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Public DB Plans: Pursuing Downside Risk Mitigation Without Compromising Return Prospects

Capital-efficient investment strategies could help public pension plans access the downside risk protection benefits of Treasury bonds without jeopardizing their ability to meet return targets.

When it comes to public pension plans, a lot has been written and said about expected return on assets (EROA) assumptions. Whether one believes those assumptions are reasonable and achievable or not, the fact remains: Investment decisions of public defined benefit (DB) plans are influenced by the return targets implied by their EROA. Inevitably, plan sponsors implement investment strategies that seek to maximize the likelihood of achieving those objectives.

In the end, the effort comes down to balancing asset allocation optimization, clever pursuit of alpha and sound risk management. As part of the latter element, many plans have sought to develop a dedicated allocation focused on mitigating downside risk that they expect will perform well when times get tougher. The goal is to create a pocket of resiliency that will enable plan sponsors to avoid potentially harmful decisions such as selling risk assets in a market downturn to meet liquidity needs (e.g., for benefit payments).

Traditional approaches are unlikely to succeed

From an implementation perspective, plan sponsors have employed a number of strategies in their pursuit of mitigating downside risk, including crisis risk offset (CRO) buckets and tail-risk-hedging programs (TRH). However, the most common expression remains a dedicated Treasury bond allocation (most often longer-duration U.S. Treasuries).

Nevertheless, dedicated long Treasury allocations can create important challenges for public DB plan sponsors. They have relatively low return expectations in the current market environment. For example, PIMCO expects long Treasury bond indices to return approximately 2.5% annually over the next five years. This can become a meaningful drag versus the roughly 7% EROA target used by many plans.

Consider a plan that would be invested one-third in fixed income and two-thirds in return-seeking assets (equities, both public and private, real estate, alternatives, etc.). If this plan expects to earn 8.5% on its return-seeking portfolio, it would need to achieve a 4% return on its fixed income assets to reach the 7% EROA target. With a sufficient amount of credit risk, active management and other return-enhancement techniques, it is not unreasonable to seek to build a high quality fixed income portfolio that aims to meet or even moderately exceed that 4% return target. But if our hypothetical plan allocates 10% of its total assets (nearly a third of the fixed income bucket in this example) to a long Treasury mandate for diversification and downside risk mitigation, it would have to accept a significantly lower expected return on its fixed income portfolio. And this could ultimately lead it to fall short of both the required 4% return on fixed income investments and the overall 7% EROA on total assets (see Figure 1).

Figure 1 has a table listing a hypothetical plan with an asset allocation of two-thirds return seeking, and one-third fixed income. Underneath the table, one box on the left shows that a 4% return on the fixed income portfolio is required to achieve a plan EROA target of 7%. A box on the right shows how the current expected return on long Treasury bonds of 2.5% is inconsistent with a DB plan’s return requirements of 4% for fixed income

Achieving downside risk mitigation without losing sight of EROA objectives

Fortunately, plan sponsors can turn to capital-efficient strategies to seek the desired diversification and lower overall portfolio risk while avoiding or mitigating the substantial return trade-off implied by a dedicated long Treasury allocation. One simple yet effective strategy consists of pairing a modestly to moderately leveraged long Treasury bond exposure with a low-volatility and low-correlation alpha engine to strive for both sufficient downside risk mitigation (from the leveraged Treasury bond exposure) and a level of return that has the potential to be more consistent with public DB plans’ EROA targets (combination of Treasury exposure beta and alpha engine).

