Ahead of the Liquidity Risk Management Congress, October 17-18 in New York City, Greg Muenzen, Principal, Novantas shares his insight on integrating liquidity risk management into business line management.
Greg, can you please tell the Risk Insights readers a little bit about yourself, your experiences and what your current professional focus is?
I have been a management consultant to the financial services industry for the past 8 years, focusing primarily on Treasury and Risk issues. I have worked with financial institutions ranging from small community banks to international money center banks across the U.S., as well as in Canada, Australia, and Latin America. Most often I lead analytically-focused engagements centered around liquidity, asset/liability management, interest rate risk, stress testing, and funds transfer pricing applications. Thematically, these engagements tend to involve behavioral modeling of deposit and loan portfolios—which entails constant innovation in terms of the analytic and econometric approaches we apply to client problems.
At the Liquidity Risk Management Congress, you will be speaking on your insight regarding – Integrating liquidity into business line management. Why do you believe this is a key talking point in the industry right now and what can risk professionals gain from this insight?
First, it is a key talking point because liquidity behaviors are important drivers of profitability, and as such they should be integrated into funds transfer pricing so that banks can, for example, credit deposit gathering activities appropriately. Regulation has been a recent catalyst, given the very real economic costs associated with liquidity ratios becoming a binding constraint. However, another reason for the heightened discussion is the recent evidence that liquidity crises are still real-world problems—there will continue to be reputational and solvency events, and it is easier than ever for the average depositor to move funds given technological advances in online and mobile. For that reason, it becomes even more critical to measure expected liquidity behaviors and use those insights to drive business activities such as deposit gathering.
In your experience, how can financial institutions best manage the development of strategies for optimizing liquidity in a changing interest rate environment?
Deposits are certainly top of mind when considering the changing interest rate environment. In my view, leading institutions understand that liquidity characteristics are part of a broader evaluation framework, which also considers interest rate sensitivity, price sensitivity, behavioral life, and other characteristics. With that framework in mind, an institution might “solve” for liquidity by paying aggressively for stable retail deposit funding, but then create a problem for themselves in a rising rate environment should the acquired balances be from price-sensitive, short-tenured customers. This means we are not optimizing for liquidity, but certainly optimizing with liquidity in mind. I think the other imperative is to consider a management view of stressed liquidity, distinct from the existing regulatory view of stressed liquidity communicated through LCR. While the LCR is a fair starting point, and represents the ultimate binding constraint, regulators are increasingly challenging banks to analyze internal data and strengthen segmentation around stressed liquidity outflows, to create a challenger view of stressed outflows and liquidity buffer size.
What are the essential considerations that need to be made when incorporating liquidity costs into funds transfer pricing for line of business measurements?
In my opinion, a better practice FTP framework considers two liquidity components: business-as-usual structural liquidity and stressed liquidity. Together, these components suggest a term structure for deposits. Structural liquidity measurement answers the question “at what tenor can my deposit funds be conservatively invested?”. For structural liquidity, we tend to model deposit balance decay, often separating between account decay and average balance per remaining account, and unsurprisingly there are distinct, fit-for-purpose statistical approaches we apply to each of those measurement exercises. On the other hand, stressed liquidity answers the question “what portion of my deposits funds are long-term investable in the first place?”. For stressed liquidity, we usually focus on triangulating a range of “worst-case” stressed outflows, drawing from both internal data and external data sources where appropriate. Consistent with regulatory guidance, we have an eye towards differentiating runoff by scenario and by time horizon.
In your opinion, what approaches can be taken to identify measures of the market cost of liquidity?
It is well established that the market cost of structural liquidity is determined in FTP through the assignment of liquidity premia. On that front, one notable challenge in the current market environment is the potential illiquidity of market funding at certain yield curve tenors (e.g., 6-mo. funding). We have observed our clients using a wide range of approaches to deal with this problem, such interpolating illiquid yield curve points with neighboring liquid points, though a consistent but a consistent trend we have observed is the revisiting of term funding quotas.
The other notable challenge in the current environment involves the calculation and allocation of the liquidity buffer cost. For cost calculation, we have debated using the actual securities portfolio yield vs. a benchmark yield with our clients, as well as the appropriate funding term for the liquidity buffer. For cost allocation, we are seeing a broadening allocation of cost from liabilities to undrawn facilities to off-balance sheet capital markets instruments.
What, in your opinion does the future hold for liquidity risk professionals, and how can they keep up the increasing change?
It is hard to predict how the future will evolve for liquidity risk professionals, but I suspect there will be heightened scrutiny on liquidity risk measurement techniques going forward. We have seen the spotlight shift from stress testing to interest rate risk measurement, and we are beginning to see signs of increased regulatory scrutiny on liquidity. Given this, we suggest institutions take care to accumulate as much data as possible, ideally with frequent data capture (e.g., weekly periodicity) and robust account- and customer-level attributes for segmentation analysis. While liquidity risk is not conducive to econometric modeling in the same way as stress testing or interest rate risk measurement, we see an increasing bar for supporting runoff assumptions through deeper analysis of internal and external data.