Linear thinking is potentially lethal

We have written before about our concerns over the dangers of “framing” in respect of risk analysis. So were heartened to read a recent article by Olivier Blanchard ( who is the IMF Economic Counsellor and head of its Research Department in which he admitted that the economics profession had been lulled into what amounted to a sense of false security by the seeming fit that its largely linear models (based upon rational expectations) had for the so-called Great Moderation.

As the Great Financial Crisis and subsequent Great Recession which began in 2007-8 proved definitively, assuming that the period of volatility and erratic business cycles was an artifact of history was not only wrong, but very dangerous. Even in 2008, it was being argued as a matter of “faith” (!) that US house prices could not decline in nominal terms- and we all know how wrong that was!

Even supposed experts fail to grasp that, while many causal relationships, trends or probable outcomes are linear or bounded within certain parameters, many are not;  such that being constrained to think in such a way, or base one’s analysis and risk assessment upon models which are fundamentally flawed and fallible precisely because they have been constructed using a linear mindset leads to results that are “unexpected”, but should not be.

Of course, thinking in ways which are non-linear is not easy; and models tend to be linear because constructing ones which are non-linear is very difficult. Nevertheless, simply recognizing that “past is not prologue” and that just because certain outcomes are rare or have not yet been observed, does not mean that they can and will not happen.

In the world of natCAT modeling, there are various conventions (but seemingly no consistency) in terms of modeling the probability and severity of 1-in-50, -100, or -250 year events, which naturally is supposed to drive pricing and risk capacity. This helps to some extent in terms of aggregations and estimating relative risk appetites; but, in reality, such an approach is based upon heuristics and past observations. There is always the concern that an event will occur that has never been observed; and that, therefore, additional conservatism should be built into assumptions and pricing. Unfortunately, as current market experience clearly demonstrates, the idea of building in buffers seems to be an alien concept, as participants compete to retain “market share” or generate sufficient “top-line” revenues to justify their cost structures.

In Awbury’s E-CAT business model, our aim is to build our risk models from scratch, without relying upon “received wisdom” or past experience unless it can clearly be demonstrated that they provide an appropriate framework. Yet, even then, we go through a process of challenging each key assumption, asking ourselves: “but what if we are wrong? How bad could the outcome be; and does it still support rational pricing of risk versus reward?”

Exploring beyond the obvious, the linear, the expected outcomes may just maintain your solvency and viability.

-The Awbury Team


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