Having worked in the finance industry developing risk models for clearing houses, I can safely say that the problem is a lot worse than the authors make it out to be.
Regulators across the globe, practically all of whom are wholly ignorant of what they regulate, demand implementations of their tabular formulas which are often highly specific rather than generalized and so become obsolete as quickly as people find gray spots in the formulations and ways to circumvent the intentions of the regulation. Basic it reads more like law than math and it doesn’t work.
That means all costs go to implementing these sub-par models leaving no room for innovation.
In the end, common people will have to foot the bill and regulations will get even worse. Vicious cycle.
Catastrophe risk models, however, shouldn’t be be lumped together with the types of financial engineering/quant models used in capital markets. The latter are generally very theoretical, based on pretty shallow theory and little data. Academia has been spewing out a lot of quant finance models which have little bearing on real market dynamics.
Cat risk models, however, are generally more data-driven and rich in terms of science and engineering I.e. understanding of underlying hazard mechanisms. But, like any model, they still are only approximations of reality.
An additional problem with regulations is that the regulators are often completely blind to activity on the other side of regulatory boundaries. This was a pretty big contributor to the 2008 Financial Crisis - risky loans were sold off of the books of American banks, packaged up as AAA rated securities, and then sold to European banks which were allowed (by formula) extremely high levels of leverage for buying AAA rated debt.
Exactly, these are companies in business to make a profit not to provide a societal benefit. They all benefit from regulation of margins and common model/pricing, "everybody wins" except the customers.
Most of the world doesn't have a Health Insurance industry (of US size and cost) and they have better outcomes with lower pricing. That doesn't mean there isn't an insurance industry in those places, it's just smaller, more properly regulated, and less profitable.
Insurance and reinsurance are highly regulated businesses, where firms have to hold reserves commensurate with the risk they are taking. While the models have limitations, they are good enough to evaluate capital requirement.
In the last 20 years, investors have become a welcome source of risk capital which was driven by (a) the increasing need of insurance coverage relative to existing loss-paying capacity within the system (I.e. Need for extra capital to support the growing market) and (b) the fact that insurers and reinsurers seek to diversify their own insurance coverage, By minimizing credit risk.
And if climate change continues the course... does not bode well current society. Doomsday scenarios are always painted with grand events. With this homogeneity a mid size event could cause a economic domino effect.
The risk models are updated with new science and current risk data on a regular basis, but yes, climate risk is a worry to the industry. The models do allow for climate risk scenarios e.g. by tweaking the frequency/severity of land-falling hurricanes/cyclones etc.
Havn’t read the paper in detail yet, but the authors’ understanding of the property catastrophe market is rather simplistic. I’ve traded this risk as an investor for fifteen years and have used the risk models that are used by all firms in the market. Yes, there are a handful of cat risk model vendors, but their numbers are not constrained by the need for certification.
The fact is these risk models are quite sophisticated and all make use of pretty much all the science, engineering and data available with respect to the hazards in question. They still are limited in their ability to accurately estimate risk and this is taken into account when pricing transactions.
Furthermore, while all firms have access to the same models, many (who employ scientists and engineers) will apply tweaks based on their evaluation of model weaknesses/limitations. Hence, there is an inherent level of diversification in terms of how the models are used to price risk. This is what makes a market.
The objective of catastrophe risk modeling is to estimate, long-term risk as accurately as possible, while knowing full well that there remains a lot of uncertainty.
The regulators, rating agencies and other market watchers are actually ill-equipped to assess catastrophe model risk.
It is not uncommon for agent based models to lack on the side of good engineering practices. Given that the majority of these softwares are programmed to use and throw.
I did research in that field for about 7 years and at that time the focus was mainly on the paper that was to be published.. it is even a win to have the source available nowadays.
Why is too few models the issue? Wouldn't the problem be that the existing models arent good enough? Its not clear to me how an increased diversity in models addresses the problem - at the end if the day a form has to pick one to make any particular decision. If each firm is using a different model in the name of diversity, then while the few firms with more accurate models potentially do well the subpar models will underperform in general and probably increase the overall risk level of the industry. Is there something I'm overlooking from my skin through the article here?
No model is going to be perfect and given that, you want errors that models make to be as uncorrelated as possible.
In the extreme case, if all firms use a single model that underestimates the risk of an event then that event occurring poses a threat to the entire industry.
