Tightening up

Building a robust liquidity risk management system is the top priority at many banks and insurance companies, ahead of the expected introduction of tough new liquidity risk regulations this year and the easing of government support for many financial markets. But there are significant technology challenges. Clive Davidson reports

For all its focus on historical data, the financial industry has a surprisingly short memory. Given a decade or so of benign markets, it quickly forgot that liquidity risk is a killer that can destroy a bank in no time. It took the virtual overnight collapse of banks such as Northern Rock in the UK and Washington Mutual and Wachovia in the US to remind the industry of its destructive force. As the Basel Committee on Banking Supervision (BCBS) of the Bank for International Settlements stated in its International Framework for Liquidity Risk Measurement, Standards and Monitoring report in December 2009, “a key characteristic of the financial crisis was the inaccurate and ineffective management of liquidity risk”.

To some degree, Asian banks were better prepared than their US or European counterparts for the current financial crisis, having already experienced a regional liquidity shortage in 1997–98. Countries such as Japan, Hong Kong and South Korea have had comparatively demanding liquidity analysis and reporting requirements for some time, and the healthy size of foreign exchange reserves helped many banks in the region weather the worst of the storm. 

However, the severity of the global financial crisis, and events such as the run on Bank of East Asia in September 2008, have alerted local regulators and banks to the continuing threat of liquidity risk in their backyard as well as internationally. It has also highlighted the need to substantially upgrade policies, practices and technology to measure and manage liquidity more accurately and effectively.

The UK’s Financial Services Authority (FSA), the Committee of European Banking Supervisors and, to some extent, the Australian Prudential Regulation Authority, have taken the lead in their attempts to redefine regulatory requirements for liquidity risk. And the Basel Committee is consulting on a new international framework for liquidity risk measurement and monitoring, which is expected to appear in June. The consultation is based on principles the committee first set out in September 2008 (Principles for Sound Liquidity Risk Management and Supervision) and include a requirement for board and senior management oversight; the establishment of policies and risk tolerance; the use of liquidity risk management tools such as comprehensive cashflow forecasting, limits and liquidity scenario stress testing; and the maintenance of a sufficient cushion of high-quality liquid assets to meet contingent liquidity needs.

These principles, however, present a number of challenges for banks, not only in terms of organisational change and policy development, but particularly with respect to having capabilities for comprehensive cashflow forecasting, limits to monitoring and liquidity scenario stress testing. It will take some time for the international framework to be put in place and for local regulators to develop their own interpretations and set time frames for implementation. Nevertheless, it appears inevitable that the regulatory requirements for liquidity risk management will be tighter in the future. Meanwhile, many banks that watched how rapidly liquidity issues engulfed institutions such as Northern Rock and Washington Mutual during the crisis are not waiting for new regulations to spur them into action.

Dah Sing Bank, a regional retail bank based in Hong Kong, is acutely aware of the lessons of the crisis. “Washington Mutual could be categorised as a regional retail bank, and the type of situation it got into is something we want to try to manage and not have happen in Hong Kong,” says Frederic Lau, chief risk officer at Dah Sing Bank in Hong Kong.

John Janse van Rensburg, a risk manager at a leading Australian bank, says it was partly by luck and only partly by design that the Australian banking system was not as exposed to complex leveraged mortgage-backed securities as occurred in the US. But despite the reduced exposure, there was still a ‘flight to quality’, with retail and wholesale deposits moving to the big four Australian banks at the expense of smaller institutions. And as the crisis subsides, and the Australian and other government deposit guarantees are lifted, new liquidity risks and challenges will emerge that require banks to have more sophisticated liquidity risk measurement and management capabilities in place, he says.

Meanwhile, it is not just about avoiding disasters – there is a potential upside to adopting more sophisticated liquidity risk management practices, says Trevor Holford, solutions manager for London-based technology supplier Misys’ Almonde asset and liability management (ALM) and regulatory reporting system, whose Asian users include India’s IDFC and French Polynesia’s Bank Socredo. “In many financial institutions the internal liquidity rules are likely to be more demanding than those imposed by the regulator, and those banks that can show their liquidity ratios are set above the regulatory thresholds can expect greater liability placements from all depositor sectors, more corporate business, potentially better lending opportunities and even an increase in their credit rating,” says Holford.

Lau says regulatory requirements are typically minimal standards, and banks often go beyond them. “For example, in Hong Kong the regulators require banks to have a liquidity ratio of at least 25% – we keep a much larger margin than that,” he says. “In addition, we do a lot of things the regulators do not require us to do, such as sensitivity analysis on the behaviour of depositors. Also, along with many banks all over the world, we have been trying to improve our liquidity stress-testing capabilities over the past three years.”

Stress testing is emerging as the most critical factor to effective liquidity risk measurement and management, both from the regulatory and banks’ own point of view. “Liquidity stress-testing is now high on the radar of supervisors and regulators, and is part of the macro-prudential framework being advocated world-wide to introduce conformity in liquidity risk supervisory approaches,” says Janse van Rensburg. However, developing and putting into operation a best practice liquidity stress-testing framework is a major challenge. “It requires that all cashflows, both interest and principal flows, are derived at the contract level across all entities in a group to derive net funding requirements. This dictates the need for granular data on all contracts, applied to a proper cashflow engine or simulation model that enables the modelling of scenario stress tests,” he says.

