More granular deposit modeling and enhanced stress testing approaches will augment liquidity risk management practices, says Gavin Pugh at AxiomSL.
Asia Pacific’s banking sector managed well through the last year, despite economies in the region being ravaged by the pandemic. Governments spent vast sums on stimulus programs and promoted credit leniency to shore up collapsing economies.
Permissive lending policies have been further supported by the low-interest rate environment that has been a fixture of financial markets since the global financial crisis. High levels of liquidity have also allowed Asia Pacific’s capital markets to serve as a foundational component for the recovery.
However, loose credit markets and growing regional economic growth have also created conditions that could potentially upend the economic recovery and expose banks and other financial institutions to new risks.
Low interest rates have prompted investors to embrace higher levels of risk in search of higher yields, which in turn has increased market volatility, potentially creating or inflating asset bubbles. These factors could set the stage for shocks caused by swift asset price corrections, thus exposing lenders to large liquidity and credit risks.
These shocks could lead to a reactive tightening of financing conditions and stymie Asia Pacific’s credit markets and liquidity levels. Such risks could be further exacerbated as governments begin to wind down pandemic-driven stimulus measures later this year.
While large liquidity shocks may be unlikely in Asia Pacific’s cash-rich lending markets, the persistent threat of mini-crises puts pressure on banks, which operate on thin margins, to maintain vigilance regarding their capital and liquidity levels and the composition of their loan portfolios.
A Granular Approach to Analysis
To increase their resilience against liquidity and credit risks, it is essential for Asia Pacific banks to undertake more frequent and effective liquidity monitoring and become more proactive in their risk management processes, says Gavin Pugh, Head of Risk Solutions for Asia Pacific at AxiomSL.
Specifically, there is a need for liquidity measurement and management mechanisms to be more responsive to market conditions, liquidity and funding risks, and any changes in key portfolio metrics.
According to Pugh, one capability that is becoming increasingly critical in liquidity risk management is deposit modeling. This is the practice of analyzing current deposits, interest rates, and macroeconomic variables – among other factors – to forecast their impact on bank deposits and liquidity.
Historically, cost and complexity have driven banks to model deposits at the portfolio level, rather than at an individual account level. Given that deposits are the most important funding sources for banks, more granular (and frequent) deposit modeling can help them prepare for and limit potential liquidity and credit risks that may arise in future.
According to Pugh, the parameters banks use in constructing their portfolios are often derived using qualitative judgments, and not based on granular analysis of account-level data. Therefore, efforts to model deposits at the broader portfolio level often lack the detail required for maximizing insights.
More granular deposit modeling is also helpful for effective management of interest rate risk. The Basel standards for IRRBB (interest rate risk in the banking book) require more robust modeling of cash flows and more precise information on credit and liquidity spreads to mitigate interest rate variations over time.
Such variations can expose banks to maturity and rate mismatches, which in turn can impact bank margins and profitability, exacerbate the impact of asset shocks, or in extreme cases, cause bank runs.
While deposit modeling at the account level may help banks to ensure adequate liquidity levels, it can also provide banks additional insights to help them better manage the deposit mix in their portfolios.
Enhancing Responsiveness to Market Changes
Deposit modeling, as part of a good liquidity risk management framework, has become more significant as a result of Basel requirements on LCR (liquidity coverage ratio) and NSFR (net stable funding ratio).
Under the LCR, banks are required to hold enough high-quality liquid assets to fund cash outflows for 30 days, while the NSFR requires them to maintain a stable funding profile based on a longer-term view. LCR and NSFR calculations have presented challenges to banks over the years, in some cases leading to supervisory scrutiny and regulatory fines.
“Many institutions have tried to address these issues using in-house systems to model their deposits, either in siloed business units or as part of a larger transformation effort,” says Pugh. “However, silos can lead to problems with upstream and downstream systems, and large transformation projects are fraught with potential points of failure due to the complexity of the calculations involved.”
Rather than “simply calculating” the ratios, greater data lineage is needed end-to-end: from calculations all the way through to reporting, Pugh notes. Indeed, AxiomSL’s approach is to put all liquidity data in one place for calculations, without the need for transformation, while still preserving the original data sources from across an enterprise. This allows for greater flexibility and responsiveness to market conditions.
When measuring liquidity, best practice dictates that banks have control functions in place in their risk or finance departments. These functions are often driven purely by regulatory requirements, which may not necessarily promote best practice for assessing liquidity if and when market conditions change.
“It is sometimes more in line with best practice to reduce your liquidity thresholds and measure liquidity beyond what is asked by the regulatory requirements,” says Pugh. “Ideally, you want to have the flexibility to perform regulatory calculations and reporting for certain scenarios separate from your internal calculations and management reporting. But it is critical that both internal and external calculations are done using the same transparent and traceable data.”
Expanding Stress Testing Capabilities
The impacts of the Covid-19 pandemic have made liquidity risk managers’ access to comprehensive stress testing capabilities more critical than ever. Specifically, they have expressed a need to run a greater variety of scenarios to account for all potential risks so that they can assess capital and liquidity needs holistically.
Meanwhile, regulators around the world are moving towards stress testing requirements that are more in line with the CCAR (Comprehensive Capital Analysis and Review) used by the US Federal Reserve. The aim of CCAR is to ensure that a bank has a strong, forward-looking capital planning process that accounts for risks and
ensures sufficient capital is on hand to operate under severe adverse conditions.
CCAR was designed to incorporate larger and more granular data sets into stress testing, thus enabling banks to gain a more comprehensive view of their business operations and risk profiles. Often the exercise can help banks detect anomalies in loan portfolios or macroeconomic factors that may otherwise go unnoticed.
Prompted in part by the pandemic, this approach is increasingly being adopted across Asia Pacific as regulators seek to ensure that banks can continue operating and lending during times of economic and financial market stress. The shift means that banks need the ability to not only incorporate larger and more granular data sets in their stress testing processes, but to drill down to source data and demonstrate how they arrived at their projections.
Evolving expectations from regulators – coupled with the ever-expanding palette of risk events banks need to model as part of their internal capital management exercises – mean that banks will need capabilities to model numerous new scenarios that all draw from the same granular data sets.
“While banks need the flexibility to model their own scenarios as an internal capital management exercise, they also need to ensure consistency between their internal calculations and regulatory reports,” Pugh says. “Ensuring highly scalable data management practices is imperative.”
Future Proofing for Uncertainty
In the year ahead, Asia Pacific banks are expected to continue to focus on risk management to guard against shocks that could arise from heightened volatility, potential asset price corrections, and a possible rise in borrower defaults when stimulus measures are withdrawn.
Traditional silo-based liquidity management practices that do not account for other departments and business lines may prove insufficient in a changing landscape. While techniques such as granular deposit modeling and expanded scenario analysis can enable better preparedness for risk, the complexity of calculations and the
heavy data ask will necessitate advancements in technology to cope.
Ensuring liquidity resilience is as much a technology challenge as it is an operational and regulatory one. Banks require speed and efficiency in their modeling and stress testing practices to match the current pace of change in financial markets.
With the uncertainties that lay ahead, the ability to confidently comply with reporting requirements while also optimizing risk management and internal capital planning will prove to be a competitive advantage that ensures banks are prepared for the next crisis, whenever it arrives.
In December 2020, AxiomSL was recognized for its Credit Risk Modeling, Capital & Liquidity Modeling and Trade Reporting solutions by an independent panel of industry experts serving as judges at the 3rd Regulation Asia Awards for Excellence 2020.
More information about AxiomSL’s Global Liquidity Monitoring, Risk Management Calculations, and Reporting Solutions is available here.