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Nassim Taleb On XIV And The Problem With Forecasts

Lots of commentary out there on the S&P 500 collapse, but although some may disagaree I think Taleb probably has the most interesting take on this … Nassim Taleb on why everyone got the market crash wrong, below is an excerpt followed by an informational transcript, video and summary below:

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Academics cannot get the idea that you don't have to be right about the world. You has to make decisions that are convex other with the sort of that makes sense and in fact you don't have to be. And this also explains why paranoia is entirely justified. If you're F of X as concave so you overpricing underpricing probabilities is not what matters. What matters is your payoff. So let's see what happened here.

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Antifragile explains why understanding x is different from f(x) the payoff or exposure from x. Most of the harm/gains come from f(x) being convex or concave not understanding x. Forecasting is off an average, and average is for academics and other morons. This video illustrates the point with XIV that went bust while being correct about volatility --and why people who make money are usually wrong.

By Nassim Taleb, CC BY-SA 3.0, Link

 

Friends. Let's discuss the problem forecasting one same time explaining the XIV ex trade the VIX. A bunch of people stress of the contract saying the VIX is overpriced mispriced as volatility is over forecast of what's going to happen. The variation in the market the contract. Is poorly designed let's make money off of that. And they were right but they were destroyed and lost all of this money. Why. Because they didn't realize that forecasting has nothing to do with PNL nothing to do with where you live. What matters is if they are watching. So these people are right. You invest if you invest invested say twenty five dollars you would have made some money. I think a high of 46 dollars overtime. And in one one a couple of days they lost everything. It's now at Faisal's and probably these senators will be lower. So what what did they do that was wrong. What they did is not understand that being right on a random variable X doesn't mean making money out of it your pay or function of x needs to be aligned with what you're forecasting. And in fact the function of x is never X and X can be very complicated and realize most people think you've got to focus on X or academics or other idiots. Of X is what you what you focus on when you make the decision. It's much easier to understand your function of running a variable than a version of itself. That's what I said. It's what antifragile. Very few people are getting it.

[00:01:57] Academics cannot get the idea that you don't have to be right about the world. You has to make decisions that are convex other with the sort of that makes sense and in fact you don't have to be. And this also explains why paranoia is entirely justified. If you're F of X as concave so you overpricing underpricing probabilities is not what matters. What matters is your payoff. So let's see what happened here. Let's stick your pal function. Remember you have a function of this appoint anyo. You know pretty much everything in life is some nonlinear function that can be expressed through and putting on the model as you add these functions. And of course you can do a more sophisticated way. That's pretty much what it is. This is a wait for us to understand first. Not nearly so. You plot one X square. You see what happens here and I build a function of a vector x. Cap X is a vector is the mean 1 minus X square. So what does it do here. You say with a vector like x 0 1 1 1 1 1 0 is 1. And as for next year it would be zero. OK so that's a function. So let's do a thought experiment another thought experiment x x itself C is 1 1 0 1 1 1. The mean of X is point 7 1 and of course are going to make money because the same is going to be lower than 1 1. Is your price at which you're forecasting otherwise. Or are you starting off with the lower than 1 or higher level and you have a concave payoff.

[00:03:45] So you think the average is going to be lower than one your average is lower than one you may point to eight because it's submitted in a certain way. That sort of works. But now if I take the same average was X to 0 0 0 0 0 and then 5. In other words it all came on a variation came 1 from 1 observation 1 2 3 4 5 6 7. A sentence of evasion. It summarizes everything all the properties. Then let's look here. Menas x. 2 is n but ethnics 2 is negative. Although you're right on X you're wrong in your PR function you're going to be harmed big time. Like I often say. The idea. I've never seen the rich forecaster good forecasters. Are sport because they don't get it that the average forecast is what matters. What matters is not to be harmed by these cavities. Now let's take an extreme case. X 3 is I have 9 9 9 zeros and then one of the ration at say a 1000. Okay that's x 3. And. The mean of x3 is 1. Visibly short that one. And yet it was not 1 9 9. Even if that was 300. Okay a lot of zeros and the last observation is 300 K. The mean is going to be points 3 and you still lose money. Okay you're the variable you're forecasting. Game 75. Percent 70 percent below what you are forecasting in the right direction and you still got hammered. So this is to explain the fallacy of forecasting in a very short note.

[00:05:46] Thank you for listening to me and have an excellent weekend.

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