Benjamin Felix

I thought John was great.

This was his first podcast ever.

And I thought he spoke really well.

Well, it came back so quickly, right?

That's one of the crazy things about higher frequency of return data is that you can see these crazy events.

Even with daily returns, it can be even crazier.

There can be these massive short-term drawdowns that bounce back.

And it's like, if you look at annual data, things just look a lot smoother than if you look at monthly and then likewise with monthly versus daily.

So that finding I think does make sense.

Totally.

For that part, basically what we found was that the new simulation method makes the return distributions look more similar to actual historical return distributions than what we had been doing previously.

Is that a good summary?

We could see that when we do our expected returns update, which we do twice a year.

So it's not a huge deal.

Did you look at the impact sorted by age or like length of the projection?

Interesting, you're correct.

That's an important point.

And this is one of the cool things, as John mentioned about the model that they built is that we are still using the same expected returns.

We've just changed the way that we're generating the distribution around those expectations.

So as you said, we're not shifting the distribution one way or another, we're just changing the shape of the distribution to be more historically accurate, realistic.