Glide path ALM: A dynamic allocation approach to derisking

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1 Glide path ALM: A dynamic allocation approach to derisking Authors: Kimberly A. Stockton and Anatoly Shtekhman, CFA Removing pension plan risk through derisking investment strategies has become a major focus for pension plan sponsors. Vanguard researchers propose that sponsors can benefit by incorporating derisking logic directly into their asset-liability models, thus providing more practical, actionable results. Changes in pension law and other factors have left plan sponsors more exposed to pension plan risk. As a result, many have made derisking the foundation of their investment strategies. This approach involves reducing risk through asset allocation changes as the funding ratio improves, with the goal of stabilizing the portfolio once full funding is achieved, at which point higher investment returns offer little benefit to the sponsor. (Overfunding of the plan is not rewarded, because, except in limited situations, assets can be used only to fund plan liabilities.) The issue: Plan sponsors are seeking to reduce exposure to pension plan risk by incorporating derisking investment strategies. The challenge: Asset-liability models, widely used by plan sponsors, are often based on several static asset allocations. Yet, derisking approaches suggest that portfolio allocation should change if the plan s funding ratio rises. Vanguard conclusion: Our glide path ALM approach incorporates derisking logic directly into the assetliability model. This provides a number of benefits to plan sponsors. Asset-liability modeling (ALM) has long been the preferred tool used by sponsors to assess a plan s strategic asset allocation relative to its liability. ALM studies typically look at a number of static allocations to forecast the potential risks, returns, and costs to the plan. Yet a derisking strategy entails making substantial changes in the portfolio as the plan moves toward full funding. Our glide path ALM approach combines traditional ALM with derisking by incorporating a dynamic asset allocation. Specifically, we forecast market and pension metrics over the investment strategy planning period, with asset allocations changing as funding level thresholds, or triggers, are reached. This approach creates a more applicable and actionable result for plan sponsors. This article uses a case-study approach to explain our methodology and to illustrate how a typical pension plan would employ glide path ALM to derisk. We present our quantitative and qualitative tools including a risk-adjusted G-ALM score we created for evaluating progress and the likelihood of success. Note: This article is adapted from a 2012 Vanguard research paper by the same authors and title; available at Vanguard Investment Perspectives > 1

2 Case-study plan characteristics Initial funding ratio: 84%. Initial assets : $4,169.4 million. Initial liability: $4,938.1 million. Plan status: Closed. Liability discount rate: 5.4%. Normal cost: $75 million. Liability duration: 12.6 years. The case study As with any ALM process, the glide path ALM begins with a forecast of plan assets, liabilities, and other metrics. Our asset simulation model, the Vanguard Capital Markets Model (VCMM), employs a regression-based Monte Carlo simulation approach to generate forward-looking distributions of expected long-term returns for global equities, fixed income, and other asset classes. For this study, we analyzed a closed plan with an 80% equity/ 20% fixed income starting allocation 1 (see details in the accompanying box). Using the asset forecast and plan metrics, we created a liability-driven investing (LDI) efficient frontier for our plan. The frontier is based on the highest expected return for a given level of liability tracking error over five years. 2 Next, to facilitate the derisking process, we decided on three phase portfolios from the efficient frontier. As shown in Figure 1, we chose portfolios from across the frontier. Moving from right to left, the portfolios reduce risk in steps, gradually decreasing the equity exposure to about one-third of the portfolio while increasing the bond allocation and lengthening its duration to achieve greater hedging. For example, the Phase 1 portfolio here is 45% U.S. equity, 20% international equity, 20% extendedduration U.S. Treasury bonds, and 15% U.S. longterm bonds. The Phase 3 allocation is 30% U.S. equity, 5% international equity, 15% extendedduration Treasury bonds, and 50% U.S. long-term bonds. In practice, specific portfolios should be chosen on the basis of desired duration hedge ratios and the plan sponsor s willingness and ability to take risk. Important: The projections or other information generated by the Vanguard Capital Markets Model regarding the likelihood of various investment outcomes are hypothetical in nature, do not reflect actual investment results, and are not guarantees of future results. VCMM results will vary with each use and over time. The VCMM projections are based on a statistical analysis of historical data. Future returns may behave differently from the historical patterns captured in the VCMM. More important, the VCMM may be underestimating extreme negative scenarios unobserved in the historical period on which the model estimation is based. 1 Although the typical pension plan asset allocation is closer to 55% equity/45% bonds, we chose this more aggressive starting allocation for the study because it better illustrates the derisking process. 2 Liability tracking error is measured here as the return volatility relative to liability volatility. 2 > Volume 12

