Time to re-assess CAT models?

As is now customary, Munich Re released its annual overview of CAT losses (https://www.munichre.com/en/media-relations/publications/press-releases/2019/2019-01-08-press-release/index.html).

While the “headline” numbers of USD 160BN of losses and USD 80BN of insured losses were by no means as severe as the trauma of 2017 (USD 350BN and USD 140BN respectively), nevertheless, the insured losses were almost double the 30-year, inflation adjusted average of USD 41BN.

Of course, one can debate the validity of using simple “inflation adjustments” as a metric for what a “normalized” and average CAT year should produce; and there will be those who, quite reasonably, say that 2 years of results do not constitute a trend.

However, as the world’s population continues to increase and climate volatility seemingly rises, resulting in changing and increased patterns of severity, it would be a foolish risk manager who dismissed the outcomes of 2017 and 2018 as merely an aberration.

Unusually, the event that caused both the largest overall losses (USD 16.5BN) and largest insured losses (USD 12.5BN) was not a windstorm but a wildfire- the so-called Camp Fire in California. This followed a combination of drought, strong winds and difficulty of access to the affected area. Including other large wildfires, such overall wildfire losses in the state in 2018 were USD 24BN, of which USD 18BN (or 75%) was insured- the worst on record, for a second year.

Not surprisingly, Munich Re commented that the greater frequency of “unusual” events and possible links between them should cause insurers to question whether such events were already built into their CAT models. We would hazard that it is improbable that models in place at the start of 2018 included the possibility of 2 very severe wildfire seasons in a row; which begs the question of how quickly and rigorously such events can and will be incorporated and thus flow through to technical pricing models.

The other factor to notice is that, in the most severe events that occurred, the ratio of insured losses to overall losses showed an increasing trend- i.e., the scale of claims for the most severe events was proportionately higher than those seen historically overall in previous CAT years. One could argue that this was happenstance (i.e., California just happened to be the key epicenter in 2018). However, if climate change is leading to new patterns of risk, and these happen to become more concentrated (at least for now) in geographical areas with both a higher economic value and a higher insured loss percentage (because insurance penetration is much higher for that type of risk), changes to models will surely also have to include new assumptions on insured percentages. Otherwise, “negative surprises” will occur.

So, CAT modelers (and their clients and colleagues) will have to decide how to adjust and re-weight their PMLs and EMLs. Not to do so would expose underwriters to the increasing probability that their technical pricing models were no longer fit for purpose, which is hardly helpful in a market that is still struggling to achieve sustainable price increases to compensate for the CAT losses of the past 2 years.

Of course, Awbury does not write any form of property CAT risk. However, we are very much part of the overall (re)insurer ecosystem. As a result, it would be irresponsible of us to pretend that the CAT events of 2017 and 2018 have no relevance. At first order levels, they do not. However, our aim is always to look beyond the obvious to “further order” effects, including how behaviours and risk appetites may change. We would be remiss ourselves in not updating our own expectations and models.

The Awbury Team

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