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Decline - Redirect me to

Pensions & Investments recently reported that mutual fund assets in DC plans total $3.19 trillion and target date funds have an $877 billion share of it! Assets in these strategies are surging – including collective-trust versions – given their use as a qualified default investment alternative (QDIA).

A target-date strategy makes a great QDIA, but can fiduciaries improve them to help participants secure retirement? We think so, and the change isn’t that radical.

Like all asset allocation models, target date strategies expose participants to a lot of risk throughout the glide path. Why? Because equities often remain the largest source of risk in any asset allocation model. The key to improving target date strategies is to mitigate that risk.

Common Risk Remedies Fall Short

Typically, you might attempt to solve this challenge through diversification, like adding alternatives, managing factor exposure or adding fixed income. Others turn to tactical decisions. But the risk equities pose to your glide path is so large that all of these avenues have shortcomings.


Potential Pros and Cons of Common Remedies, Alternatives, Pros: Lower Equity Correlation, Expanded Opportunities, Inflation Hedge, Cons: Higher Fees, Liquidity Risks, Hidden Equity Beta. Factor Exposures, Pros: Targeted Risk Exposures, Lower Correlation Between Factors, Cons: Transient Performance, Overcrowding Risks, Spurious Modeling. Fixed Income, Pros: Lower Equity Correlation, Capital Preservation, Income Generation, Cons: Interest Rate Risk, Credit Risk, Longevity Risk. Tactical Decisions, Pros: Enahcned Returns, Lower Risk, Cons: Market Timing is Difficult, Implementation Costs, Governance Costs.


Add to Your Risk Management Toolbox

But what if you could systematically increase and decrease equity beta through risk regimes, participating in the market’s upside yet mitigating downside risk? A variable beta strategy seeks to offer just that. This type of performance contour changes the value proposition of equites in any asset allocation model.

A variable beta strategy is a hybrid of active core and low volatility strategies that systematically adapts to market risk regimes. It attempts to reduce the overall risk of equity investing without sacrificing its return potential. In risk-on environments, the equity beta of a variable beta strategy may be close to 1.0, while in risk-off markets the beta adjusts downward, reducing systematic risk exposure.

Upside participation and lower downside capture helps fuel the “magic” of compounding. Consequently, variable beta strategies offer the potential to change the value proposition for equities in two ways:

  1. Reduce the total volatility of a portfolio while maintaining the same equity exposure.
  2. Maintain volatility levels while increasing equity exposure to improve return potential.

Implementing Variable Beta

We illustrate the benefits of adding a variable beta strategy to a target-date strategy in our short case study. Like all asset allocation models, constant risk premia assumptions limit target-date strategies. These assumptions may subject the portfolios to substantial drawdowns arising from the equity allocation, which reduce compounding benefits and exacerbate poor timing decisions by plan participants. Both have negative long-term return consequences on wealth accumulation.

Our case study compares three uses of variable beta:


BC: Base Case, Our Base Case reflects the Morningstar Lifetime Moderate Index with a zero allocation to variable beta. RV: Reduce Volatility, The Reduced Volatility model seeks to reduce Base Case volatility while maintaining total equity exposure. MV: Match Volatility, The Match Volatility models seeks to match Base Case volatility while increasing total equity exposure.

Comparing Three Uses of Variable Beta


The Reduced Volatility model might be a helpful scenario for asset allocators who can’t or won’t change their strategic allocations. By substituting one-third of the Base Case equity allocation with a variable beta strategy, we show that we can reduce Base Case volatility throughout the glide path and maintain the overall equity exposure.1



The Matched Volatility model might be a helpful scenario for asset allocators who have the flexibility to change their strategic allocations. By substituting one-third of the Base Case equity allocation with a variable beta strategy, we show that we can match Base Case volatility while increasing equity exposure throughout the glide path.2



What's the Impact on Wealth?

As you might imagine, the RV and MV models might have a material impact on wealth creation relative to the Base Case model. You can see those impacts by downloading the whole study in our eBook, “Are Your Asset Allocation Models Exposed Right Now?

Are Your Asset Allocation Models Exposed Right Now?  Learn about an innovative way to address equity risk in your portfolio. Download Now



1. The full asset history of asset class returns is used to calculate the covariance matrix, and the same matrix is used for each fund (or retirement year); therefore, the change in volatility comes from differences in target-date fund weights.

2. The increase in equity exposure to 100% in the early-career stage creates a bigger gap in volatility between the funds since we cannot increase the equity portion further to achieve the same volatility as the Base Case.

The information expressed herein is subject to change based on market and other conditions. The views presented are for general informational purposes only and are not intended as investment advice, as an offer or solicitation of an offer to sell or buy, or as an endorsement, recommendation, or sponsorship of any company, security, advisory service, or fund nor do they purport to address the financial objectives or specific investment needs of any individual reader, investor, or organization. This information should not be used as the sole basis for investment decisions. All content is presented by the date(s) published or indicated only, and may be superseded by subsequent market events or other reasons. Past performance is no guarantee of future results. Investing involves risk, including possible loss of principal and fluctuation of value.

Hypothetical performance results presented (including those in the case studies) are for illustrative purposes only. Hypothetical performance is not real and has many inherent limitations, some of which, but not all, are described herein. It does not reflect the results or risks associated with actual trading or the actual performance of any portfolio and has been prepared with the benefit of hindsight. Therefore, there is no guarantee that an actual portfolio would have achieved the results shown. In fact, there will be differences between hypothetical and actual results. No investor should assume that future performance will be profitable, or equal to the results shown. Hypothetical results do not reflect the deduction of advisory fees and other expenses incurred in the management of a portfolio.

MSCI makes no express or implied warranties or representations and shall have no liability whatsoever with respect to any MSCI data contained herein. The MSCI data may not be further redistributed or used as a basis for other indices or any securities or financial products. This report has not been approved, reviewed, or produced by MSCI.