The Grant Williams Podcast Ep. 129 - Marvin Barth

**Grant Williams** (0:10)
Before we get going, here's the bit where I remind you that nothing we discussed should be considered as investment advice. This conversation is for informational and hopefully entertainment purposes only. So while we hope you find it both informative and entertaining, please do your own research or speak to a financial advisor before putting a dime of your money into these crazy Thematic Markets. You're about to listen to a special preview edition of The Grant Williams Podcast featuring my very special returning guest, Marvin Barth of Thematic Markets. Marvin is one of the most popular guests I have on the show. He's been on several times and each time he generates a fantastic response. His work is incredibly thought-provoking. You can find out all about what he does at thematicmarkets.com. He's on Substack under the Thematic Markets tag and also on Twitter at thematicmarkets.
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Marvin, my friend, so good to see you. I'm immensely grateful for you for doing this on your self-imposed holiday break in August. So thanks again for taking the time to be with me.

**Marvin Barth** (1:50)
Hey, it's my pleasure. And you're the one person I would definitely do this for, because it's always a pleasure to come on your podcast. It's the best out there.

**Grant Williams** (1:59)
We are very kind. And in your absence from the day-to-day grind of this, you've been putting out some library archive articles, which, as I think you made the point, they are testament to the longevity of your research and how useful it is to be able to go back and look at a library and pick apart things. And I love the fact that one of the big things you've been talking about is the things you got wrong and the things you didn't call right. And I think that's so important to do because we're all looking at tea leaves, we're all trying to guess the future. It's the rigorousness of the analysis that is the most important thing. The outcome can always go the wrong way. Your best bet is to get 51% of your guesses right and you'll be doing pretty well. So what I'd like to do is start somewhere a little bit oblique. You and I have mutual friends in the Convex Strategies guys, Dave Dredge and Julian Lagayas there. One of the things that they've been banging on about for years is that this whole edifice of modern policymaking rests on models that assume that the world is normally distributed and pretty well behaved. There's no fat tails, there's no long memory, there's no volatility clustering, stuff like that. I think the deeper point is that institutions can't really learn without accountability. So central banks just keep making the same errors and calling them all unforeseeable. We've seen that over and over again, right?
Now, you wrote two pieces about Kevin Warsh, The Warsh Cycle, which I loved. It was a great title. I'm a sucker for a great pun of a title. And what struck me was that Kevin's four stated principles, which were epistemic humility and sticking to that mandate, a framework rather than good old forward guidance, and perhaps above all accountability, are almost like a point-for-point restatement of that idea of Dave's and Justin's, but couched in central bank language. And I get to your list of task force co-heirs, and there's a good buddy, Bill White, on the Inflation Framework Group.

**Marvin Barth** (3:53)
And it was a wonder, right?

**Grant Williams** (3:54)
He's a man who spent two decades arguing that financial volatility is actually endogenous to policy choices, and it's not something that actually comes in from the outside. So I guess there's a long way of saying this, my question is this.
Is Warsh actually mounting some kind of serious intellectual challenge to how central banks understand both risk and uncertainty? Or is he a hawk in sheep's clothing, a hawk with a reform agenda who's found some kind of useful language to address him, because he had to?

**Marvin Barth** (4:27)
That's a good question, especially after the July FOMC. So, look, I think he's been consistent enough in his language and frankly in his predictions that we have to accept that he actually does share a lot of the sort of ontology or belief systems that I think you and I and our good friend, Dave Dredge, all believe that, hey, look, the world is a lot crazier than anything that you can strictly model. And so you have to, that doesn't mean throw out the model. The model is useful. It helps you train what is the sort of average expectation, right? But you need to accept that all models are wrong. I always think of the Box-Jenkins test, but the statistician thing was David Box, famously said, all models are wrong, but some are useful, right?