Tail event: Difference between revisions

Put another way, though the “interconnectedness” of similar transactions means they do ''not'' have the quality of independence that [[normal distributions]] require, most of the time it's close enough: the information is chaotic — as traders say, “noisy” — in the immediate term, here the dissimilarities between trader motivations are most pronounced, but over a large aggregation of trades and a longer period of time a “signal” emerges. This is what Black Scholes, volatility and convexity models track. As long as all traders are using the same aggregated market information — and the market works hard to ensure they are — a “normal” probabilistic model<ref>I am working hard not to use the intimidating term [[stochastic]]” here by the way.</ref> works fairly well. It’s not a bad ''model''.  

But there is a second order sense in which the earlier and later trades ''are'' related, in practice: the later participants know about the earlier trade, and its price — it is part of that universal corpus of market information, deemed known by all, that informs price formation process: all can thereby infer the trend from prior trades — and use this abstract information to form their [[bid]] or [[ask]].  

When you bounce a ball, friction, energy loss, structural imperfections, impurities in the rubber and environmental interference frustrate the conditions needed to satisfy the “[[nomological machine]]”: the required conditions for Newton’s laws to hold are not present, so we let it pass when our bouncing ball never quite conserves momentum. It is close enough and usually no one is counting in any case.  

This is the sense in which, as {{author|Nancy Cartwright}} puts it, ''the laws of physics lie''. They ''don’t'' represent what happens in the the real world.

The same applies to the statistical techniques for we use to measure behaviour of the market. Much of the non-homogenous behaviour cancels itself out. Where it doesn’t — where it creates a persistent variance from how a normal distribution would behave over time, we can model that, too, with measures like [[volatility]]. We use probabilistic —  that is, independence-assuming —techniques to model these second order corrections like volatility, too.  

Why do we make these assumptions of independence and homogeneity of events? Because otherwise, we could not predict at all. A human being with free will and moral agency does not obey laws of probability. She can put a coin down heads up every time. She can go out of her way to deliberately frustrate any prediction we make.  

It's not just that individual humans ''can'' do that: they ''like'' doing that. Likewise, you can't draw models that predict the behaviour of dissimilar objects. Statistical rules require homogeneity. The odds of rolling a six hold true for fair dice, but not for carpet slippers or fish.  

But his is the magic, so claimed, of [[big data]]. All those idiosyncrasies cancel themselves out and leave us with a set of basically homogenous participants. ''You'' night not like rice pudding or [[lentil convexity|lentils]] but over a whole population, a fairly reliable proportion of the population does. We can ignore the individual. It is our dystopian lot that our social systems increasingly are configured to ignore us.

{{Quote|“Yes! We’re all individuals! We’re all different!”

 

“I’m not.”

:—''Monty Pyhthon’s Life of Brian''}}