Asset Allocation Analytics for End-State Corporate Pension Plans

Michelle (Yu) Teng, PhD, CFA, Vice President, with contribution from Junying Shen, Senior Associate, PGIM

Michelle (Yu) Teng, PhD, CFA, Vice President, with contribution from Junying Shen, Senior Associate, PGIM

In recent years, following the trend of transitioning from Defined Benefit (DB) to Defined Contribution (DC) plans, many US corporate pension plans have closed to new entrants. Even though remaining benefit payments may stretch for decades, these plans are on a path to irrelevance and have entered their “End-State.”End-state portfolios typically have a heavy and increasing percentage of retirees and the liabilities are (or, soon will be) in “run-off” mode. As end-state corporate pension plans have become more prevalent, their special portfolio management challenges have gained attention and call for new asset allocation analytics.

"CIOs need to know the tradeoff between portfolio performance and constraints to make more informed asset allocation decisions"

CIOs with well-funded end-state plans (e.g., 85% and above) are less concerned about their ability to make benefit payments in the next 5-10 years than keeping the plan’s funded status stable. Also, many CIOs have enjoyed good performance from illiquid private assets (private equity, credit and real estate) and may wish to keep them. CIOs face a choice: Should they immunize with fixed income assets? or, should they hold an allocation to return-seeking assets to protect against longevity and other risks? If the latter, what is the optimal mix of public and private assets?

To answer this last question, we argue for new portfolio allocation analytics that explicitly incorporate the unique characteristics of private assets such as their illiquidity and uncertainty of capital calls. These analytics establish a linkage between private and public markets using estimated PMEs which measure the performance of a private asset relative to public markets. The analytics also give CIOs the opportunity to express their own views on expected private asset performance relative to public markets and their fund-selection skill, which is an important driver of private asset performance. The goal is to help CIOs identify the optimal allocation to private assets, as well as the mix within both the public and private portfolios, to achieve a desired level of funded status stability.

An optimal asset allocation solution will maximize the end-state portfolio’s expected horizon value while meeting future cash obligations at a very high confidence level (i.e., 99% and above) and keeping funded status sufficiently stable over the investment horizon. Funded status stability may be represented by a maximum funding ratio variability threshold, which is the average year-over-year (absolute value)percentage change in a plan’s funding ratio over the investment horizon. Intuitively, a lower variability threshold represents a stricter CIO constraint.

These portfolio analytics help CIOs measure the trade-off between the funding ratio variability threshold and expected portfolio performance. The figure below illustrates how the optimal asset allocation and the corresponding portfolio horizon value change with the threshold. Starting from a relatively high threshold (e.g.,6%), as the threshold decreases (i.e., constraint becomes tighter), the total allocation to private assets decreases and the allocation to the hedging(or, “immunizing”) asset increases. For example, moving from a 6% to a 2% funding ratio variability threshold, the allocation to private assets decreases from 31% to 8%, while the allocation to the hedging asset increases from 59% to 89%. This shift to the hedging asset decreases the expected portfolio horizon value by 27%, from $8,932m to $6,479m. This decline in horizon value as the funded status variability threshold tightens captures the “cost of constraints” which a CIO needs to know to make the best decision for their plan.

Asset allocation analytics should let CIOs conduct “What-if” analyses to address special concerns. For example, a CIO considering a Pension Risk Transfer (PRT) transaction may impose a constraint on the maximum allocation to private assets. A 20% cap produces a large reduction in initial private asset allocation (e.g., from 31% to 11%, with a 6% variability threshold) to ensure that even after significant growth private assets will not exceed 20%. As another “What-if” example, a CIO may wish to express views on future private asset performance relative to public markets or their fund-selection skill. Optimal asset allocation will be sensitive to these views.

Tradeoff between Funding Ratio Variability Threshold and Portfolio Performance

(Hypothetical End-State Corporate DB Plan)

Note: We assume the plan has an initial AUM of $10,000m and the present value of future benefit payments is $11,772m (based on a flat 3.9% discount rate) which gives an initial 85% funding ratio. There are five assets in the investment opportunity set: two public assets (a “low-risk” asset and a “high-risk” equity asset (i.e., S&P 500)) and three private assets (LP buyout private equity, mezzanine debt and real estate funds). The public low-risk asset is a fixed-income “hedging asset” meant to proxy a plan’s hedging portfolio constructed to track the growth of the plan’s present value of liabilities, with full flexibility to dynamically select and adjust individual underlying securities.

Portfolio analytics incorporating the key characteristics of public and private assets can help solve for the optimal public vs. private asset allocation – as well as allocations within the public and private portfolios. These analytics capture the concern faced by end-state investors – “Maximize expected portfolio horizon value while keeping the plan’s funded status sufficiently stable over the investment horizon.”CIOs need to know the cost of imposing constraints to make more informed asset allocation decisions.