The Creator of VaR Explains How Large Banks Measure The Risk Of Their Own Portfolios

Tracy Alloway (0:09)

Hello, and welcome to another episode of the Odd Lots Podcast. I'm Tracy Alloway.

Joe Weisenthal (0:15)

And I'm Joe Weisenthal.

Tracy Alloway (0:17)

So Joe, one thing we talk about quite a lot on this podcast, especially lately, has to be volatility.

Joe Weisenthal (0:24)

Yeah, absolutely. We didn't see much volatility in any markets in 2017, and it's picked up more in 2018, though not dramatically, but we certainly have seen some very interesting episodes across a range of markets, making things quite a bit more interesting.

Tracy Alloway (0:42)

Yeah, I love that we have episodes when there's no volatility, and then we have episodes when there's lots of volatility. But do we ever stop to ask about the backbone of volatility measuring or volatility models?

Joe Weisenthal (0:56)

No, we don't really. I mean, we talked a little bit. It's like we always sort of talk around it, don't we? Like we talked about how the people blew up when they were shorting the VIX and stuff like that.

But it is true that to some extent, we feel like these measures of volatility are like handed down to us on stone tablets on the top of a mountain rather than something that people had to sort of come up with on their own.

Tracy Alloway (1:23)

Yeah, so today we're going to be talking about the origins of a volatility model that is essentially the backbone of a lot of Wall Street risk management and a lot of the volatility modeling that we've seen in recent years. It's something called value at risk, and I think you're already familiar with it.

Joe Weisenthal (1:43)

I have a vague idea of what it is, but I think it's something like if you have a big portfolio, you want to measure what is a sort of reasonable amount you might expect to lose on any given day over some time period to see how risky your portfolio is.

Tracy Alloway (1:58)

That's pretty good, actually. That's impressive.

So just to harden-

Joe Weisenthal (2:04)

Thanks, Tracy. I'm glad my very rudimentary definition was enough to impress you.

Tracy Alloway (2:09)

No, but it's true. I mean, that's it. It's the amount of money that you might expect to lose at a given confidence level over a certain time period. So for instance, if you and I were running Odd Lots Capital, which we totally should do at some point, and we had a one-day value at risk of a million dollars, say at the 95% confidence level, that would mean we would expect to lose more than a million dollars on one day out of 20 at a 95% confidence level. So this model, it was invented in the 1990s by JPMorgan, and it spread throughout all the banks, and it became the backbone, as I said, but it's also intensely controversial, and you see lots of criticisms of it.

Nicholas Nassim Taleb is probably the most famous critic of the model, but I should say today we're going to speak with someone who not only invented value at risk, but can also explain what it is that the thing does and what it is that it doesn't do.

Joe Weisenthal (3:11)

Great. Well, I really don't know much more about it beyond what I told you, so I am looking forward to learning more.

Till Guldimann (3:17)

Thank you for watching.

Tracy Alloway (3:25)

With more. So without further ado, let's bring on our guest for this episode.

It is Till Guldimann. Till, thank you so much for joining us.

Till Guldimann (3:32)

You're welcome, glad to be with you.

Tracy Alloway (3:34)

So Till, maybe just to begin with, you could walk us through your early career history. I mentioned that VAR, Value at Risk, was invented at JPMorgan, and you were obviously at the bank when you invented it. But how did you end up there?

Till Guldimann (3:51)

So I was a banker at JPMorgan, and in a banking career in those days, so you were sent around the world in different jobs and different locations. And my job at the time before this was trading room in Hong Kong. And I was in charge of that. And I was approached from a numerical viewpoint rather than from a making money viewpoint. And as a consequence, I didn't make that much money, but I had collected a lot of numbers. So they said, well, perhaps you're better off coming back to New York and apply the number skills and we make the money, which I accepted and I became head of Asset Liability Management, which at the time was being in charge of the balance sheet risks of the bank.