3 Figure 1. Case study: Efficient frontier and phase portfolios a. Efficient frontier for expected return and tracking error based on VCMM simulations b. Allocations: Current portfolio and phase portfolios selected from efficient frontier 10% 9% Current portfolio Phase 1 Phase 2 Phase 3 U.S. equity 48% 45% 30% 30% Return 8% 7% 6% 5% 4% Phase 1 Phase 2 Phase 3 Current portfolio International equity Aggregate U.S. bond market Extended-duration Treasury bonds Long-term U.S. bonds Current duration hedge ratio 6% 44% 56% 64% 3% % Liability tracking error Note: Duration calculation is point-in-time and assumes that the equity duration is 0 year. Source: Vanguard, from VCMM simulations. As funding-level thresholds are reached, plans make the transition to the next phase portfolio, which will have lower tracking error and a higher liability hedge ratio. The transitions are driven purely by funding level, and not by time. Therefore, the year in which a plan moves to a new phase varies. Starting-phase portfolios also can vary, depending on the initial funding status. Sponsors of underfunded plans could choose to make a large contribution to improve their status and begin in Phase 2 or even Phase 3. However, the need for the glide path arises from the practical reality that large one-time contributions are not often a viable option for plan sponsors. Setting the triggers and finding the glide path The next step in the glide path ALM process is determining at what funding levels the plan will move to a lower-risk allocation. Funding-level triggers are plan-specific and should be based on a number of factors, such as plan status, sponsorcompany constraints, and risk aversion (for a detailed discussion, see Inglis and Sparling, 2012). In practice, determining the appropriate trigger can be an iterative process based on close dialogue between the plan sponsor and the advisor. Vanguard Investment Perspectives > 3

4 Probability Figure 2. Figure Phase 1 Phase 2 Phase 3 Source: Vanguard. 100% 0 Funding-level thresholds for derisking 100% 110% Glide path ALM: Annual probability of holding each phase portfolio Phase 1 Phase 2 Phase 3 Notes: Based on VCMM five-year simulations. Calculations assume that portfolio holds the Phase 1 allocation at the start of year 1. Source: Vanguard, from VCMM simulations. For our case-study plan, we chose the fundinglevel thresholds shown in Figure 2. Based on these thresholds, we used VCMM simulations to determine the probability that this plan would be in each phase (portfolio) over the five-year study horizon in other words, the likelihood of reaching a funding-level trigger each year. The results are shown in Figure 3, where the height of each color reflects the probability of the plan s being in that phase during a given year. Because our case-study plan starts out very underfunded, it is most likely to maintain a Phase 1 portfolio in years 1 3. Not until year 4 is the plan expected to be sufficiently well-funded to trigger a transition to the lower-risk Phase 2 portfolio. A move to Phase 3 appears most probable in year 5, when the plan is expected to be much better funded. Note, however, that the portfolio does have a measurable chance of reaching each phase trigger earlier. The full spectrum of these portfolio probabilities will be used to drive the funding metric forecasts. Estimating metrics based on probabilities Next, we evaluated the pension metrics for the current portfolio and the glide path portfolio. Note that the pension metrics for the glide path are estimated based on the portfolio probabilities as they change over time. This is a critical component of our approach, because we are attempting to estimate what the plan will experience over the next five years. Given the uncertainty in future asset and liability returns, we cannot be sure when the plan will move between phase portfolios. As a result, we base the metrics forecast on the probability of its being in each phase portfolio each year. For example, in year 2, the probability of being in Phase 1 is 80%, so the VCMM uses the Phase 1 allocation for 80% of the portfolio assets. The Phase 2 probability is 15% in year 2, so the VCMM applies that allocation to 15% of the assets. And the final 5% of assets are assigned to the Phase 3 allocation. In this way, plan sponsors receive a forecast that emphasizes the highest-probability metrics but also reflects other potential outcomes. 4 > Volume 12