Gathering the granular data is the first task, and here banks have been pushed in the right direction by the Basel II capital accord, which requires comprehensive and detailed data for reporting. Banks are likely to have to work on their data for liquidity risk management in terms of ensuring its quality and completeness. And once the data is prepared, there is the question of the technology to measure liquidity risk, undertake stress tests and calculate the liquidity reserve requirements.

Traditionally, banks have calculated and reported their liquidity risk using their treasury or ALM systems, but in most cases these will not be adequate to meet the new best practice or regulatory requirements. “In the past, ALM systems have churned out daily reports of cashflow gaps and duration gaps, but it was more often than not limited to only a part of the bank,” says Mike Hamm, UK managing director for Luxembourg-based financial analytical software company Fernbach-Software, whose clients include Industrial and Commercial Bank of China, Woori Bank and Mizuho Trust and Banking. For liquidity risk, banks will need to pull cashflow information from across the institution, aggregate it, reconcile it, analyse it and generate reports. And if the daily liquidity reporting becomes the norm as the UK’s FSA is arguing for, this will have to all be done in one night. “Few traditional ALM systems will be up to this,” says Hamm.

Having collected all the relevant transaction data, a bank has to generate cashflows for each transaction. Cashflow generation is a common function, and banks are likely to have several cashflow engines within their trading, treasury, risk or core banking systems. However, it is unlikely a bank will simply be able to take the cashflows from each of these and aggregate them for an enterprise-wide total. “If you take, say, a simple interest rate derivative, the trading system might generate its cashflows one way, the treasury system generates them another way and the risk system another way again,” says Hamm. “The differences might not be material at an individual transaction level, but when you add up the little differences they could become significant.”

Therefore, banks need a single centralised engine to generate cashflows consistently for all transactions from across the business, he says. It is also often not feasible for a bank to simply nominate one of its existing cashflow engines for the central liquidity risk management role because the engines are often embedded in their applications or not up to the task, even if they can be decoupled. Hence when banks want to upgrade their liquidity risk measurement and management this frequently entails an upgrade of their ALM system and/or the implementation of specialised liquidity risk modules. A number of risk management software suppliers now offer such systems or modules, including Misys, Fernbach, Sydney-based Razor Risk, London-based Lombard Risk, Toronto-based Algorithmics, Pennsylvania-based SunGard and Honolulu-based Kamakura.

Dah Sing Bank is in the process of a full-scale upgrade of its risk and asset and liability management systems, implementing the Kamakura Risk Manager (KRM), which includes a cashflow engine that can handle the cashflows for liquidity risk analysis, as well as scenario simulation and stress-testing functionality.

Also implementing KRM is South Korea’s Samsung Fire & Marine Insurance. Although the financial crisis was largely a banking crisis, the collapse of what was the world’s biggest insurer, AIG, demonstrated how other financial institutions were not immune to its impact and that liquidity risk is universal in the financial sector. Samsung Fire & Marine has attempted to keep abreast of global best practice in insurance liquidity risk management, developing its own model which it has assessed against the same benchmarks used by Axa, Munich Re and other leading global insurers, says Hyun Seo Park, risk manager at Samsung Fire & Marine in Seoul.

Samsung Fire & Marine’s liquidity risk model incorporates measures such as policy surrender rates and loss ratios, as well as stress scenario simulations of interest rates and gross domestic product. However, the main factor influencing liquidity is claims, and the level of claims is often out of an insurer’s hands because it is the result of accidents or demographic trends, says Park. Because of this, the company has introduced the concept of liquidity assets in preparation for unexpected events such as financial crises and natural catastrophes. The company defines liquidity assets as assets that are convertible to cash within three days with minimum loss. “And we have set a guideline to reserve enough [liquidity assets] to cover a stress scenario that mimics the [Asian] financial crisis of 1997,” says Park.

Because banks were so caught off guard by the current financial crisis, there is a reluctance to rely too heavily on history in devising stress scenarios. “A lot of the time stress testing is based on historical events,” says Lau. “However, the financial crisis taught us that stress tests based only on historical events is not sufficient, and that we have to focus on the current situation and forecast what might happen in the future. And to do that, we have to be more innovative.”

A particularly difficult aspect of the future is predicting customer behaviour. “During a financial crisis, depositor or borrower behaviour can change, so we have to try to be very close to the market,” Lau adds. A complicating factor is that governments around the world introduced deposit guarantees where they didn’t already have them and this had an impact on depositor behaviour. “Now what happens when the government withdraws these guarantees? How will depositors’ behaviour change? This is challenging [to incorporate in a stress test].”

Janse van Rensburg highlights the importance of incorporating macro-economic factors in stress test scenarios because of the inter-relatedness of liquidity risk with interest rate risk and credit risk. “The ALM model should consider all these factors and how they may impact each other – for example, higher interest rates may change customer behaviour, which manifests itself in higher deposit levels, higher prepayments and also defaults, which in turn impact liquidity risk,” he says. Furthermore, reputational risk is highly correlated with liquidity risk, as was shown by the experience of Northern Rock and Washington Mutual, which should therefore also be considered when looking at liquidity risk, he says.

To some degree, banks have been able to avoid fully addressing liquidity risk while governments have pumped liquidity into the markets. But this won’t last forever, and the regulators are laying the groundwork for a substantial increase in liquidity risk management requirements. For most banks this will entail a review of their processes, and few are likely to get away without some significant degree of upgrade to their systems.