A new simulation algorithm for more precise estimates of change in catastrophe risk models, with application to hurricanes and climate change

Catastrophe models are widely used for assessing extreme weather risks. One way catastrophe models are applied is to run an analysis, and then run it again with adjusted hazard. This allows, for example, estimation of the impact of seasonal forecasts or climate change projections. One way to implement this approach involves resimulating a year loss table (YLT) from a list of events with adjusted frequencies. The new YLT is usually simulated independently from the original YLT, but this leads to differences between the two YLTs, even if the frequencies have not been adjusted, due to simulation noise. The simulation noise reduces the precision of all estimates of change. We present a new algorithm that attempts to reduce this problem. We create the new YLT incrementally by copying the original YLT and then adding or removing just enough events to capture the change. This algorithm does not involve any approximations, and is no slower. We test the algorithm using a number of simple catastrophe models for U.S. hurricane loss, to which we apply adjustments for climate change. We find that the incremental simulation method is much more precise than independent simulation in all our tests. For example, the estimates of the impact of climate change so far are three times more precise. This equates to the increase in precision that would occur from using nine times as many years of simulation, but for no extra cost.

Automated summary

Catastrophe models are widely used to assess extreme weather risks. One approach involves running an analysis with adjusted hazard to estimate the impact of forecasts or climate change projections. However, simulating a new year loss table independently can lead to differences due to simulation noise. A new algorithm is proposed to incrementally create the new loss table, reducing the problem and increasing precision.