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By Christine Benz | 09-10-2015 03:00 PM

A Retirement Allocation Starter Kit

Brian Huckstep of Morningstar Investment Management delves into the research and philosophy underpinning the Lifetime Allocation Indexes used in Christine Benz's model portfolios.

Note: We're refeaturing this video as part of Morningstar's January 2017 Guide to Saving for Retirement.

Christine Benz: Hi, I'm Christine Benz for It's Model Portfolio Week on Joining me to discuss Morningstar Investment Management's approach to asset allocation is Brian Huckstep--he is a senior portfolio manager for Morningstar Investment Management.

Brian, thank you so much for being here.

Brian Huckstep: Thanks for inviting me.

Benz: Brian, one thing that I've done in creating all of these model portfolios on is that I've relied on Morningstar's Lifetime Allocation Indexes to inform the asset-class positioning of the various portfolios. I wanted you to be here to sit down and talk about how you put together those indexes because you're very involved in asset allocation for Morningstar Investment Management, as well as those indexes. So, let's talk about the overall philosophy, at a very high level, that guides those indexes' asset allocations.

Huckstep: The lifetime indexes glide from a more aggressive equity stance to a more conservative one--as do many target-date glide paths. We spend a lot of time working on the intra-asset-class allocations within there. So, not only do they glide from high equity levels to low equity levels as they get closer to the retirement target date, but within there, we're really thinking hard about those intra-asset allocation mixes and what the most optimal mixes are.

We're using a number of different optimizers. We use a traditional optimization approach with mean-variance optimization with forward-looking expectations for each asset class. It's taught in every portfolio-management class today in business school--trying to maximize return for each unit of risk. At the equity level, it's selective for the glide path. We know the expected volatility, and so we select those asset classes that maximize return. But then we go further. We have [mean-conditional value-at-risk] optimizers--is what we call it. We're trying to minimize the drawdown--the tail events. So, we have optimizers that do that, and they're looking at concepts like skew and kurtosis--those are the tail event.

So, if you think about 2008, we want to minimize the impact of those events on the portfolios. And then there are number of other optimizations we're doing. We do liability-driven optimization, which is a way to consider the liability in retirement. So, when people retire, the typical U.S. investor is spending his or her dollars in the U.S. They might go outside the U.S. or buy things from other places, but the majority of the money is going to be spent in U.S. So, we think about inflation and how that liability is changing over time. If inflation is high, that liability is going to be higher, in nominal terms. So, we are thinking about ways to hedge against that. So, liability-driven optimization is a way for us to build the best model we can to hedge against that inflation risk in the U.S.

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