are empirically driven in that the rates are not connected parametrically to a theoretical distribution. This leads to lower confidence in the estimated rates.

We propose an alternative approach for anticipating losses that produces predict expected wind speed distributions at any location. The parametric distributions which give tighter confidence intervals can be naturally conditioned on preseason climate variables. More importantly this approach could allow the reinsurance industry to examine which coastal regions are most sensitive to the changing climate. For example, with increasing Atlantic sea-surface temperatures is it realistic to expect that the risk of hurricane damage will increase everywhere? The answer to this question has implications for insurance rates and societal vulnerability.