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Toronto/London - December 18, 2007 -

Recent turmoil in the global financial markets has provided new evidence for the importance of liquidity risk management as a sound business practice, with what started out as a relatively confined, credit related problem in the US sub-prime real estate market, resulting in a global credit crunch and a systemic liquidity shortage.

A White Paper released today by Algorithmics, examines the specific vulnerabilities still facing financial institutions currently, and best practices for liquidity risk management going forward.

Dr. Mario Onorato, Director of Enterprise Value Based Management said, 'For too long liquidity risk has been the forgotten risk - but given the events of 2007, I believe it will be the next focus for enterprise risk management.

'Our research suggests that best practice liquidity risk management is evolving from a framework of ratios and limits, to one where probabilistic methods are key. There is no single measure that will provide an assessment of this risk, but rather a simulation framework is required which incorporates probabilistic scenarios, behavioural and business strategies and extreme stress tests.'

In terms of liquidity risk management, Algorithmics' research highlights two types of liquidity risk. That which is more commonly discussed and can be addressed through sound capital measures, is market liquidity risk - the risk that a firm cannot easily offset or eliminate a position without significantly affecting the market price because of inadequate market depth or market disruption.

However the often neglected issue is funding liquidity risk - the risk that the firm will not be able to efficiently meet both expected and unexpected current and future cash flow and collateral needs, without affecting daily operations or the financial condition of the firm.

Algorithmics concludes that best practice in managing funding liquidity risk is for financial institutions to apply probabilistic analysis to liquidity risk in the same way as market and credit risk and increase analytical accuracy by incorporating dynamic stochastic methodologies (versus deterministic methodologies).

In particular, the correct way to build up an effective protection against funding liquidity risk is to clearly define from both a short and a long term risk-return perspective, an adequate amount of both cash and promptly marketable assets and other instruments to quickly raise funds (such as irrevocable standby facilities), and to maintain that level, adapting it in time according to business developments.

This requires:

An emphasis on quantitative analysis using a stochastic approach for determining risk exposures. The more comprehensive the scope of the scenario-based analysis undertaken, the more effective the cash flow projections and risk evaluation can be. Stress scenarios within a stochastic framework provide new information as to where liquidity risks concentrate and which risk factors will produce the most exposure.

Going beyond the static maturity ladder of cash flow projections into dynamic analysis. Integrating dynamic assumptions about future operations into liquidity risk analysis produces a more realistic view of future exposures. As a comprehensive step, balance sheet planning can be subject to probabilistic-based scenarios. Virtually any number of specific stress scenarios can be generated, producing a more comprehensive picture of potential risk over time. The combined technique reveals likely future evolution of the balance sheet and the inherent risks, therefore increasing the effectiveness of contingency planning.

Organizational best practices which ensure awareness at the senior management level of a firm's funding liquidity risk. The management of liquidity risk under a crisis context does entail a trade-off between risk and underperformance, which in some cases can be very hard to be optimized. Considering the low probability of occurrence of funding emergencies, incurring high costs for extensive protections could appear irrational.

Dr. Onorato added, 'Managing the trade-off between risk and underperformance is one of the key challenges for the executive team, as has been well demonstrated during the sub-prime crisis. Failure to assess this trade-off correctly, can have dire consequences for a financial institution. However solutions such as Algo ALM allow companies to apply dynamic stochastic analysis to liquidity risk in the same way as they currently do for market and credit risk. Only by modelling the planned evolution of the balance sheet within a dynamic stochastic framework will companies be able to use their capital most efficiently and gain an enhanced ability to develop effective contingency plans.'

'Liquidity Risk Management: Assessing and Planning for Adverse Events', by Fabio Battaglia, Steven Good and Dr. Mario Onorato www.algorithmics.com/EN/publications/whitepapers/1207/WhitePaper1207.cfm

Notes to Editors:

Algorithmics is the world's leading provider of enterprise risk solutions. Financial organizations from around the world use Algorithmics' software, analytics and advisory services to help them make risk-aware business decisions, maximize shareholder value, and meet regulatory requirements. Supported by a global team of risk experts based in all major financial centers, Algorithmics offers proven, award-winning solutions for market, credit and operational risk, as well as collateral and capital management. Algorithmics is a member of the Fitch Group.

Algo ALM provides a comprehensive assessment of earning sensitivity and future market valuation using a dynamically modeled balance sheet. Earnings and value are supported by a single, integrated and analytical framework with common scenarios, growth and reinvestment assumptions, as well as common cash flow generation and valuation models.

Balance sheet professionals can aggregate, measure, monitor and restructure the market and liquidity risk of the balance sheet according to their specific needs through Algo ALM. In addition to satisfying the liquidity risk and interest rate risk in the banking book requirements of Basel ll, Algo ALM supports:

• a full range of assets, liabilities and off balance sheet instruments;
• flexible facilities to chart accounts as well as group and consolidate transactions;
• traditional ALM analytic tools and reports including static and dynamic interest rate and liquidity gap reports, beta and shift gap reports and duration and convexity reports;
• advanced analytic tools including dynamic balance sheet income simulation, full liquidity and funding risk analysis, VaR and market value sensitivity analysis;
• a patented scenario-based optimizer to assess the trade-off between earnings and values;
• a comprehensive EaR framework, which includes the assessment of losses from credit events.

Fitch Group is the parent company of Fitch Ratings, a global rating agency dedicated to providing the world's credit markets with independent and prospective credit opinions, research and data. The Fitch Group also includes Derivative Fitch, an independent provider of a suite of ratings and comprehensive services for the credit derivatives market; Algorithmics, the world's leading provider of enterprise risk solutions; and Fitch Training, which offers high-quality analytical training for financial professionals. The Fitch Group is a majority-owned subsidiary of Fimalac, S.A., headquartered in Paris, France. For additional information, please visit www.fitchratings.com; www.algorithmics.com; www.fitchtraining.com; and www.fimalac.com.

© 2007 Algorithmics Software LLC. All rights reserved. ALGO, ALGORITHMICS, Ai & design, ALGORITHMICS & Ai & design, KNOW YOUR RISK, MARK-TO-FUTURE, RISKWATCH, ALGO RISK SERVICE, ALGO CAPITAL, ALGO COLLATERAL, ALGO CREDIT, ALGO MARKET, ALGO OPVANTAGE, ALGO OPVANTAGE FIRST, ALGO RISK and ALGO SUITE are trademarks of Algorithmics Trademarks LLC.

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