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macrocephalopod

@macrocephalopod

Paul Allen, Vice President M&A, Pierce & Pierce (Sie/Hir) | Vegan | Silence is Violence | Women’s Rights Are Human Rights | ACAB (Assigned Cephalopod At Birth)

Joined December 2020

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    1. macrocephalopod‏ @macrocephalopod 19 Mar 2021
      Replying to @goldstein_aa

      VIX futs not that useful as they are forwards on 30-day realized variance, so include (i) vol risk premium (forward vol an upward biased estimate of future realized vol) and (ii) variance risk premium (var is convex wrt vol) and both of those are time-varying

      1 reply 0 retweets 8 likes
    2. macrocephalopod‏ @macrocephalopod 19 Mar 2021
      Replying to @macrocephalopod @goldstein_aa

      i.e. there are two sources of bias and unless you have a good model for the size of those biases they add additional noise to your vol forecast

      2 replies 0 retweets 5 likes
    3. Robot James  🤖 🏖‏ @therobotjames 19 Mar 2021
      Replying to @macrocephalopod @goldstein_aa

      Of course, you can remove one of those biases by using ATM IV instead of VIX (which is derived from the var swap) This *can* be useful cos it "knows" about future events, whereas your realized estimate/forecast won't unless you adjust it yourself.

      1 reply 0 retweets 5 likes
    4. Robot James  🤖 🏖‏ @therobotjames 19 Mar 2021
      Replying to @therobotjames @macrocephalopod @goldstein_aa

      Looks like you want to use this for Kelly sizing. In practice, your problem there is mu. Your return estimate will be a ton worse than even the very dumbest vol estimator

      2 replies 0 retweets 7 likes
    5. Adam Goldstein‏ @goldstein_aa 19 Mar 2021
      Replying to @therobotjames @macrocephalopod

      Well, this gets into a few other aspects I'm working on / been thinking about for years. There's quite a bit of evidence that S&P 500 *long-term* (10-yr) geometric returns are predictable based on valuation metrics. Can be modeled as Ornstein-Uhlenbeck process.

      1 reply 0 retweets 1 like
    6. Robot James  🤖 🏖‏ @therobotjames 19 Mar 2021
      Replying to @goldstein_aa @macrocephalopod

      Don't disagree on the effect, generally. But there's probably a lot less evidence than you might think. Think about how far you're looking forward and back with feature and target calc windows there. How many independent observations do you actually have?

      1 reply 0 retweets 2 likes
    7. Adam Goldstein‏ @goldstein_aa 19 Mar 2021
      Replying to @therobotjames @macrocephalopod

      Yes, totally agree. We seem to think a lot alike! Are you familiar with John Hussman? He blocked me on twitter because I talked too much about that issue. He even wrote a blog post referring to me and this issue as "zombie troll bait from hell". :-)

      2 replies 0 retweets 3 likes
    8. Robot James  🤖 🏖‏ @therobotjames 19 Mar 2021
      Replying to @goldstein_aa @macrocephalopod

      Robot James  🤖 🏖 Retweeted Robot James  🤖 🏖

      Hahaha. Badge of honor.https://twitter.com/therobotjames/status/1372384046349623301?s=21 …

      Robot James  🤖 🏖 added,

      Robot James  🤖 🏖 @therobotjames
      Replying to @AgustinLebron3 @M1tchRosenthal @robbevantillo
      Good stuff guys... Here's a tip. If your scatterplot looks like it has "trails" in it... like something John Hussman would create... then you have overlapping data, and a lot fewer independent data points than you think. pic.twitter.com/VhFtbpzGi1
      1 reply 0 retweets 2 likes
    9. Adam Goldstein‏ @goldstein_aa 19 Mar 2021
      Replying to @therobotjames @macrocephalopod

      Amazing!!! You're the first person (besides myself) I've seen who talks about that "trails" issue! Bugs the crap out of me that almost nobody notices how strange those scatterplots look.

      1 reply 0 retweets 4 likes
    10. macrocephalopod‏ @macrocephalopod 19 Mar 2021
      Replying to @goldstein_aa @therobotjames

      AQR (I think?) had a paper basically debunking the cape ratio for market timing by pointing out that using overlapping 10Y returns on the lhs of a regression and trying to adjust t-stats with newey-west is hopelessly optimistic

      2 replies 0 retweets 8 likes
      macrocephalopod‏ @macrocephalopod 19 Mar 2021
      Replying to @macrocephalopod @goldstein_aa @therobotjames

      Instead you should use non-overlapping 1M returns on the lhs but smooth the regressors with a 10Y moving average, which gives the exact same regression coefficients but now you have non overlapping samples so you can do a normal t-test

      12:43 PM - 19 Mar 2021
      • 11 Likes
      • woodytama experquisite Tong Robert Martin nima drew. phattails Yannick Kälber
      3 replies 0 retweets 11 likes
        1. New conversation
        2. macrocephalopod‏ @macrocephalopod 19 Mar 2021
          Replying to @macrocephalopod @goldstein_aa @therobotjames

          And if you do that you find that the evidence for CAPE predicting equity returns is incredibly weak, t-stat is like 1.5 or something

          2 replies 0 retweets 4 likes
        3. Adam Goldstein‏ @goldstein_aa 19 Mar 2021
          Replying to @macrocephalopod @therobotjames

          Guess you just answered my previous question...

          1 reply 0 retweets 0 likes
        4. Show replies
        1. Adam Goldstein‏ @goldstein_aa 19 Mar 2021
          Replying to @macrocephalopod @therobotjames

          Hey, you've got some interesting ideas. Have you tried that and compared to the "standard" way of doing it (similar to Hussman)?

          0 replies 0 retweets 0 likes
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        2. Adam Goldstein‏ @goldstein_aa 19 Mar 2021
          Replying to @macrocephalopod @therobotjames

          Wait - isn't it possible 1M returns aren't very predictable but 10Y returns are? That's the whole theory of long-term valuations: short-term is dominated by noise, long-term much more predictable.

          1 reply 0 retweets 2 likes
        3. macrocephalopod‏ @macrocephalopod 19 Mar 2021
          Replying to @goldstein_aa @therobotjames

          That’s the cool thing about smoothing the regressor with a 10Y MA — it incorporates long horizon predictability but in a way which makes the statistical inference more reliable! The regr coefficients are identical, it is testing exactly the same thing but with better inference.

          1 reply 0 retweets 4 likes
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