Good size swans and turkeys

In connection with The Black Swan, Nassim Taleb wrote a small essay called The Fourth Quadrant on The Edge. I found it much more pleasant to read than the book because (a) it directly focus on the difficulty of dealing with fat tail distributions and the prediction of extreme events, and (b) it is delivered in a much more serene tone than the book (imagine, just the single remark about the Frenchs!). The text contains insights on loss functions and inverse problems which, even though they are a bit vague, do mostly make sense. As for The Black Swan, I deplore (a) the underlying determinism of the author, which still seems to believe in an unknown (and possibly unachievable) model that would rule the phenomenon under study and (b) the lack of temporal perspective and of the possibility of modelling jumps as changepoints, i.e. model shifts. Time series have no reason to be stationary, the less so the more they depend on all kinds of exogeneous factors. I actually agree with Taleb that, if there is no information about the form of the tails of the distribution corresponding to the phenomenon under study—assuming there does exist a distribution—, estimating the shape of this tail from the raw data is impossible.

The essay is followed by a technical appendix that expands on fat tails, but not so deeply as to be extremely interesting. A surprising side note is that Taleb seems to associate stochastic volatility with mixtures of Gaussians. In my personal book of models, stochastic volatility is a noisy observation of the exponential of a random walk, something like\nu_t={\exp(ax_{t-1}+b\epsilon_t)},thus with much higher variation (and possibly no moments). To state that Student’s t distributions are more variable than stochastic volatility models is therefore unusual… There is also an analysis over a bizillion datasets of the insanity of computing kurtosis when the underlying distribution may not have even a second moment. I could not agree more: trying to summarise fat tail distributions by their four first moments does not make sense, even though it may sell well. The last part of the appendix shows the equal lack of stability of estimates of the tail index{\alpha},which again is not a surprising phenomenon: if the tail bound K is too low, it may be that the power law has not yet quicked in while, if it is too large, then we always end up with not enough data. The picture shows how the estimate widely varies with K around its theoretical value for the log-normal and three Pareto distributions, based on a million simulations. (And this is under the same assumption of stationarity as above.) So I am not sure what the message is there. (As an aside, there seems to be a mistake in the tail expectation: it should be

\dfrac{\int_K^\infty x x^{-\alpha} dx}{\int_K^\infty x^{-\alpha} dx} = \dfrac{K(\alpha-1)}{(\alpha-2)}

if the density decreases in\alpha\cdotsIt is correct when\alphais the tail power of the cdf.)power estimate

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.