John Yang

To tackle the first thing you've said about the individual asset classes return distribution, we first built the historical return series for portfolios that represent asset classes.

Then we model out its distributions based on its historical probability of having different returns.

It's also important to mention that we are building those proxies for those asset classes using indices.

For example, we're using the MSCI EAFE Investable Market Index for approximating international equities.

And we combine them into portfolios to make sure it realistically represent one asset class, for example.

And once we do that, we learn the historical distributions.

We model out the correlated structure between asset classes.

Through doing that, we have our shocks for the market that we are trying to generate.

At that point, those data are still normalized.

They're in standardized space.

And what's good about this model is it allows you to impose the expected returns that you believe to be true.

And that can be adjusted from time to time when you're planning.

Yeah, exactly.

I'm also going to go through that as well and how we do that.

But I think we can first look at how we are modeling out the correlation structures and the distributions of the return of every single asset class.

Essentially, like we are not only thinking about modeling those two things, the correlated structure and the marginal distribution of each asset separately.

When we're implementing it, we're also like dealing with them separately using two separate steps.

I think essentially we're asking two questions here.

First is how does each asset class work?

behave on its own.