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Rational Turbulence

Kent Osband, author of “Pandora’s Risk: Uncertainty at the Core of Finance,” sent along this paper:

Fluids are turbulent when tiny differences in space or time make for gross differences in behavior. The mathematical signature of turbulence is an endless moment or cumulant hierarchy. Bayesian tracking of continuous-time processes turns out to have a similar mathematical structure. As a result, tiny doubts about regime change or tiny errors in estimation or calculation are prone under stress to balloon into gross differences of opinion. In effect, reasonable people are bound to disagree. This finding has profound implications for our understanding of financial markets and other instruments of social learning. In particular it explains forecast degradation, frequent trading, excess volatility, and GARCH behavior without imputing widespread irrationality. Rational learning makes markets turbulent.

I have not tried to evaluate Osband’s argument but the general idea is very appealing to me, and I hope that people with more knowledge than I will look into this.


  1. numeric says:

    Sounds like chaos theory. For those who don’t remember, there was a time about twenty-five years ago when every other economics paper dealt with this. It even made it into a portrayal of an academic in Jurassic Park! Completely non-predictive and useless for pretty much anything (so why was it dropped from economics?) One should be very suspicious of applying physical models to social phenomenon–Black–Scholes brought us the Great Recession (a theory only an academic could love! No risk on risky assets through modeling).

    • Feral Oink says:

      This I couldn’t agree with more:

      One should be very suspicious of applying physical models to social phenomenon

      But this:

      Black–Scholes brought us the Great Recession (a theory only an academic could love

      well, less so. Black-Scholes did work quite often. I should know, I used it. Don’t be over hasty to heave everything quantitative out the window insofar as it pertains to finance. Black-Scholes (oh dear, never noticed the abbreviation for that would be “BS”) is worthwhile as one among several criteria for valuing options and warrants.

      I did have a good giggle over that abstract though. About tiny doubts ballooning into gross differences of opinion under stress! I thought it was all-humor at first, what with the inundation of network and complexity theory as applied to financial crises and corporate webs of deceit, influence etc. recently. But the paper is actually a good faith effort. I just don’t think Bayesian approaches, nor much of anything else, is going to accurately capture people’s behavior under trying circumstances. (As for irrational fears, I’ve been increasingly worried that soon, NO research results, none at all will be reproducible anymore…)

      • numeric says:

        The actual economic decimation was almost certainly caused by too much capital looking for “return” in the last decade (the constant refrain from those with money was the lack of “return”). Anytime people are looking for extra-market returns there will be people willing to give it to them through methods of increasing risk. People used Black-Scholes as a convenient method for doing this, and like a large part of the social science literature, the quantitative sophistication made the invalid seem valid (of course, Goldman Sachs made a lot of money shorting these instruments, which didn’t keep them from pushing them to clients at the same time–the point is, they weren’t taken in by the bogusness of this quantitative application–at least in this instance). The Commodity Futures Trading Commission (CFTC)
        was kept from regulating derivatives in the late 90’s and the (female) head was forced out by Summers, but her remarks on LTCM before it collapsed were prophetic–these people have 4 million dollars covering a billion dollars in bets. For statisticians, when thinking derivatives, think gamblers ruin!

    • Jonathan says:

      The problem with chaos theory applied to economics, even if it’s factually correct in its description of the world, is that you end up with the conclusion that extreme sensitivity to initial conditions means that you are practically unable to predict anything. This counsels simple despair and ends economics. So instead, we proceed on the basic assumption that no matter how many unpredictable abrupt phase changes the world may be in for, the behavior in between is predictable, and the development of flexible tools to deal with unpredictable changes is essential, as is the development of such tools as real options theory to counsel adaptability.