Move over cholera, here’s the Black Death

The Black Death, caused by the bacterium Yersinia pestis, wiped out 30 to 50 percent of Europe’s population between 1347 and 1351

Now, South Africa has been placed on high alert for a potential plague infection.

Mortality rates are estimated anywhere between 30% and 100% without treatment. Many estimates are towards the top end of this range, 80% to 95%. Treatments are available (mostly antibiotics) and are generally effective. Mortality rate where adequate treatment is administered within 24 hours can be 11%.  (Either “just 11%” or “11%!” depending on whether you’re counting up from 0% or down from 95%.)

Spread of Black Death across Europe in 14th century
Spread of Black Death across Europe in 14th century

Plague in Madagascar

124 people have already been killed by the plague in Madagascar. But this is just a particularly bad year. Continue reading “Move over cholera, here’s the Black Death”

Island Life

There is a Mauritian insurer called Island Life. Best name ever for an insurance company.

I firmly believe in anthropogenic climate change. I am not an expert, but my reading has convinced me of the seriousness of the issues, the overwhelming evidence that it is humans at fault. Having young children makes me seriously concerned about our ability to remedy the mess we’re in for their sake.

Part of my very inwards focussed, selfish research was on the practical impact for where I live. Unfortunately for my cycling aspirations, this is current on the bottom slopes of Table Mountain.  Fortunately for my flood risk, the same.

The screen capture below is of an extreme, and hopefully not realistic, 30m rise in sea levels.  The website I used for visualising the impact of sea level rises makes no claims to accuracy, but it is interesting all the same.

Oranjezicht in Cape Town mostly stays above the water line even with 30m rise in sea level

Continue reading “Island Life”

ERP update – delayed response to a blog reader

I reader asked why so many practitioners use high Equity Risk Premiums in their valuations and fairness opinions.

In particular, he mentioned a specific assumption set he had seen including:

  • ERP of 6.8%
  • company specific risk premium of 4%

He also commented on how haphazard the use of risk premiums can be and referenced a few sources I’ve used myself.

The ERP of 6.8% does seem high. However, it really isn’t possible to comment on the specifics of the company specific risk premium without knowing the company.

Although I haven’t updated my research on this in a few years, in my own work I still generally stick with a range of 3% to 5% for an ERP, before considering company specific factors, liquidity, and so on. Historically / empirically estimated ERPs shouldn’t change frequently since the time series used is long. Another few years on a 20 year estimation period shouldn’t have much impact.

Why some practitioners persist in using too-high ERP estimates

This delves into the area of philosophy, but here are my top reasons (a post from 2011 also covers this): Continue reading “ERP update – delayed response to a blog reader”

ENID not Blyton

ENID is a term widely used, just generally not in South Africa. For some reason we didn’t import the term along with most of Solvency II.

This has nothing to do with the Famous Five. While it is most common in the general insurance space, it is relevant across the spectrum of risk management and assumption setting.

Events Not In Data or “ENID” is the forgotten cousin of “what to do with outliers in your data”.

Outliers and where to find them

Outliers are observed values substantially different from others in a sample. Some more formal definitions include:

“An outlier is an observation that lies an abnormal distance from other values in a random sample from a population”

“an outlier is an observation point that is distant from other observations”

Not these sort of outliers. Entertaining book though.


How to deal with outliers?

Simple question, complex answer. It depends a great deal on the context.

Ultimately you need to make the judgement call “are these outliers under- or over-represented in the data”. Continue reading “ENID not Blyton”

Book Review: Loss Coverage – Why Insurance Works Betters with Some Adverse Selection

In his book, Loss Coverage: Why Insurance Works Better with Some Adverse Selection, Guy Thomas propose an interesting point that adverse selection may not be as harmful as many actuaries believe. They actually go further and suggest that, at least from a policy perspective, adverse selection may be a good thing.

This is particularly relevant given the ambition of some InsurTech players to hyper select risks or price on many more factors than are traditionally used in order to gain a competitive advantage.   Thomas doesn’t argue that it will be individual insurers’ interests to allow adverse selection, but if these companies are successful it may then have implications for policy makers.

Incidentally, there are some interesting reasons for insurers themselves (with commercial interests) to be wary of selecting too well, counterintuitive as that may seem, but more on that for another time. Continue reading “Book Review: Loss Coverage – Why Insurance Works Betters with Some Adverse Selection”

Modelling one side of a two-sided problem

Ah models, my old friends. You’re always wrong, but sometimes helpful. Often dangerous too.

A recent article in The Actuary magazine addressed whether “de-risking in members’ best interests?”  I say “recent” even though it’s from August because I am a little behind on my The Actuary reading.

In the article, the authors demonstrate that by modelling the impact of covenant risk, optimal investment portfolios for Defined Benefit (DB) pensions actually have more risky assets than if this covenant risk is ignored.

The covenant they refer to is the obligation of the sponsor to make good deficits within the pension fund. Covenant risk then is the risk that the sponsor is unable (typically through its own insolvency) to make good on this promise.

On the surface it should seem counterintuitive that by modelling an additional risk to pensioners, the answer is to invest in riskier assets, thus increasing risk.

The explanation proffered by the authors is that the higher expected returns from riskier assets allow the fund to potentially build up surplus, thus reducing the risks of covenant failure.

I can follow that logic, particularly in the case where the dependence between DB fund insolvency and sponsor default is week. It doesn’t mean it’s a useful result. Continue reading “Modelling one side of a two-sided problem”

Systemic risk primary poll

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Behavioural economics and XKCD

This is premature for a book review as I’m only on page 3 or something. The book:Insurance and Behavioral Economics: Improving Decisions in the Most Misunderstood Industry has been recommended to me a few times. (Okay, twice by the same person, but that also actually says something.) I’ll work my way through it and give you my views.

But this XKCD reminded me of the ubiquity of behavioural economics everywhere in life.