Liquidity is the new volatility: why we need a better model for building portfolios
By Junying Chen, MS, a Senior Associate at PGIM’s Institutional Advisory & Solutions group and principal author of Building a Better Portfolio.
During the decade long bull market, institutional investors poured more than $4 trillion into private assets such as private equity, real estate and private credit, seeking to diversify and enticed by returns that outpaced public assets by a significant margin. Then COVID-19 hit.
With the global economy in lockdown, there is rising concern among some chief investment officers of large institutional funds with a substantial allocation to private assets that a lack of portfolio liquidity may make it difficult to unload those assets quickly to meet cash flow commitments without taking a painful haircut. Whether cash is needed to help make pension benefit payments, meet general partners’ calls for capital due to prior commitments, or pursue attractive investment opportunities during market dislocations, portfolio illiquidity may prove to be a bigger threat than market volatility.
While portfolios generally recover from volatility shocks, a liquidity shock can be a matter of survival. For many CIOs, the crisis has highlighted a key question: What’s the right amount of private assets to maximize portfolio performance without sacrificing too much liquidity?
Grappling with this question—more than a year before the current crisis—we teamed up with Singapore’s GIC, one of the world’s largest sovereign wealth funds, to create a new risk framework that formally integrates liquidity measurement and management into a multi-asset, multi-period portfolio construction process.
It was clear that a better way was needed to quantify liquidity risk and provide an alternative to traditional risk models such as mean-variance optimization or risk parity to put liquidity needs at the forefront.
Dubbed OASISTM (Optimal Asset Allocation with Illiquid Assets) the framework can help CIOs better understand a portfolio’s liquidity characteristics by analyzing how top-down asset allocation changes (e.g., the mix of public stocks and bonds and private asset holdings) interact with bottom-up private asset investment decisions (e.g., private asset pacing, or commitment, strategies) to affect their ability to respond to liquidity demands.
Our framework generates an “efficient frontier,” a chart illustrating the trade-off between the frequency and severity of liquidity events (i.e., a liquidity severity score, or liquidity risk) and expected performance for a set of optimal portfolio allocations. First, investors specify their expected cash flow commitments (e.g., benefit obligations, GP capital calls and dry powder needs) their portfolio must try to meet—and choose a target liquidity severity score that is appropriate for their organization. Next, investors enter several customizable inputs, such as their own public capital market assumptions and expectations about how the private market will perform relative to the public markets. They can also adjust based on their private limited partnership fund selection skills, an important performance driver.
Importantly, CIOs can use the framework to explore how to pace their private asset investments to manage exposure and the uncertainty in timing and magnitude of their cash flows over time. The model can accommodate various private asset commitment strategies—for example, overcommitting or cash flow matching—based on different investor objectives such as targeting a higher percentage of net asset value in the overall portfolio over time; efficiently balancing quarterly private asset capital calls and distributions to minimize disruption to the public portfolio; or achieving vintage diversification.
CIOs naturally worry about how the liquidity of their portfolios might withstand various market conditions, particularly downturns. For example, the framework can be used to study how portfolios might weather a U-shaped compared to a V-shaped recovery—both of which are especially appropriate scenarios to contemplate in today’s market environment.
While the current economic crisis has brought the need for such a portfolio construction model into stark focus, the usefulness of the model goes well beyond the challenging times of COVID-19. For a CIO with a robust allocation to private assets, weighing liquidity risk against performance is better than relying on a mean-variance or risk-parity model.
Investors now have a framework for looking at liquidity and performance. Some may find they can comfortably take more liquidity risk; others may find they are taking too much. But both groups will understand their portfolios better.
Junying Shen is a Senior Associate in the Institutional Advisory & Solutions (IAS) group, focusing on quantitative research related to traditional and alternative assets and the development of asset allocation model. Ms. Shen joined IAS in June 2017 from Market Risk Capital & Analysis team at Goldman Sachs & Co. as a senior analyst, where she analyzed market risk factors for various product types including syndicated loans, public equity, private equity, and real estate assets. Ms. Shen earned her BS degrees in Finance and Mathematics from University of Illinois at Urbana-Champaign and an MS in Mathematics in Finance from New York University.