How sustained market volatility is reshaping risk workflows

In a structurally more volatile environment, risk management is shifting from a periodic exercise to a continuous workflow, reshaping how institutional investors approach portfolio visibility, stress testing, and liquidity analysis across public and private markets.

How are workflows changing as investors navigate sustained market volatility across public and private assets?

A scenario-based approach to the evolving landscape of risk and market volatility

Volatility is no longer a single event or episode to navigate. For the CIOs and CROs that I work with at some of the world’s largest asset managers, pension funds, and leading insurance companies, it has become a permanent workflow condition to solve.

The institutions that are adapting well are not simply building bigger risk teams. They are rethinking how decisions get made, how portfolios get stress-tested, and how data and analytics sit inside that process—continuously, not periodically.

In 2026 year-to-date we are clearly in a higher-volatility regime. And there are structural reasons for that; namely, a confluence of technological disruption, including AI, layered on top of deepening geopolitical fragmentation. You don't have to resolve the debate about how different this period is from previous ones to accept that clients are preparing for it very differently. One way they're doing that is through scenarios. In more stable times, you'd really have to work hard to identify a couple of meaningful alternative outcomes. Now, clients want to know about half a dozen different outcomes and that's become the baseline expectation, not the exception.

What to consider in a volatility scenario

1. Know your exposures. Know what you own. Know what it is worth. This is where everything starts. You cannot manage what you cannot see, and in volatile markets, visibility is the first thing that slips.
2. Be aware of what can change. A colleague of mine is fond of saying: “chance favors the prepared mind.” He is right. This is not about predicting outcomes. It is about understanding which assumptions your portfolio is most sensitive to so that when conditions shift, you are already thinking clearly.
3. Stress test your models. Look at your model. Then look at it again when the environment moves. Keep asking: is there something I would change, something I am not capturing, something I am missing? In the Aladdin platform, for example, Market‑Driven Scenarios (MDS) provide a way to assess how alternative market environments translate into portfolio P&L.

The willingness to adapt your model to reflect current reality is not a sign of weakness, it is the whole point. There is no such thing as a perfect model. Perfect volatility preparedness in 2026 is a model that gets better every time the world forces you to look at it again.

How is market volatility reshaping the workflow demands of institutional investors navigating public and private assets—and where is the technology gap most acute?

Liquidity, commitment risk, and the race for real-time data

Stress testing used to be a monthly exercise. Now it's a daily practice. And that shift isn't only about what markets are doing—it's about what clients are holding. Portfolios have become structurally more complex. The types of assets and structures that institutions are investing in have gotten way more complicated, which means that during a sell-off, understanding where your exposures actually sit has become a major analytical challenge in itself.

And really the workflow has to start there: with exposure. Not the stress test—with what you own. Ideally that means having all positions modeled accurately, with real-time cash and position-level data across both public and private assets, on a single platform. This is particularly critical for complex structures such as private credit, where it’s not enough to understand aggregate exposure—you need visibility into which positions are PIKing, increasing long‑term exposure while deferring near‑term cash interest, and therefore creating hidden liquidity and concentration risk.

The second major challenge our clients face is the pricing gap. Public markets reprice in real time. Private assets don't—there's typically a quarter's lag before they are formally marked. That gap between what you're seeing in public markets and what you can actually see on the private side is precisely where risk officers are asking the hardest questions right now.

Liquidity is where it becomes structural. When markets sell off, GPs see opportunity and call capital—at the exact moment that asset owners are already absorbing losses on the public side. That dual pressure, hit on the asset side and hit on commitments simultaneously, is a liquidity burden that can be supported with a near-real-time view of cash flows, income, and commitment risk across the whole portfolio.

Once you know what you own–only then–comes forward-looking stress testing. Instead of re‑running historical scenarios, today’s stress testing should take a forward‑looking, multidisciplinary approach that connects near‑term market risk with longer‑term credit migration and structural liquidity risk.

The institutions adapting well are building these components into a single, continuous workflow rather than an ad hoc exercise.

What are some considerations when preparing for a volatility scenario?

1. Measure how long it takes to understand your exposures: end to end. Pressure‑test the full chain: market move, data ingestion, aggregation, exposure insight. Determine if it takes minutes (or days) to aggregate positions and understand underlying exposures. Volatility compresses decision windows so time to action matters more.
2. Review the organization’s ability to surface integrated credit, liquidity, and market risk insights in a timely manner.
3. Establish a timely, forward‑looking liquidity view across public and private assets, incorporating cashflows, unfunded commitments, delayed cash income, and stress‑driven cash needs so liquidity risk is understood by cash flow behavior and timing, not asset classifications.