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by aubreyclayton 2632 days ago
So... I wrote this. Maybe I can clarify a little what I meant by the statement "Since Silver’s forecasts begin with probability models, it’s safe to assume they obey all the rules, including Bayes’, and would be arbitrage-free.", since this seems to be confusing some people:

What I'm addressing here is Taleb's claim that Silver's probabilities would allow for arbitrage (i.e., riskless profit, not just profit on average) if turned into betting prices. This is in the sense of arbitrage-through-time, buying low and then selling high. As I discussed in the piece, an old argument due to de Finetti says that if prices are arbitrage-free they must satisfy the equations of probability, meaning you can in some sense think of the price as giving a probability of the outcome. For time-dynamic arbitrage the relevant equation is Bayes' Theorem. All I meant by my statement above is that the converse to de Finetti's argument is also true, trivially. If prices obey the equations of probability they are automatically arbitrage-free. And since Silver's probabilities begin life as probabilities, they satisfy all the relevant equations (one would expect).

Technically, the way we'd express this in modern finance is through the Fundamental Theorem of Asset Pricing, which says a (complete) market is arbitrage-free if and only if there exists an equivalent measure under which asset prices are martingales. Silver's probabilities are necessarily martingales, just because of the way conditional probability math works, so unless Taleb can claim that his and Silver's probabilities aren't equivalent, meaning they disagree on what events have probability zero, then there is no chance of arbitrage. That's just the mathy way of saying the same thing I said in the post.

There are many other possible errors Silver could be making, and many other possible criticisms Taleb could have made but did not. In this case he wrote a paper claiming a mathematical result that just isn't true.

Hope that's helpful!

2 comments

Aside (but not really): guessing this is you? https://www.youtube.com/watch?v=P6P1rjJuD_M A totally awesome set of lectures... highly recommended.
Yep! Thanks!
Needing help with one part of this. Say in the simplest case of a coin flip and two instants in time I assess probabilities:

  t=0: P(heads) = 0.75, P(tails) = 0.25
  t=1: P(heads) = 0.60, P(tails) = 0.40
These are consistent (or behave as probabilities or what have you) at each time (separately), so de Finetti's argument covers each (separately). Does this alone somehow protect from arbitrage over time? Or is the martingale stuff in the next paragraph, though postured as only a technical rephrasing, essential? Unsure since that part's over my head.

The above's enough to pose the question, but continuing for concreteness: if a buyer can determine any "significant" pattern in my assessments over time--for instance maybe my past assessments have been routinely seen to tend to a uniform distribution over time--aren't I still vulnerable to arbitrage, or else what's protecting me?

So, if I knew ahead of time what your price was going to be tomorrow, I would have a clear arbitrage strategy. But that's not a fair example. The question is: what do I know at t=0 about what your price could be at t=1?

A better example is this: suppose you're creating a probabilistic forecast of the chance of a coin coming up Heads twice. Suppose this represents a win. You say, "according to my model, P[H1 and H2] = 0.3." And suppose you also say P[H1] = 0.4

Then I ask you: imagining the first flip comes up Heads, what will your price for the second Heads be then? The only arbitrage-free price you can quote me is your conditional probability: P[H2 | H1] = P[H1 and H2]/P[H1] = 0.75. Anything else allows me to buy and sell bets and make a riskless profit.

Now, note that if H1 occurs, P[H2 | H1] is now the updated chance of getting H1 and H2. You observed some information and updated your probability accordingly. Also note that according to your model, the chance of H1 happening was 0.4, so the possible prices of "H1 and H2" after one coin flip were 0.75 with probability 0.4 and 0 with probability 0.6 (if the first flip were tails, "H1 and H2" is impossible). So the expected price of "H1 and H2" after one flip is (.75)(.4) + (0)(.6) = 0.3, which is also your price at t=0. That means the probability/price is a martingale according to your probability assignments, which, according to the Fundamental Theorem of Asset Pricing, again means no arbitrage is possible, unless we disagree on what events have probability zero.

So, the election forecasts are like this except we don't get to see all the inner workings. Nate doesn't quote things like the probability of the polls moving by a certain amount each day. We just get to see things like P[H1 and H2] before and after the first coin flip. But as long as it's possible that those come from a consistent set of conditional probabilities and Bayesian updating, there's no chance for arbitrage.