Forecaster as Entrepreneur (or How Do You 'Know' Without Knowing)

I came across a very interesting article in the Economics Focus feature of The Economist: A new fashion in modelling: What to do when you don't know everything (22 Nov. 2007). The article describes the work of NYU economist, Roman Frydman. In his new book, Imperfect Knowledge Economics, Prof. Frydman makes the case for using predictive models that are more intuitive and qualitative than those traditionally favored by economists.

As the article notes, Prof. Frydman starts from a very interesting proposition: "The forecaster is like an entrepreneur. He uses quantitative methods, but he also studies history, and relies on intuition and judgment." From this brilliantly crafted idea (and I am very sympathetic to this viewpoint), he crititques traditional methods of economic forecasting -- using exchange rates as a case study (of sorts). The method that comes under the most scrutiny is the dominant approach of the 'rational expectations' school. Much to -- what I can safely forecast -- the chagrin of the adherents of rational expectations, Prof. Frydman compares them to communist idealogues who insisted, to the bitter end, that communism could be made to work.

The problems with rational expectations -- which is so dominant in economics that even its critics essentially fall under its spell -- are legion and isn't worth detailing here. But there is one fact that best encapsulates its failings: Predictions of currency movements using rational expectations type approaches are usually worse than flipping a coin!

Someone who follows the bloodsport of the endless debates within economics and finance faculties between different schools of thought might think that Prof. Frydman might favor 'behavioural economics.' Surprisingly, although he does give behaviourialists credit when they critique rational expectations, Prof. Frydman is critical when behaviouralists turn around and try to 'precisely' forecast human behaviour (which, if you will recall, is what I call falling into the 'rational expectations trap.')

Prof. Frydman doesn't just cast stones; he offers solutions. What Roman Frydman proposes will be familiar to intellectually oriented traders -- mixing (or 'mashing,' as kids now a days would call it) quantitative methods with the spotting of qualitative regularities (or irregularities) that only a 'forecasting entrepreneur' (e.g., thinking traders and investors like George Soros, Warren Buffets, Nassim Nicholas Taleb, etc.) can do. Prof. Frydman's approach to uncertainty is essentially Bayesian as opposed to 'frequentist' -- i.e., it is inherently subjective. (See the latest book by financial theorist, Riccardo Rebonato: Plight of the Fortune Tellers: Why We Need to Manage Financial Risk Differently. In that book, Rebonato also advocates a Bayesian approach.)

The 'problem' with Prof. Frydman's -- and, I suppose, Riccardo Rebonato's or Nassim Nicholas Taleb's -- approaches are that the forecasting models that they are advocating will not be as 'precise' as the models advocated by the mainstream. My response to that: Good! What is the point of having precisely wrong models of reality (other than for falsification)? If we can get more accurate models of reality, we should be glad to sacrifice false precision.

In light of the recent credit crises, the closing comments in the article seem especially important:

Messrs Frydman and Goldberg are now turning their attention to the troubled subprime-mortgage markets, and the performance of the rating agencies. The rating agencies, argues Mr Frydman, have generally been better at rating corporate bonds than rating asset-backed collateralised-debt obligations. Why? One reason is that the rating agencies used both a mathematical model and the judgment of their in-house specialists when forecasting the default probabilities of corporate bonds; on subprime-related securities, they could only use mathematical models, not least because the instruments were so new. “They had no experience, no intuition, no entrepreneur,” he says. That is “empirical proof that relying on models alone is not wise.”

Finally Waking Up to the Credit Derivatives Mess

Regulars to The Econophysics Blog know that, since day one of the current crises, I've been pointing out the role that credit derivatives have played in creating and worsening the credit/mortgage crises -- a fact that was obscured or under-reported by the mainstream financial press and 'experts.' There is an article in The Economist that basically confirms the case I've been making -- along with some new fears -- about the toxicity of credit derivatives: CDOh no!: With trades scarce and losses mounting, it is going to be a harsh winter (Nov. 8, 2007).

According to the very interesting and comprehensive article, the AAA tranches of CDOs (collaterised-debt obligations: a vehicle used to package credit derivatives) have been substantially downgraded in value (see the chart of the ABX index -- and index of credit derivatives -- below) with fears of more downgrades to come. This would wreak havoc on already shaky financial markets.


There is also the question of accounting for these credit derivatives. The recommended method is to stick it in a category called "Level 3" which assesses "fair value" using "assumptions that market participants would use." But is that a good way to assign "fair value"? This situation is made worse by the fact that Level 3 securities have grown so much that they "now exceed shareholder equity" of many banks. No wonder many investment and commercial banks (as well as insurers, hedge funds, etc.) have been accused of not marking down their credit derivatives sufficiently.

Things can get much worse: AAA rated securities are relied on by a diverse range of investors -- from old age pensioners and municipal goverments to high flying hedge funds and banks. If AAA rated securities take a bigger hit because of their links to credit derivatives -- and/or other type of credit linked instruments (linked to consumer loans, for example) start sliding -- things can get really ugly ... much uglier than it is now.

Book Review: The Mathematics of Natural Catastrophes

I haven't written a pure book review blog post since coming to the UK, so I thought I would write one reviewing a book by a British author. Gordon Woo -- the author of The Mathematics of Natural Catastrophes -- was a senior wrangler in the Maths tripos as an undergraduate at Cambridge University. He went onto a PhD in theoretical physics at Cambridge as well (while taking a detour through MIT and Harvard). He is currently on the staff of RMS (a risk management consultancy) doing research on catastrophic risk and the risks of terrorism.

The title of Dr. Woo's book is slightly deceptive (but in a good way). Yes, it is literally about the mathematics of natural catastrophes, but it's a lot more than that. It is an insightful yet accessible guide to the philosophy of risk and chance.

Although the book has its share of mathematical formulae and equations, the book -- despite the title and the academic background of its author -- is less about maths (British English) in the colloquial sense (i.e., complicated symbolics) and more about maths in its true sense, a logical way of tackling problems. In that way, the book is highly readable and should be accessible to people with even rudimentary mathematical backgrounds, yet remaining sophisticated enough for people with more formal training in maths.

The types of natural disasters discussed in the book focuses on examples from geology and meteorology, but the discussion can easily be extended to other types of events (including terrorism). Not surprisingly, given Dr. Woo's background, the author applies the tools of mathematics, statistics, probability theory, physics, geology, meteorology, engineering, and actuarial methods, in order to get a grip on these thorny issues.

But he doesn't stop there; he also demonstrates -- in the framework of Isaiah Berlin's The Hedgehog and the Fox -- a certain intellectual 'foxiness.' Dr. Woo incorporates history, philosophy, and even literature into his analysis of catastrophic risk. For those interested in the increasingly important links between natural catastrophes and financial instruments, Dr. Woo devotes a chapter to financial issues (including Cat bonds) and other chapters to issues relevant to the insurance industry and the 'management' of extreme risk.

The most fascinating aspect of this book, beyond the purely practical (and there are definitely practical aspects to this book), are the philosophical aspects of the book. As a mathematician philosophizing about the nature of probability, Dr. Woo reminds me of another Cantabrigian mathematician (unfortunately, known more as an economist), John Maynard Keynes (especially in his magisterial, A Treatise on Probability). Dr. Woo's book has not received as much attention as Nassim Nicholas Taleb's Black Swan (my book review can be found here), but I strongly believe that fans of the Black Swan will enjoy reading The Mathematics of Natural Catastrophes.