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.
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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
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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.
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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?
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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". :-)
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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 🤖 🏖 @therobotjamesReplying to @AgustinLebron3 @M1tchRosenthal @robbevantilloGood 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/VhFtbpzGi11 reply 0 retweets 2 likes -
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.
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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
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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
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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.
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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.
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Details of how the regression works and why it is testing the same thing are in the paper, but the core idea is that smoothing the regressors over N months and predicting 1 month is identical to forecasting price changes over N months with an unsmoothed regressor.
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Replying to @macrocephalopod @therobotjames
Ahh... this method is in the paper I referenced, I guess you're saying. Ok, obv. I need to go back and read that paper again! It's been a long time.
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