Practices and Emerging Trends in Asset Liability Management and Liquidity Risk

Asset liability management (ALM) plays a critical role in weaving together the different business lines in a financial institution. Managing liquidity and the balance sheet are crucial to the existence of a financial institution and sustenance of its operations. It is also essential for seamless growth of the balance sheet in a profitable way.

In recent times, even large multinational financial institutions were in a deep liquidity crisis and in dire need of external intervention for survival. This resulted in regulators attaching high importance to new measures needed to ensure a sound liquidity management system.

The core functions of ALM and liquidity risk management enable financial institutions to manage and mitigate the risks within accepted levels. Of course, financial institutions are increasingly inclined to carry out this process profitably.

Financial institutions borrow and lend for different terms and maturity tenors. Apart from equity and retained earnings, the average maturity of borrowings and liabilities tend to be on the short to medium-term buckets. On the asset side, the maturity tends to be across a broad range from overnight to as long as a home mortgage could run. A financial institution is normally required to participate in lending short, medium and long terms depending on the nature of financial products on offer and what segment of the market the bank operates within.

ALM Core Functions: Managing Interest Rate Risk, Structural Gaps and Liquidity

The ALM core function consists of managing maturity gaps and mismatches while managing interest rate risk within the overall mandate prescribed by the asset liability committee (ALCO). Typical activities initiated by an ALM team would include a healthy funds transfer pricing (FTP) mechanism and the vigilant management of structural gaps, interest rate sensitivity, liquidity and thorough ALCO reporting.

In fact, the ALCO function is critical to ALM function and serves as the reviewing and approving authority for several key decisions including balance sheet structure, gap analysis, capital adequacy ratios and above all pro active management of balance sheet.

Building Blocks of ALM

Considering all these factors, an institution should consider addressing ALM both functionally and with the appropriate technology. The building blocks of an ALM solution could include:

Cash-flow engine

A significant aspect of ALM consists of forecasting and generating future cash flows based on historical data and assumed scenarios. A time-tested cash flow engine that’s capable of modelling a wide range of financial products on and off the Balance Sheet is a crucial part of an ALM solution.

Unified data model

Having a pre-defined, financial products-specific and time tested analytics data model accelerates implementation by providing a head-start. Further more it helps leverage and makes much wider use of data for a wider range of analytics apart from ALM. This is particularly useful considering that enterprise-wide time series data at a granular level is stored in our analytical applications over time.

Market rates and economic scenarios

Define external economic indicators as well as define interest rate scenarios and forecast rate movements. Maintain economic assumptions separately to quickly develop alternative forecasts and stress test the balance sheet under alternative environments.

Deterministic and stochastic analysis

There are broadly two approaches to making ALM forecasts. In the deterministic approach, the user makes explicit assumptions about interest rate movements and forecasts interest rates and currency exchange rates for various scenarios and different term points. In stochastic scenario, the forecast rates are modelled using Monte Carlo simulation method and the output is then generated at desired confidence intervals.

Behaviour modelling

The contractual behaviour alone is not adequate in modelling the balance sheet. It is essential to take into consideration behavioural maturity based on historical observations in order that cash flow predictions are more reliable and in tune with demonstrated behavioural trends. It is also possible to develop a model for behavioural trends using certain additional and optional infrastructure components.

Powerful analytical reporting

Identify solutions with business intelligence pre-integrated with the analytical data model referenced above in order to analyse and report the cash flow outputs and financial element calculations.

Emerging Trends in Liquidity Management Guidelines

Regulators in many countries are attaching increased significance to liquidity management and its impact on ALM and funds transfer pricing. There is an increasing realisation that while different types of risks and exposures may have contributed to the financial crisis in many ways, the liquidity, or lack thereof, contributed significantly to the closure of financial institutions. There is a school that believes that institutions like Lehman might have survived if they were provided liquidity and an extended life.

