John Yang

That is what we mean by the marginal shape score.

It considers things like quantile gap, skewness, kurtoltis, and downsize loss measures like CVAR.

And the second metric we look at is if our model does better on the tail.

This measure is mainly driven by the tail quantile gap.

And third, we also ask, does the model still preserve how assets move together?

So that is the correlation structure that I talked about at the start of this presentation.

We are measuring this using a basket of correlation measures like Pearson and Kendall, which some argue are more realistic.

We're basically combining all of them together to capture all of it.

That's actually like exactly what we were expecting.

Because for marginal and tail shape, that's where our method is mainly improving in.

That's because we're using the empirical distribution of return rather than assuming a bell curve shape.

That's why it does a lot better in marginal and tail shape.

But for co-movement, our goal is to be on par with the Gaussian method.

That is because in the Gaussian method,

The goal is to optimize for that correlation structure that was given to the model.

So it is doing a very good job at it.

And we are actually surprised that we didn't have to compromise any of the cold movement score to get a better marginal Intel score in that sense.

But yeah, it's great to have.

Well, we've only been working on this for the past five months, and this is like far from perfect.

There are a lot of things that we still can do and will do in the coming semesters.