5 Figure 4. 20% Liability tracking error: Glide path portfolio versus current portfolio To further illustrate the decline in risk as a plan moves along the glide path, we estimated fundingratio distributions each year for both the glide path portfolio and the current portfolio. Figure 5, on page 6, shows the range between the left and right tails (the 5th and 95th percentiles) of distributions for each portfolio. The lower, flatter track of the glide path portfolio relative to that of the current portfolio indicates that it is reducing the distribution of outcomes. The range shrinks because both risk and expected return are curtailed Current Glide path Note: Liability tracking error here is calculated as the standard deviation across all VCMM asset-liability simulations each year. Source: Vanguard, based on VCMM simulations. Because the glide path ALM is primarily a tool to help sponsors assess the derisking process, expected liability tracking error is a key metric to evaluate. In Figure 4, we compare expected liability tracking error for the glide path portfolio to that of the plan s current portfolio (80% equity/20% fixed income). Two points are clear from Figure 4. First, the tracking error with the glide path portfolio, even in the first year, is expected to be dramatically lower than for the current portfolio. This translates to lower funding-ratio and balance-sheet volatility. Second, tracking error declines over time with the glide path portfolio. This is not surprising, given that the asset allocation is changing from high to low risk as it moves into the later phases. Recall that in year 5 the portfolio consists primarily of the Phase 3 allocation, which has the highest duration hedge ratio. Figure 5 shows the expected (50th-percentile) results for funding ratios and tracking error. The glide path portfolio has lower expected funding ratios because of the risk return trade-off; yet, even with its considerably lower risk, this portfolio is still expected to end in full funding. Navigating the trade-offs Figure 5 illustrates some of the trade-offs that plan sponsors must face in managing plan assets. When addressing deficits, sponsors basically have two ways to improve the funding level: They can contribute more to the plan, or they can own a higher-risk asset portfolio in hopes of earning more return. In the end, sponsors must consider the same trade-off all investors do: Increasing potential return usually means taking on more risk. This decision is not black-and-white, and there are ways to customize the fit. Glide path ALM planning gives plan sponsors additional dials to use in assessing the risk return trade-off. For example, the glide path ALM process can help answer questions such as: How large should the initial contribution 4 be, and will that affect the beginning portfolio? What will subsequent derisking portfolios look like, and should the derisking process end with immunization (i.e., matching the plan s cash flow, duration, or other metrics with those of the liability)? 3 As noted earlier, this lower upside potential should not be problematic for pension funds once full funding is in view, because too high a surplus then represents uncompensated risk. Again, this illustrates the primary benefit of derisking: managing risk in pension funding ratios and limiting large contribution surprises. 4 The initial contribution is a cash lump sum that is added to the assets backing liabilities at the beginning of the planning period. Vanguard Investment Perspectives > 5

6 Figure 5. Funding-ratio ranges and 50th-percentile metrics for glide path portfolio and current portfolio At what funding levels (triggers) should the derisking portfolios begin, and at what frequency should they change? a. The difference in tails : Funding-ratio ranges, 95th 5th percentile Funding-ratio range 120% Current Glide path b. Expected funding ratios and liability tracking error Expected funding ratio (50th percentile) Current 82% 86% 93% 100% 106% Glide path Expected tracking error Current 15.4% 16.7% 16.7% 16.9% 16.6% Glide path Notes: The funding-ratio range in the chart represents the difference between the 95th- and 5th-percentile results each year. Liability tracking error here is calculated as the standard deviation across all VCMM asset-liability simulations each year. Source: Vanguard, based on VCMM simulations. No hard rules govern the decision process. Rather, it should be based on a steady communication flow between the plan sponsor and the advisor. To illustrate the trade-off dynamics of a glide path approach, we estimated annual tracking error, contributions, and return for our case-study plan using the glide path portfolio over the five-year time horizon. As shown in Figure 6, the glide path risk reduction is not free, although the trade-offs are not necessarily proportional. Both risk and return will decline over time as the portfolio becomes more highly correlated with liabilities. But here risk declines more than return. Contributions can initially be higher for the glide path than for riskier portfolios. But contributions are expected to decline over time as full funding is reached. At that point, the plan maintains a more conservative asset allocation to substantially lower its chance of becoming underfunded again. Tracking error in year 5 is expected to be about 25% lower than in year 1, in order to reduce risk but still maintain full funding for this plan. The G-ALM score: A risk-adjusted measure For pension plans that implement a glide path derisking approach, forward-looking estimates are important for managing the trade-offs just discussed, and specifically for anticipating the timing of next steps. Forecasts of pension metrics are one component of this, but we think it is also helpful to evaluate the chances of success on a risk-adjusted basis. 6 > Volume 12

