Has Climate Change Increased Natural Catastrophe Volatility?

You sometimes hear people say that climate change has not just increased the severity of events, but it’s also increased the volatility of events. i.e. that weather is specifically getting more 'extreme'. For example, the following from Aon [1] “Climate change, for example, is increasing this weather-related volatility”, or this from a BBC article "...climate models likely under-estimate the changes seen so far, but even those models suggest a doubling of the volatility" [2]

We've been playing around with the Swiss Re Global nat cat data in the last 2 posts, and I thought it could be interesting to see if these claims about volatility bear out in the data. More specifically, if annual cat losses are not just getting higher on average over time, but whether they are also become more volatility over time.

To try to isolate the volatility component and not be influenced by the change in average severity, I’m only going to look at on-levelled losses. (for more explanation of this adjustment, see the following blog post) [3].​ 

@ Alexego01 https://www.goodfon.com/city/wallpaper-download-3840x2160-snow-london-tower-bridge-river-thames-sneg-london-tauerskii.html
Photo not really related to the post, but looks lovely and is a 5 minute walk from my office. 

Model 

To test the hypothesis, we are going to plot three volatility metrics over time using the on leveled data.

Metric 1 – 5 and 10 year rolling coefficient of variation (CoV). If volatility has been increasing, we should see an upward trend in this metric. I've used 5 and 10 year windows to see if the answer is sensitive to the choice of numbers of years. (Jumping ahead, this choice doesn't seem to matter too much.)

Metric 2 – 5 and 10 year rolling mean average deviation (MAD). MAD should be more robust to outliers, which is good as we know our data has a number of outsized years.

Metric 3 – We're going to fit a Lognormal to the whole series, and then plot where each year falls in percentile terms against this distribution. If volatility were constant, these percentiles should look random, but if we have increasing volatility, then we should see more extreme percentiles (both high and low) over time.

I’ve carried out the analysis in a Juypter notebook, so you can copy and paste or extend yourself. Like most actuaries, I do most of my work in Excel, but it’s just much easier to embed a notebook onto a website. Don't want anyone to take away the wrong impression that I'm always using python!

​Results

There you go, despite claims to the contrary, the Swiss Re data actually seems to show that the volatility of annual losses has been decreasing in the last 15 years or so. All three metrics tell a similar story, but looking at the CoV graphs, there is an upward trend from 1980 with volatility peaking around 2011 (with a CoV around 1), and there has been a downward trend in volatility since then, with a low around 0.2 in 2024. Actually not what I was expecting at all, but sometimes surprising results are the most interesting!

The first two graphs show broadly the same story, but I think it’s good to test using a couple of metrics, and also vary the number of years in the rolling window.

This is telling us that in some ways, the annual global insured losses are actually more predictable  than they were in previous years. Previously we could expect relatively clean years, followed by outsized events. What we've seen in the last 5 years is annual losses clustering around the 100bn-150bn range on a revalued basis rather than swinging wildly as they did in older years.

Why might volatility have decreased post-2011?

I don’t have a definite answer for you here.

A few possible effects are that these large events (Andrew, Katrina, etc.) really were quite extraordinary, and we’ve had some reversion to the mean since. (I'm planning to examine this in a future post).

It’s also possible that with more data we will look back and the effect we are seeing is just random noise, and there was somewhat of an upward trend in volatility.

It’s also possible that climate change has increased the volatility of weather, but that both good and bad weather (say extremely hot summers and cold winters) both lead to higher insurance losses on average. Meaning the increased volatility in weather just doesn't really feed through to increased volatility in annual losses.

Extensions

I think I’m going to do one more post on the Swiss Re data before moving on. Next time I’m planning to do two things, firstly, I want to back-test the above methods on synthetic data, to ensure that they do actually work (these seem sensible intutitively, but sometimes you never know until you actually test them). I’m then going to try to look at running a Bayesian analysis against the synthetic data, and examine given the historic volatility, what’s the likelihood that what we are seeing is just random variation vs.  an actual trend.

[1] https://www.aon.com/unitedkingdom/insights/weather-volatility-and-chronic-climate-risk.jsp
 
[2] https://www.bbc.co.uk/news/articles/c0ewe4p9128o
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[3] www.lewiswalsh.net/blog/impact-of-climate-change-loading-on-property-cat-insurance