For example, our hypothetical plan sponsor seeking to allocate 10% of its assets to a bucket focused on downside risk mitigation could invest half of the 10% (i.e., 5% of total assets) in a leveraged long Treasury bond allocation (1:1 leverage to achieve a net exposure of 10% of total assets as intended). The remaining 5% of assets could be invested alongside in a low-volatility, low-correlation alpha engine that seeks an adequate amount of excess return over Libor (for example, Libor +3%). A combined structure of this sort could provide the plan sponsor with the desired 10% long Treasury exposure while bringing the expected return potential on those assets more in line with the 4% level that our hypothetical fixed income portfolio must achieve to increase the likelihood that the plan sponsor achieves its overall EROA target. While the hypothetical example above assumes a 50/50 split between the leveraged long Treasury allocation and the alpha engine, the breakdown could be customized to reflect plan-specific return targets and/or leverage tolerance (see Figure 2).

Figure 2 is a table and graph showing four hypothetical scenarios of using various allocations between long Treasury bonds and a low-volatility alpha engine. The graph shows how a 100% allocation to Treasuries yields a hypothetical return of 2.5%. Various additions of the alpha engine increase potential returns: with an allocation of 25% to the alpha engine and 75% Treasury bonds, the return rises to 3.3%. A 50-50 split yields a 4% return, and a 75-25 split has a 4.8% return. The graph shows that estimated volatility slightly decreases as the allocation to the low-volatility alpha engine increases. More data is within the table

As Figure 2 shows, a hypothetical strategy combining a 50% allocation to modestly leveraged long Treasury bonds (1:1 leverage ratio) and a 50% allocation to a low-volatility alpha engine could significantly improve estimated return expectations relative to a 100% dedicated long Treasury allocation (+1.5% estimated return) while maintaining relatively similar diversification and downside risk-mitigation potential (-0.2 equity beta versus -0.3 for the dedicated Treasury bond allocation). Even more significant estimated return benefits were achieved with a higher allocation to the low-volatility alpha engine (See option C in Figure 2). Note, however, that a higher allocation to the alpha engine would require a more meaningful amount of leverage on the long Treasury allocation to achieve the same net exposure to Treasury bonds.  

To achieve its primary objective of mitigating overall portfolio risk, it is crucial that the alpha engine embedded in the strategy displays a relatively low correlation to risk in the equity market. In our modeling, we used a PIMCO Dynamic Bond Strategy representative account as a proxy for the alpha engine risk. Because of its limited correlation to equity markets, its use as an alpha engine should not jeopardize the goal of downside risk mitigation. This is supported by the fact that the equity beta remains similar to that of a dedicated long Treasury bond allocation, even as the weight of the exposure of the alpha engine increases. (Learn more about PIMCO Dynamic Bond Strategy.)

Conclusion

Public DB plan sponsors are rightfully seeking ways to protect against pronounced market downturns with diversifying strategies that they expect will be resilient in the face of such market environments. The current low interest rate environment suggests that traditional defensive strategies like dedicated Treasury allocations may deliver fairly low returns in the future, making implementation challenging without compromising EROA objectives. Instead, we believe plan sponsors should consider innovative strategies that combine capital-efficient long Treasury bond exposure with a low-volatility and low-correlation alpha engine. By doing so, we believe plan sponsors will be in a better position to achieve their downside risk mitigation objectives with a potentially lesser return compromise.

The Author

Rene Martel

Head of Retirement

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Disclosures

The analysis contained in this paper is based on hypothetical assumptions. HYPOTHETICAL PERFORMANCE RESULTS HAVE MANY INHERENT LIMITATIONS, SOME OF WHICH ARE DESCRIBED BELOW. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE SHOWN. IN FACT, THERE ARE FREQUENTLY SHARP DIFFERENCES BETWEEN HYPOTHETICAL PERFORMANCE RESULTS AND THE ACTUAL RESULTS SUBSEQUENTLY ACHIEVED BY ANY PARTICULAR TRADING PROGRAM.