7 Portfolio return and tracking error Figure 6. 14% Notes: Contributions and return are 50th-percentile annual results. This analysis uses a proxy for minimum contribution requirements based on the seven-year amortization of funding shortfalls defined by the Pension Protection Act. Liability tracking error here is calculated as the standard deviation across all VCMM asset-liability simulations each year. Source: Vanguard, based on VCMM simulations. To that end, we created a G-ALM score, a riskadjusted measure of success for pension plans derisking in phases based on funding-ratio thresholds. Specifically, we forecast the probability (Pb) of meeting a funding target objective (FT ) and the liability tracking error (LT ) for sponsors with planning horizon (t). The risk score is: G-ALM(t) = PbFT(t) LT Glide path trade-offs: Contributions, tracking error, and return Liability tracking error Return Contributions $ The G-ALM score can be interpreted much like other risk-adjusted return measures. The best way to evaluate the score is relatively: over time, or based on alternate asset allocations, funding level objectives, or other variables. For example, it might be useful to compare the score with a shorter/smaller bond allocation to the score with a longer/larger bond allocation in the glide path portfolios. We intend the score to be another tool for plan sponsors to use when evaluating the trade-offs we have described Contributions ($ millions) Figure 7, on page 8, shows the G-ALM scores for our plan assuming that contributions are made, asset allocations are dynamic, and the funding-ratio objective is 110%. We also include the probability of meeting the funding-ratio objective and trackingerror results on which the score is based. We compare scores for the glide path allocation to scores with the current allocation at four different initial funding ratios. In general, the score improves as the initial funding ratio rises, because this increases the probability of meeting the funding objective in the numerator. However, initial funding ratios affect which portfolio, glide path or current, results in a higher G-ALM score. For this plan, scores are higher for the glide path portfolio than for the current allocation when initial funding ratios are 100% or greater. With these beginning ratios and a 110% objective, the glide path portfolio is more efficient in terms of the relevant success/risk measure. Given a 100% or 107% starting ratio, the return benefit offered by the current portfolio is not sufficient to overcome the benefit of lower tracking error by the glide path portfolio, resulting in a higher G-ALM score for the glide path. To illustrate the trade-off here: With a 100% initial funding ratio, the probability of being 110% funded at the end of the period is 34% for the glide path portfolio and 53.7% for the current portfolio. At the same time, the expected tracking error is 9.5% for the glide path portfolio and 16.6% for the current one. This means that for the glide path portfolio, expected tracking error is about 43% lower, while the probability of reaching the funding-ratio objective is only about 37% lower than for the current portfolio. In other words, the return loss is less than the risk reduction, so the glide path score is higher. Vanguard Investment Perspectives > 7

8 Figure 7. Scores represent probability of reaching 110% funding divided by expected liability tracking error over five years. Initial funding status 107% G-ALM scores at different funding levels Current Glide path G-ALM scores Probability of reaching 110% funding in year Initial funding status 84% 90% 100% 107% Current 44.9% 47.8% 53.7% 57.9% Glide path Liability tracking error Initial funding status 84% 90% 100% 107% Current 17.2% 16.9% 16.6% 16.5% Glide path Notes: This analysis uses a proxy for minimum contribution requirements based on the seven-year amortization of funding shortfalls defined by the Pension Protection Act. Liability tracking error here is calculated as the change in assets relative to the change in liabilities. The calculation includes estimates for normal cost (the portion of the cost of projected benefits that is allocated to a specific plan year). Source: Vanguard, based on VCMM simulations. However, the G-ALM score does not always suggest immediate derisking. If a plan like this wants to obtain a funding surplus without making a large initial contribution, the initial funding ratio will be lower, and in that case the sponsor may value higher expected returns more and risk reduction less. In that situation, the benefit of higher expected returns a greater probability of meeting the funding objective outweighs the detriment of higher tracking error. 5 Conclusion With changes in the pension governing laws, a new pension management strategy derisking based on funded status has been widely embraced by consultants and plan sponsors. Derisking lays out a glide path for reducing portfolio risk as the funding ratio rises, reflecting the view that as the advantage of higher expected returns diminishes, portfolio risk should diminish as well. We suggest combining derisking with ALM studies, the process most plan sponsors use to forecast and evaluate plan risk, return, and costs in order to make investment decisions. Although derisking approaches suggest that portfolio allocation should change dramatically if the funding ratio rises, few of today s ALMs account for this. As our case study illustrates, incorporating the derisking logic directly into the ALM provides a number of benefits to plan sponsors. Glide path ALM assists with strategic decisions and provides more practical, actionable output. Forecasts of funding-ratio volatility, downside risk, and costs made with dynamic asset allocation provide a better understanding of the potential financial impact, and ultimately, produce results more closely aligned to the concerns and questions of senior management. 5 We also calculated G-ALM scores for this plan using the same assumptions but with a 100% funding objective, rather than 110%. With that funding-level objective, the return hurdle is not as high, and the risk reduction trumps the return reduction for the glide path portfolio. Using the 100% funding objective, the G-ALM score is higher for the glide path portfolio at each of the four initial funding ratios. 8 > Volume 12

