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The Stagflation Challenge: How to Future-Proof Liquidity Risk

When financial markets are in turmoil, liquidity is the most critical indicator of a firm’s financial health. What steps can financial institutions take to identify the optimal size and composition of liquidity buffers, particularly under unprecedented market conditions fueled by inflation and stagnant growth?

Friday, June 24, 2022

By Alla Gil

Cash is king. This may be a cliché, but it also happens to be true. If your balance sheet is bulging with greenbacks, it’s almost impossible to default.

By contrast, you can have capital buffers in the billions, but if they’re not liquid enough, even Croesus-like sums won’t be able to save you from default. Just ask Lehman Brothers.

Which brings us to liquidity risk. Essentially, there are two types: funding liquidity risk (an inability to roll-over maturing debt obligations at an acceptable cost); and asset-side liquidity risk (an inability to sell assets at expected prices). Though they’re not the same, in times of crisis, they can be highly correlated. As a result, we need to analyze them together.

Stagflation Concerns

The current economic environment carries a high risk of stagflation – a combination of inflation and stagnant growth. It hasn’t been experienced in 40 years, and so modern risk management models might not be suited to fully protect liquidity buffers in such conditions.

alla_gil-Oct-28-2021-08-06-19-02-PMAlla Gil

One of the key characteristics of stagflation (as well as other stresses) is drastically changing correlations between risk drivers. In normal times, markets usually have a negative correlation between the short-rate level and the slope of the curve. Another feature of yield-curve behavior is that corporate yields are usually “sticky,” which means that when rates go up, credit spreads go down – and vice versa.

These dynamics can change in a crisis or when stagflation strikes. Rates and credit spreads can increase together, making debt refinancing very expensive, with simultaneous drops in asset prices, increased loan defaults and a depletion of savings deposits. All of these negatively impact liquidity ratios, fueling the need for a different toolkit – one that borrows from the past but also involves new tricks.

Certain liquidity buffers, of course, are prescribed by regulators. But they all recommend that institutions should develop and use their own internal arrangements to measure, monitor and control liquidity, while following the supervisory expectations.

The spirit of these regulations focuses on three issues: (1) integrated, multi-facet risk assessment (e.g., for interest rate, credit and behavioral risks); (2) consistent analysis of economic value (i.e., present value of net assets) and net profit; and (3) incorporation of outliers (i.e., tail risk).

The challenges of satisfying multiple regulations, optimizing the size of liquidity buffers, and preparing for rapidly changing market conditions must be addressed through full range scenario analysis that incorporates synthetic data.

Preparing for the Road Ahead

Constructing comprehensive, interconnected relationships – where explanatory variables are all potential drivers of liquidity risk, and where dependent variables include deposits, levels of loans, costs of funding and other firm-specific outcomes – is the first step in future-proofing liquidity buffers.

When generating scenarios containing all these variables, the dynamics of explanatory ones must incorporate all plausible outcomes, including shocks that break historical correlation structures. Dependent variables should be calculated using equations developed with historical data, but with the ability to predict outliers. Firms can estimate their liquidity needs, at each point in time, through the integration of market variables driving both funding liquidity and asset-side liquidity.

After the first step is complete, you can move on to the second: consistent evaluation of your assets (at each point in time) and the simultaneous assessment of your cash flow needs – from the perspectives of both deposit behavior and wholesale funding.

Assets’ liquidity can be measured by the bid/ask spread or, at a high level, by obtaining from each scenario the prices of respective asset segments. At the same time, a firm can learn about the volume of deposits (run-offs or new inflows) it should expect from each of its product types by analyzing the macroeconomic and market conditions of particular scenarios.

One additional liquidity consideration concerns wholesale funding. When wholesale funding needs to be rolled over, scenario-based rates and spreads should be used to estimate the new cost of funds. To make decisions on how to spread out their wholesale funding, a firm must consider the pros and cons of long-term borrowing vs. short-term debt.

Long-term borrowing reduces liquidity risk, but can be more expensive; short-term debt may have attractive prices at a particular moment, but might also present a substantial liquidity risk at frequent rollover times. Wholesale funding decisions should be considered in conjunction with projected deposit run-offs, prepayments, potential defaults and asset-side prices.

The third step in future-proofing liquidity calls for the inclusion of outliers – or various kinds of stress scenarios. However, this requirement is largely covered in the first two steps, which consider the full range of scenarios. Combined, these three steps should provide a firm with a full picture of its liquidity needs for each scenario, at each point in time.

Liquidity Optimization

The optimal size of a liquidity buffer depends on all of the aforementioned liquidity needs. Importantly, they must be proactively anticipated: the last thing you want to happen is to need money in the middle of a crisis, when everyone else is boosting their liquidity and the cost of borrowing has surged.

Firms must therefore develop contingency plans based on early warning signals to replenish their liquidity buffers for potential future needs. Whether we’re taking about market-wide disruptions or idiosyncratic operational risk events (like fraud or a cyberattack), such contingency plans for crises must include implementation procedures that address identified liquidity shortfalls. Early warning signals are the triggers for such contingencies and can be identified through reverse analysis of the scenarios.

Based on the shocks that occurred under these scenarios, a firm can design an appropriate set of actions - whether this entails, say, conditional capital arrangements, or shifting asset positions to more liquid ones. Alternative management decisions – like adjustments to strategic directions or changes to risk appetite, policies and limits – should also be considered.

Parting Thoughts

To survive even unprecedented risk events, a proactive liquidity strategy must be designed to account for the full range of scenarios. These scenarios can be company-specific or can affect the economy as a whole.

The current unparalleled combination of shocks has created an environment with no useful historical data. Consequently, it is difficult for risk managers to analyze the true impact of these shocks on liquidity.

It is also true, however, that there has been a tremendous change in the speed of information exchange. For example, we’ve seen the emergence of machine-learning (ML) techniques that must be incorporated into the modern approach of addressing both daily and unexpected liquidity risks.

Firms therefore now have the ability to build a new approach to monitoring these risks – one which identifies early warning signals coming from a multitude of risk drivers. Indeed, ML techniques and data integration can be used to optimize liquidity resilience under every scenario.

 

Alla Gil is co-founder and CEO of Straterix, which provides unique scenario tools for strategic planning and risk management. Prior to forming Straterix, Gil was the global head of Strategic Advisory at Goldman Sachs, Citigroup, and Nomura, where she advised financial institutions and corporations on stress testing, economic capital, ALM, long-term risk projections and optimal capital allocation.




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