Regulators such as the Financial Services Authority (FSA) were early to come up with guidelines outlining a new liquidity framework. The new regulatory framework includes scenarios and assumptions, revised and new guidelines for liquidity buffers and new reports to be furnished by the banks. The revised approach is focusing not just on how things are working out in one bank but the regulators seem to be increasingly interested in observing potential systemic risks and pockets of liquidity concentration in order to anticipate and stall risk events before they snowball into a crisis.

The liquidity management framework impacts the assumptions underlying FTP too. It is common practice in many banks to include a liquidity premium on top of base FTP. Financial institutions are now attaching importance to determining liquidity premiums and reviewing practices.

The Dodd-Frank Act has dealt with the ‘too big to fail’ syndrome in a manner that going forward the inclination is to let banks liquidate their affairs in an orderly fashion rather than let governments rush to bail them out on taxpayers’ money. This implies that despite the costs associated with increasing liquidity buffers, there is some irrefutable wisdom in ensuring liquidity buffers for bad days for prolonged and sustained stress scenarios.

The liquidity framework requires that financial institutions have a robust ALM and liquidity risk management system, and that the banks comply with the new regime in an efficient and timely manner.

Capital Requirements

The revised capital regulations seek to address liquidity from a short- and long-term perspective. The revised rules require banks to hold enough capital to survive a 30-day severe stress scenario. This rule is ‘observational’ until 2015 and will be watched for any unintended consequences. The long-term liquidity is sought to be addressed by a new set of requirements that seek to align the assets and liabilities. The net stable funding ratio, though back in drawing board, will re-appear in some updated form, though its application will have to wait until 2018.

Stress Testing

Stress testing on a periodical basis is crucial to establish resilience levels and simulate effectiveness of remedial measures in the event of a crisis. A liquidity risk management solution paves way for shocking the balance sheet under various scenarios and assumptions. The important part of stress testing is to ensure that assumptions are fine tuned in line with anticipated and changing realities and taking into account the liquidity buffers under stressed conditions.

ALCO stipulations and regulatory stipulations have made it essential for businesses to simulate a run on the bank and assess the liquidity in a stressed scenario. The frame-work clearly enables a bank to visualise taking recourse to liquid asset buffers and rehearse the application of liquidity contingency planning.

Behavioural Modelling and Assumptions

Behaviour modelling and behavioural assumptions are being revisited by some financial institutions in order to validate and test the continued relevance of historically used behavioural assumptions. Behavioural assumptions address cash flow assumptions underlying core and non-core portions in non-maturity or indeterminate accounts, deposit roll-overs and prepayment events.

The reliability and accuracy of ALM reports as well as their dependability for purposes of forecast gaps, projected cash-flows and balance sheet planning depend to a reasonable extent on assumptions underlying those aspects.

Counterbalancing

A counterbalancing strategy consists of one or multiple counterbalancing positions covering the fire sale of marketable and fixed assets, creation of new repos, rollover of existing repos and raising fresh deposits or borrowings. The impact of the counterbalancing strategy on the liquidity gaps is assessed and further refined. Additionally, multiple counterbalancing strategies are allowed to be defined on the same baseline liquidity gap report thereby enabling banks to identify and adopt the optimal strategy as part of its contingency funding plan.

It is notable that a robust asset liability management eventually becomes the foundation for comprehensive balance sheet planning. It is possibly not an exaggeration to state that the practices on ALM, FTP and profitability planning all ultimately reflect in balance sheet planning and this could potentially change the way businesses approach planning, thereby leading to proactive and profitable management of the balance sheet.

More than ever the constituents in financial institutions are increasingly aware of the risk weighting of assets and capital requirements for incremental business, and they recognise that it pays to focus on maximising profitability while optimising capital requirements. The management in the bank would like to ensure that beyond the capital planning and ALM teams, the stakeholders in each line of business are able to appreciate the cost of capital required for respective lines of business and how it is impacting the bottom-line ultimately.

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