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Article

Assessing Low-Volatility Assets in the LDI Framework

Wei Ge
The Journal of Investing Winter 2016, 25 (4) 109-120; DOI: https://doi.org/10.3905/joi.2016.25.4.109
Wei Ge
is a senior researcher at Parametric Portfolio Associates LLC in Minneapolis, MN.
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Abstract

In the liability-driven investing (LDI) framework, an underfunded pension plan may use the glidepath approach—dividing assets for liability-matching and asset-growth purposes. This practice introduces a dilemma, as the projected funding ratio and funding ratio volatility may not be improved at the same time. Low-volatility assets may be suited in the LDI framework to resolve this dilemma. This article examines the behavior of several low-volatility assets, including three low/minimum volatility equity indexes and two alternative low-volatility series (the HFRI Fund Weighted Composite Index and a volatility risk premium–based construct), in a generic underfunded pension plan with typical assumptions. The study concludes that low-volatility investments may be a good choice for growth assets in the LDI framework, as the low-volatility profile can help pension plans reduce projected funding ratio volatility and may improve funding ratio projections. On the other hand, the benefits of low-volatility investments in the LDI framework depend on such assets continuing their past return–risk patterns into the future, an assumption that may not materialize. Pension managers are encouraged to consider low-volatility assets as potential components for underfunded pension plans, although the decision whether to include such assets in pensions depends heavily on future return assumptions.

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The Journal of Investing: 25 (4)
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Assessing Low-Volatility Assets in the LDI Framework
Wei Ge
The Journal of Investing Nov 2016, 25 (4) 109-120; DOI: 10.3905/joi.2016.25.4.109

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Assessing Low-Volatility Assets in the LDI Framework
Wei Ge
The Journal of Investing Nov 2016, 25 (4) 109-120; DOI: 10.3905/joi.2016.25.4.109
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  • Article
    • Abstract
    • FUNDING RATIO VS. FUNDING RATIO VOLATILITY
    • MEAN–VARIANCE OPTIMIZATION VS. FIXED ALLOCATIONS
    • DATA AND METHODOLOGY
    • PERFORMANCE ANALYSIS
    • CAVEAT EMPTOR—THE IMPORTANCE OF RETURN ASSUMPTIONS
    • CONCLUSION
    • ENDNOTES
    • REFERENCES
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