Beyond traditional portfolio construction: incorporating uncertainty

Incorporating uncertainty into the asset allocation process is a complicated but essential ingredient of building portfolio resilience, something investors are valuing more than ever in an environment where inflation, geopolitical and climate risks dominate. GIC and BlackRock have both developed asset allocation frameworks that incorporate investors’ aversion for uncertainty.

A health pandemic, war, inflation, fiscal and monetary policy divergence and climate change impacts have all contributed to an ongoing uncertain economic environment for investors in the 2020s. But for traditional asset allocation models like mean variance optimisation to work they rely on inputs including economic certainty to produce the one optimal portfolio. Perhaps this is no longer best practice.

Many institutional investors are recognising there is not one optimal portfolio and are focusing instead on portfolio resilience. However, asset allocation modelling has been slow to keep up with the need to think differently about modelling the future. Until now.

Singapore’s sovereign wealth fund, GIC, and BlackRock have both come up with asset allocation models that factor in uncertainty and have produced a joint paper to showcase the models and how they could potentially be used together.

GIC uses an explicit scenario-based approach while BlackRock’s approach is predominantly simulation-based, while also allowing for scenario analysis.  Both, however, allow for uncertainty and recognise there can be no perfect portfolio.

Grace Qiu, senior vice president, total portfolio strategy at GIC explains to Top1000funds.com that the fund is using a scenario-based approach to minimise opportunity cost, or ‘regret risk’, of any difference from the baseline scenario.

“We construct different scenarios and apply probabilities, and then design the portfolio such that it stays resilient under different macro regimes.” she says.

The BlackRock methodology, meanwhile, uses simulations and return forecast uncertainty incorporated into its capital market assumptions, and then simulates multiple pathways of portfolio return and portfolio resilience.

“They are different in design and good at handling different types of uncertainties. Both share the same core philosophy that helps produce more robust and resilient outcomes,” Qiu says.

Ding Li, Qiu’s colleague who is also senior vice president of total portfolio strategy, says the objective of incorporating uncertainty is not to produce one best outcome in the future.

“The key principle for this technique in asset allocation is to prepare the plausible scenario and possible outcomes in different scenarios so we can find the more conservative but more resilient solution to handle the outcome,” he says. “In reality we never know the scenario that will happen. The best outcome and predictor most likely is not the objective. It is to account for uncertainty and estimation and risk dispersion in portfolio construction. It’s a key difference from traditional mean variance which relies on point estimation.”

Li says GIC’s research shows there are multiple methodologies to incorporate uncertainty into account and the paper compares the pros and cons.

“You don’t have to rely on a single methodology. You can combine the two together to improve the efficiency of the robust portfolio construction.”

Senior portfolio strategist and  UK Chief Investment Strategist at BlackRock Investment Institute, Vivek Paul, says the approach allows investors to have consistency.

At BlackRock there are many investors across the entirety of the firm who may all have different qualitative overlays on top of the quantitative models, he says. To begin with, incorporating uncertainty into the models can improve the consistency of the firm-wide approach.

“When thinking about mean variance or traditional modelling, the trouble is that investors have known for a long time the limitations of those approaches and have biased a problem in a certain way to get a believable outcome. So they’ve been putting so many constraints around the process that in the end,the actual approach isn’t telling you much,” Paul says. “If you have 100 investors and they are all putting on overlays or constraints then you might not get consistency. By explicitly allowing uncertainty at a systemic level, the starting point is a more believable and consistent approach – albeit one that can still subsequently be enhanced by investor judgment.”

Uncertainty in practice

BlackRock’s Paul says the unique environment of the past couple of years is evidence of uncertainty becoming more of an issue now.

“No one was forecasting what would happen in 2020 with the pandemic and no one thought war would be on the doorstep of Europe in 2022. These are examples of why factoring in uncertainty is so crucial,” he says. “We should not pretend we know with certainty what will happen.”

Incorporating climate outcomes into the model is one example of uncertainty in practice, says Paul.

“Having uncertainty in the framework that we have is exactly why we can do what we’ve done when it comes to climate,” he says. “Leading scientists have different predictions for the outcome and how it will pan out. All of our capital market assumptions take into account our best prediction of the climate transition but we are allowing for uncertainty because our best guess might be wrong. Uncertainty is a direct part of the process.”

GIC’s policy portfolio has a 20-year time horizon so by nature asset allocation over the long term does not change materially.

Qiu says the process that models different macro economic scenarios, however, and assigns probabilities to them based on the likely path, has brought inflation into the fore in the portfolio construction process and highlights more than ever the need for diversification.

“For a naïve mean variance portfolio you go for  higher return, higher risk assets like equities, and rely on bonds to diversify. But both of those are financial assets and under inflation could get impacted. This process definitely brings out the importance of diversification and the need for more inflation resilient real assets in the portfolio,” she says.

“By explicitly modelling the need for diversification under an inflationary environment you can understand the need for real assets which may not have the same return as equities or private equity. This need to have more inflation resilient diversifying assets in the portfolio is one concrete practical example of the framework in practice.”

Paul also says the biggest risk to investors is inflation and Blackrock has already incorporated it as part of its core macro narrative.

“We believe inflation is under appreciated in market dynamics in the medium to long term, so our approach is already heavily tilted to inflation-sensitive assets, this means the inflationary impact of the war in Ukraine on the global economy has already been taken into account.”

GIC’s Li also emphasises the usefulness of the uncertainty framework across different time horizons, with GIC using long-term modelling as well in the short term across its dynamic and tactical asset allocation.

“For GIC and our peer group this framework is very flexible in terms of the different level of applications, it can be at the top down strategic level for a very long horizon and also applied to near-term uncertainty.”