Benjamin Felix

Correlations tend to go up during bad times.

And so you guys were taking all of those empirical realities and trying to figure out a way to generate synthetic data that reflects those properties.

No, I mean, I thought that was great.

We're simulating each asset individually.

As you said, they each have their own personalities, their own characteristics.

And then we care about how they behave relative to each other through time.

It's the correlation, but it's also stuff like everything going badly at once, which can happen sometimes even with stocks and bonds, which we've seen in pretty recent history.

And then the tails, which as you said, in a normal distribution, they really don't get as much attention as they probably deserve.

That's really cool.

So the way that you set it up, the shape of the distribution and the correlation properties and the tails and all that stuff gets defined using historical data.

But then once the shape of those distributions and the way that they work with each other are defined, we can put in whatever market assumptions we want.

Very interesting.

So we don't have enough data to properly capture the tails.

And so you found a way to sort of manufacture tails that are more realistic.

Yeah, that's very cool.

Yeah, I love that.

That was one of our big concerns with starting this project is that we change our expected return assumptions twice a year.

We always restate them based on the way that we calculate them.

And we need to be able to take this return generating process and input what our current mean expected return or current standard deviation are.

And you guys solved that in a really nice way.