John Yang

For us, there are three natural extensions that we're thinking about.

The first is dynamic correlation.

Right now, we're assuming a statistic correlation structure.

Essentially, what we're assuming is that the average relationship between asset is stable across all environments.

But in real markets, the relationship between asset classes changes.

For example, think about the recent tension in the Shade of Hormuz.

The oil price, inflation expectation and bond yields, equities, currencies, those things are essentially responding to the same macro event and they're interacting in the way they do right now.

But that is very different.

How they interact right now is very different from more stable time when inflation fear is not a trigger for the market, for example.

By using a dynamic correlation, we can model each period separately and better replicate the history with more realistic test cases.

And the second thing is something we weren't able to address with our current model, that is volatility clustering.

Ben mentioned at the start of the podcast, at the start of our conversation, that some of the bad days arrived together.

For example, in 2008, you don't just get one bad trading day.

Once there's a fear in a market, people sell everything and it'll sell off continuous for a long time.

And those clusterings are not modeled in our current approach because we're assuming independent draws when we're simulating.

So that is another extension we're trying to work on.

Our current idea is potentially using a GARCH-style model that deals with time series.

And by incorporating the time dimension, we can solve that problem.

And the third thing is not as much of an extension to our model, but rather an alternative path that we will explore in the future.

That is gang models.