- Optionality gives you convex payoffs (very rarely optionality <=> convexity doesn't hold)
- Example of optionality: trial and error (T&E)
- How to find hidden optionality?
A. Not easy. You need to train for it. Domain dependent, but T&E gives you optionality
3/n #RWRI 14
- Optionality implies an asymmetry. Can you have optionality without a sucker on one side?
A. Yes. Trial and error
-Low diversification makes you fragile. If you don't have a tail hedge, you'll eventually blow up.
- Model error: How to stress test your model?
Use the (1+a) rule. For x, compute x(1+a) and x(1-a) and see what happens.
- Beware of BS predictions like "x has probability 0". It can't be 0 because it misses error on the left
E.g @NateSilver538's 2016 predictions
- Extremes are not the same as outliers
Extremes: An event within the range of variation. Basically, unusual max/min (eg someone who is 2.1m tall)
Outlier: An event out of the natural range of variation (eg. 3m tall)
7/n #RWRI 14
- How to correct outliers?
A. Directly (just change the value), with the avg of nearby values, or by removing them.
- How to correct extremes?
A. Don't. You'll miss important information about the distribution. Don't even think about it.
- On predictions: Is not about *when* something is going to happen, but whether or not that something is possible. Remember what we have been saying since day 1. It's about the impact.
Today we continued talking about fat tails and convexity. @financequant's talk had to be postpone because power went out in his location.
Personal highlights
- We have said before that we can't forecast fat tail variables. But why?
2/n
Ans: Because fat tails are determine by one or a few points. The fatter the tail, the bigger the effect of one observation.
- This argument also explains why correlation has no meaning under fat tail variables. The relationship between two fat tails is given by a point
3/n
that you probably don't even have. Hence correlation is meaningless, or as Nassim said the first day, corr is not corr. Here's a paper by Nassim related to this.