New School Economic Review

Finance is back on the curriculum

by Benjamin on January 27, 2010

At least, that’s what I saw to my disappointment as I perused the £1,000 plus ($1,500+) Easter school being offered by the venerable Royal Economic Society this April. Last year they talked about Auctions and Markets – interesting, but this year they have gone for “credit, business cycle and finance”.

I am being overly harsh. If the whole thing was a big collection of theoretical Merton-Scholes / Fama financial-theory-is-fantastic-don’t-worry kind of thing there would be good reason to criticise them. I still remember Nassim Taleb’s call to boycott any business school that continued to teach portfolio theory in 2009. They still do teach that stuff, but some of the things slated for the Easter school isn’t all bad. There seems to be a focus on empirical work, at least in the recent working papers on the first lecturer (Princeton’s Prof. Hyon Shin) and a lot of his recent work looks at financial intermediaries. The second lecturer (also from Princeton, prof. Hirotaki) seems interested in empirics, but only to the extent that they fit into “theoretical models”, and an older (pre-crisis, 2007) paper of Prof Shin’s uses the assumption that traders use Value-at-Risk models and finds that this may amplify shocks to the system if traders are risk neutral. A second very timely paper of his and Gara Afonso showed, in October 2008 no less, that:

banks attempting to conserve liquidity cause an increase in the demand for intraday credit and, ultimately, a disruption of payments. Additionally, we find that when a bank is identified as vulnerable to failure and other banks choose to cancel payments to that bank, there are systemic repercussions for the whole financial system.

I think this sounds rather interesting actually, although how much of the course will be talking about exciting empirical results and research, and how much will be on theory remains to be seen. I don’t think we need to throw Taleb’s book at these people, but I am not going to throw a grand their way either. Hey, there’s a recession ending over here, (with 0.1% growth), no need to go nuts just yet.

The recession as bad as the U.S. Revolution?

by Benjamin on February 16, 2009

Having seen numerous references to the 1929 recession and one very good argument suggesting that the 1893 crisis is a better analogy of the current crisis I was expecting some similar analogy in a recent interview with Nassim Taleb and Benoit Mandelbrot. I should have known better than try to predict what Taleb will say, as both he and Mandelbrot seems to agree that things are a lot worse than 1929… Maybe as bad as the revolution?

Mandelbrot, of chaos theory,  butterflies-to-tornadoes and fractal fame, came up with a beautiful explanation of the problem that haunts the weather forecast and economic forecasters – but with some extra bad news for the economists.

The basis of weather forecasting is looking from a satelite seeing a storm coming, but not predicting the storm will form. The behaviour of economic phenomena is far more complicated than the behavior of liquids or gases

At least the focus of our economic enquiry might now shift – lets find out what causes the storms to form and the economy to act, not try and predict the outcomes.

You can see the whole interview here:

Is it that bad? I don’t know – but no-one really does. That being said, Taleb has a very good point when he argues that our over-efficient mega-banks do two things, they reduce the odds of a crisis, but when crisis strikes it is much much worse.

Kurtosis – why empirical models are buggered

by Benjamin on February 11, 2009

Nassim Taleb and Daniel Kahneman were on stage in Munich a few weeks back discussing the global financial crisis, and in the process Taleb made the statement that not a single empirical piece of work in economics replicates out of sample. This, he argues, is because there are single instances in almost all financial and economic time-series which are hugely different than the rest of the sample, and as such skews the distribution beyond ‘normality’. These extreme single events (which are unknowable in advance) are so large that the normal distribution goes out the window, and with it most of our empirical work.

Here’s the jist of the argument: Some things fit well in the gaussian framework (in “Mediocristan”) such as weight; if you take a thousand Americans and get their average weight (177.5 lb), then adding the fattest person you can think of won’t add much to the average (Even adding Carol Jager who topped at an astounding 1,600 pounds, will only add 1.4 pounds to the average) so using the normal distribution to model weight is ok. This is not the case in ‘Extremistan’ where single observations skew the distribution. For example in the matter of income. Take 1,000 Americans, get their average income ($46,800) and the add Bill Gates. Noiw Bill is a decent enough person, really, but he made $6,300,000,000 last year and will throw the distribution completely off the scale! With Bill in the sample, the average person now makes $6.34 million ! This is ‘Extremistan’ and Taleb argues that the world of finance and economics are characterised by these distributions. (On a side-note Bill adds just over $20 to the per capita GDP of the whole United States single-handedly)

If Finance and economics are characterised by single large outliers, and the models cannot predict them, we have some cause for concern, and Taleb shows in a working paper that this is indeed the case for a sample going back 40 years on:

almost ALL  transacted macro data representing >98% of worldwide volume. I used interest rates, commodities (oil, agricultural), all available equity indices (US, UK, Continental Europe, Russia, Indonesia, Brazil), main traded currencies. I selected tradability because of its “cleanliness” compared to merely computed data. I also added some micro data: although indices encompass single equities, I processed >18 million pieces of single stock daily data, and select industry datasuch as  drug sales, movie returns, etc. (what “clean” data I could find).  While we have a plethora of data with business variables, we don’t have enough in epidemics, terrorism, wars, etc.

The results seem to indicate that for the large majority of our economic variables, we live in Extremistan.

For Silver, one single observation in 40 years (out of 10,000 data points) represents 90% of the variation away from the normal! The numbers are pretty stagering. You want 20 standard deviations distance from the mean, no problem. A good 500 examples are found in stock returns, with 5 actually passing the 100 standard deviations distance. Normal is not that normal at all.

The original video found via Stephen Kinsella’s blog
Get Taleb’s paper from Edge, and the appendix here