While welcoming the FSA’s international objectives for standardized reporting and supervision of liquidity risk (Consultation Paper (CP) 09/13), Algorithmics, the leading provider of enterprise risk management systems, recognizes that this will be difficult for regulators to achieve. Algorithmics predicts that for the foreseeable future, institutions operating across regulatory jurisdictions will continue to face different interpretations of funding liquidity risk requirements from regulators.
There is now consensus that the main tools of liquidity risk best practice should include stress testing and dynamic scenarios, as Algorithmics proposed in its 2007 White Paper. However, between regulatory jurisdictions there is currently a lack of consensus on how these risk measures should be interpreted in terms of regulatory requirements, specifically around the amount of liquidity funding that institutions are required to hold.
“We have seen a high level of international convergence around regulatory capital requirements for Basel II. However, we do not see this same level of convergence happening for liquidity risk because, currently, regulators are interpreting the requirements for holding funding liquidity differently. Large institutions, faced with satisfying different reporting and compliance requirements for each jurisdiction in which they operate will need robust liquidity risk systems with enterprise liquidity risk frameworks and solid data infrastructures, in order to comply across a number of geographiesâ€, said Dr Mario Onorato, Senior Director of Balance Sheet Risk Management Solutions at Algorithmics and Honorary Senior Lecturer, Cass Business School in London.
In its response to the FSA CP 09/13, Algorithmics has requested early clarification of the criteria and weighting for the Individual Liquidity Guidelines (ILG) to help decision makers in firms understand the cost/benefit relationship that exists between investment in risk infrastructure and the corresponding lower cost for maintaining liquid assets.
Besides international convergence and definition of ILG, Algorithmics’ response also covers the FSA’s implementation timetable, reporting proposals, feedback of data, balance between quantitative and qualitative elements of the new regime, standardized reporting and the usage of behavioural data.
There is now consensus that the main tools of liquidity risk best practice should include stress testing and dynamic scenarios, as Algorithmics proposed in its 2007 White Paper. However, between regulatory jurisdictions there is currently a lack of consensus on how these risk measures should be interpreted in terms of regulatory requirements, specifically around the amount of liquidity funding that institutions are required to hold.
“We have seen a high level of international convergence around regulatory capital requirements for Basel II. However, we do not see this same level of convergence happening for liquidity risk because, currently, regulators are interpreting the requirements for holding funding liquidity differently. Large institutions, faced with satisfying different reporting and compliance requirements for each jurisdiction in which they operate will need robust liquidity risk systems with enterprise liquidity risk frameworks and solid data infrastructures, in order to comply across a number of geographiesâ€, said Dr Mario Onorato, Senior Director of Balance Sheet Risk Management Solutions at Algorithmics and Honorary Senior Lecturer, Cass Business School in London.
In its response to the FSA CP 09/13, Algorithmics has requested early clarification of the criteria and weighting for the Individual Liquidity Guidelines (ILG) to help decision makers in firms understand the cost/benefit relationship that exists between investment in risk infrastructure and the corresponding lower cost for maintaining liquid assets.
Besides international convergence and definition of ILG, Algorithmics’ response also covers the FSA’s implementation timetable, reporting proposals, feedback of data, balance between quantitative and qualitative elements of the new regime, standardized reporting and the usage of behavioural data.
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