ONE OF THE LIMITATIONS OF HYPOTHETICAL PERFORMANCE RESULTS IS THAT THEY ARE GENERALLY PREPARED WITH THE BENEFIT OF HINDSIGHT. IN ADDITION, HYPOTHETICAL TRADING DOES NOT INVOLVE FINANCIAL RISK, AND NO HYPOTHETICAL TRADING RECORD CAN COMPLETELY ACCOUNT FOR THE IMPACT OF FINANCIAL RISK IN ACTUAL TRADING. FOR EXAMPLE, THE ABILITY TO WITHSTAND LOSSES OR TO ADHERE TO A PARTICULAR TRADING PROGRAM IN SPITE OF TRADING LOSSES ARE MATERIAL POINTS WHICH CAN ALSO ADVERSELY AFFECT ACTUAL TRADING RESULTS. THERE ARE NUMEROUS OTHER FACTORS RELATED TO THE MARKETS IN GENERAL OR TO THE IMPLEMENTATION OF ANY SPECIFIC TRADING PROGRAM WHICH CANNOT BE FULLY ACCOUNTED FOR IN THE PREPARATION OF HYPOTHETICAL PERFORMANCE RESULTS AND ALL OF WHICH CAN ADVERSELY AFFECT ACTUAL TRADING RESULTS.

The return targets presented are not a prediction or a projection of return. Return volatility may be significant in shorter time periods. There can be no assurance that a portfolio will be successful in meeting its target. A target is not a guarantee and actual results may be lower.

We employed a block bootstrap methodology to calculate volatilities. We start by computing historical factor returns that underlie each asset class proxy from January 1997 through the present date. We then draw a set of 12 monthly returns within the dataset to come up with an annual return number. This process is repeated 25,000 times to have a return series with 25,000 annualized returns. The standard deviation of these annual returns is used to model the volatility for each factor. We then use the same return series for each factor to compute covariance between factors. Finally, volatility of each asset class proxy is calculated as the sum of variances and covariance of factors that underlie that particular proxy. For each asset class, index, or strategy proxy, we will look at either a point in time estimate or historical average of factor exposures in order to determine the total volatility.  Please contact your PIMCO representative for more details on how specific proxy factor exposures are estimated.

Figures are provided for illustrative purposes and are not indicative of the past or future performance of any PIMCO product.

All investments contain risk and may lose value. Investing in the bond market is subject to risks, including market, interest rate, issuer, credit, inflation risk, and liquidity risk. The value of most bonds and bond strategies are impacted by changes in interest rates. Bonds and bond strategies with longer durations tend to be more sensitive and volatile than those with shorter durations; bond prices generally fall as interest rates rise, and low interest rate environments increase this risk. Reductions in bond counterparty capacity may contribute to decreased market liquidity and increased price volatility. Bond investments may be worth more or less than the original cost when redeemed. Tail risk hedging may involve entering into financial derivatives that are expected to increase in value during the occurrence of tail events. Investing in a tail event instrument could lose all or a portion of its value even in a period of severe market stress. A tail event is unpredictable; therefore, investments in instruments tied to the occurrence of a tail event are speculative. Derivatives may involve certain costs and risks such as liquidity, interest rate, market, credit, management and the risk that a position could not be closed when most advantageous. Investing in derivatives could lose more than the amount invested.

There is no guarantee that these investment strategies will work under all market conditions or are suitable for all investors and each investor should evaluate their ability to invest long-term, especially during periods of downturn in the market. Investors should consult their investment professional prior to making an investment decision.

Return assumptions are for illustrative purposes only and are not a prediction or a projection of return. Return assumption is an estimate of what investments may earn on average over the long term. Actual returns may be higher or lower than those shown and may vary substantially over shorter time periods.

Forecasts, estimates and certain information contained herein are based upon proprietary research and should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. There is no guarantee that results will be achieved.

Alpha is a measure of performance on a risk-adjusted basis calculated by comparing the volatility (price risk) of a portfolio vs. its risk-adjusted performance to a benchmark index; the excess return relative to the benchmark is alpha. Beta is a measure of price sensitivity to market movements. Market beta is 1. LIBOR (London Interbank Offered Rate) is the rate banks charge each other for short-term Eurodollar loans.

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