Benjamin Felix

So it's basically like we know returns aren't random.

Empirically, you can see that by looking at real data.

We had been using randomly generated returns to do retirement modeling and stress testing.

So you guys were taking, if we look at real historical data, what has actually happened?

So we know things like

There tends to be a little bit of mean reversion like you were talking about.

Volatility tends to cluster.

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.