LDI Strategies, Back in Vogue, Require Technology and Data Underpinning

Joel Kornblum, Managing Director


As liability-driven investment strategies move to the forefront, capabilities in measuring performance and risk can make or break these initiatives

For much of 2015, The CBOE Volatility Index (VIX Index) has resided near historic lows, reflecting a level of complacency among market watchers who had grown accustomed to an accommodating Fed policy and more than five years of nearly uninterrupted growth in U.S. equities. At the end of August, however, investors snapped to attention as volatility ratcheted up significantly and the VIX, also known as the “fear index,” logged its largest-ever weekly increase. For plan sponsors, particularly those managing defined benefit plans or insurance assets, the re-introduction of volatility offered a timely reminder to the value of liability-driven investment (LDI) strategies.

LDI strategies, also referred to as asset/liability management or ALM, first came into vogue during the aftermath of the 1997-2000 internet bubble. LDI’s roots, however, extend back to the “cash matching” dedication strategies that were originally conceived in the late 1970s to deal with high interest rates, before giving way to immunization models, which use bond-heavy portfolios to hedge future pension payouts. Global adoption of LDI strategies—featuring liability-oriented benchmarks and incorporating the use of long-duration bonds, short-duration cash management, and derivative overlays to hedge against interest rates and inflation—has since moved in fits and starts.

It’s often been the case that interest in LDI syncs with corresponding bouts of market unrest, and as the market appears set to enter a rising-rate environment, many plan sponsors are again revisiting the model. The fact that funded ratios have improved significantly over the past few years—from the low 70% range in 2012 to over 90% as of June of this year—only adds to the broader interest in LDI. Before any organization can move ahead with liability-driven strategies, however, they first have to understand their capabilities as they relate to tracking performance and risk, as shortcomings in these areas can create significant barriers to entry.

Those who have explored liability-driven models in the past will likely find that LDI strategies have evolved considerably over the past 15 years. They’ll also discover that more is required to both track risk and performance, as well as monitor, in real time, the liabilities and funded status of their organization. These measures are a prerequisite today to facilitate the more dynamic and bespoke LDI strategies. However, just as new advances in software served as the underpinning to the “cash matching” strategies of the seventies and early eighties, technology again plays a foundational role as institutional investors seek to map out, implement and then monitor their LDI efforts.

Consider everything that goes into devising a liability-driven strategy. At the very outset, customized benchmarks need to be established at both the plan level as well as at the individual manager level, with additional benchmarks created to distinguish between the liability-hedging allocations (which should exceed future liabilities) and the alpha-generating mix (with the goal of further closing the funding gap). These benchmarks should account for exposures such as plan duration, credit spreads and yield curves. And if “de-risking” is a priority, the LDI strategy will likely follow a glidepath model in which asset allocations are determined and reset through pre-established triggers, be it strategic hedge levels, funded status, interest rates or even time-based milestones. Indeed, LDI models may be among the most intensive investment strategies when it comes to the need for robust and timely data.

All of this, for obvious reasons, requires a sufficient depth of information and analysis that goes beyond what’s required in more traditional asset management. Without a granular view of performance and attribution, for instance, just establishing the appropriate benchmarks can be impossible. Further, for the more dynamic and customized LDI models, plan sponsors need to access and warehouse security master data, yield curves, third-party benchmarks, liability cash flows, and asset performance, in addition to other information. This data then feeds into analytics around funded status and sensitivities, liability performance, hedge ratios, key rate durations, and discounted and present-value cash flows.

Moreover, as LDI models are set against actuarial benchmarks, relative performance is moot. This makes it all the more critical that plan sponsors, or even asset managers serving pensions, have a grasp around core performance and advanced risk metrics – calculations that traditionally plan sponsors have cobbled together through Excel or even manually. As such, LDI, from the perspective of a technology provider, represents an area that would benefit greatly from any efficiencies introduced into the model, a development that would surely drive further adoption among those that may not have the bandwidth for such a strategy today.

Whether it’s due to global market unrest, geopolitical uncertainty or the prospect of rising interest rates, most market watchers are bracing themselves for more volatility and less certainty in the years ahead. It’s in these times that liability-driven investment strategies typically pay off.

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