9 References Bosse, Paul M., Pensions Dynamic IPS: A Plan for Action. Valley Forge, Pa.: The Vanguard Group. Bosse, Paul M., and Francis Vincent, Pension Risk Control: Is There a Better Way? Valley Forge, Pa.: The Vanguard Group. Bosse, Paul M., Nathan Zahm, and Evan Inglis, Derisking and Pension Expense: Not All Bad News. Valley Forge, Pa.: The Vanguard Group. Inglis, R. Evan, and Jeffrey Sparling, Pension Derisking: Start With the End in Mind. Valley Forge, Pa.: The Vanguard Group. Stockton, Kimberly A., How a Pension Plan s Funding Level Should Influence Its Investment Strategy. Valley Forge, Pa.: The Vanguard Group. Stockton, Kimberly A., and Anatoly Shtekhman, Glide Path ALM: A Dynamic Allocation Approach to Derisking. Valley Forge, Pa.: The Vanguard Group. Stockton, Kimberly A., Scott J. Donaldson, and Anatoly Shtekhman, Liability-Driven Investing: A Tool for Managing Pension Plan Funding Volatility. Valley Forge, Pa.: The Vanguard Group. Wallick, Daniel W., Roger Aliaga-Díaz, and Joseph Davis, Vanguard Capital Markets Model. Valley Forge, Pa.: The Vanguard Group. Vanguard Investment Perspectives > 9

10 About the Vanguard Capital Markets Model The Vanguard Capital Markets Model (VCMM) is a proprietary financial simulation tool developed and maintained by Vanguard s Investment Counseling & Research and Investment Strategy Groups. The VCMM forecasts distributions of future returns for a wide array of broad asset classes. These include U.S. and international equity markets, several maturities of the U.S. Treasury and corporate fixed income markets, international fixed income markets, U.S. money markets, commodities markets, and certain alternative investment strategies. The asset-return distributions shown in this article are drawn from 10,000 VCMM simulations based on market data and other information available as of December 31, The VCMM is grounded in the empirical view that the returns of various asset classes reflect the compensation investors receive for bearing different types of systematic risk (or beta). Using a long span of historical monthly data, the VCMM estimates a dynamic statistical relationship among global risk factors and asset returns. Based on these calculations, the model uses regression-based Monte Carlo simulation methods to project relationships in the future. By explicitly accounting for important initial market conditions when generating its return distributions, the VCMM framework departs fundamentally from more basic Monte Carlo simulation techniques found in certain financial software. The reader is directed to the research paper Vanguard Capital Markets Model (Wallick, Aliaga-Díaz, and Davis, 2009) for further details. The primary value of the VCMM is in its application to analyzing potential client portfolios. VCMM assetclass forecasts comprising distributions of expected returns, volatilities, and correlations are key to the evaluation of potential downside risks, various risk and return trade-offs, and diversification benefits of various asset classes. Although central tendencies are generated in any return distribution, Vanguard stresses that focusing on the full range of potential outcomes for the assets considered, such as the data presented in this article, is the most effective way to use VCMM output. 10 > Volume 12

11 P.O. Box 2900 Valley Forge, PA Connect with Vanguard > vanguard.com Vanguard research > Vanguard Center for Retirement Research Vanguard Investment Counseling & Research Vanguard Investment Strategy Group > For more information about Vanguard funds, visit institutional.vanguard.com, or call , to obtain a prospectus. Investment objectives, risks, charges, expenses, and other important information about a fund are contained in the prospectus; read and consider it carefully before investing. Notes about risk and performance data: All investments are subject to risk, including the possible loss of the money you invest. CFA is a trademark owned by CFA Institute The Vanguard Group, Inc. All rights reserved. Vanguard Marketing Corporation, Distributor.

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