With more models, each making different errors, only parts of the industry are exposed to each error. The more models, the smaller the number of affected firms and with reinsurance between firms the risk to the whole industry is much smaller.
The basic question is whether the reinsurance companies are properly capitalized given the homogeneity of risk taken on by the insurance companies they cover.
There can be a lot of modeling homogeneity without lots of unanticipated risk (e.g., natural disasters in disparate geographic regions).
It's a question of fragility. If everyone thinks the same about risk, they will be holding similar bags when something happens.
Think of it like an ecology. If there's some firms being on A and some on B, the catastrophe selects for one set. If everyone is the same they all die.
Why would you assume that models can be ranked on a single one-dimensional scale from good to bad? I mean, they can, but only retroactively and by throwing away a lot of other useful information.
That's possibly because Taleb does not have a good reputation among most experienced professionals (in my circle in the hedge fund industry). He talked a lot of sense, but then failed to put most of it into practice. In other words, he isn't earning money from what he preaches, he earns money from the preaching.
It's all nice and dandy to say "you have to be anti-fragile, expect the unexpected, yadda yadda yadda...", but putting it into practice is extremely difficult. I don't know of a single institution who did so with an audited track record. Sure, there are (were) many tail event specialized funds, but these will not be providing regular income as one would expect from most funds. Rather, they'll tend to constantly bleed money (basically an insurance premium), and then pay out larger sums when the "midden hits the windmill".
Even looking at something like 2008, it is often cheaper to just accept the occasional drawdown and weather the storm rather than buy protection against it, because of the amount of headwind your investment will face in terms of the insurance premiums.
Taleb one day got into an all out drop down twitter war with my friend Andrew Shaffer, author of books like "50 shames of earl grey" "how to survive a sharknado" etc. His personal unhinged behavior makes everyone suspect of his professional work.
Taleb is a popularizer. Authors who popularize science are typically not cited in academic papers. If a concept or result is used in the paper, the original source is typically cited.
This, plus his academic work which is not for a popular audience is supposedly pretty vanilla and doesn't rant and rave about how 'dumb' everyone else in the field is, how all of their models are garbage, etc. He does have professional papers in the academic space, they're just not confrontational or particularly iconoclastic- unlike his popular work where he portrays himself as a bold rebel against convention etc.
I’ve wanted to see similar simulations but for the housing market. Historically housing rental prices didn’t exceed renters income and yet times of rapid housing inflation (2006-2008) outpaced any income gains. Of course those reasons are known, and the subsequent housing price fall set off a cascading effect. Recent housing increases outside of the bay seem eerily similar to the previous bubble, but I haven’t seen any good analytical framework to determine if it’s sustainable.
Should the insurance just find a way to exclude massive, correlated events? That might give buyers a more reasonable expectation of covered losses, given that coverage in such an event is smoke and mirrors anyway. It's all well and good to have liability insurance, since presumably one's own liability is fairly uncorrelated with other people's liability. Flood insurance, financial instrument insurance, etc. is more questionable to me.
That is what the reinsurance market is for. Natural disasters are when insurance is most needed, to rebuild a whole community. Disasters are locally correlated, but generally not globally correlated. The global insurance market can handle them just fine, and in fact are a great example of a place where insurance is needed - large, unpredictable, rare, and too expensive to handle by yourself. That is just the same as individual insurance, just at a larger scale.
> Should the insurance just find a way to exclude massive, correlated events?
Why exclude when reinsurance can deal with them fairly easily? The insurance company sells bonds that only pay when the massive, correlated event doesn't occur. Financial markets like those, because while they may be 'correlated' in a sense, they are still largely uncorrelated with global growth trends - they're free diversification from their POV. And the "wisdom of the masses" takes care of the modeling - the bond price tells you (and the company) how likely the massive event is expected to be. No added model risk!
I'd love to see simulations for Lloyds of London,where underwriters often insure each other or the the entire trail of who's responsible for what gets very tricky.
Regulators across the globe, practically all of whom are wholly ignorant of what they regulate, demand implementations of their tabular formulas which are often highly specific rather than generalized and so become obsolete as quickly as people find gray spots in the formulations and ways to circumvent the intentions of the regulation. Basic it reads more like law than math and it doesn’t work.
That means all costs go to implementing these sub-par models leaving no room for innovation.
In the end, common people will have to foot the bill and regulations will get even worse. Vicious cycle.