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January 09, 2011
Implementing Liquidity risk IT - Road ahead
Liquidity risk implementation is giving huge nightmares to software/IT arms of financial institutions across the world. Why so? Let us first try to understand what is Liquidity risk quickly.
Funding Liquidity risk – Current or prospective risk arising from an institution’s inability to meet its liabilities and obligations as they come due without incurring unacceptable losses.
Asset liquidity risk – Risk that a position cannot be unwind or offset easily at short notice without significantly influencing the market price, because of inadequate market depth or market disruption.
Historically banks have developed and maintained large and complex standalone systems with minimal opportunity for real time data sharing. Data normally in a bank is shared asynchronously through overnight batch processing which largely results into data incorrectness and data integrity issue.
Funding liquidity risk demands data across enterprise wide be consistent and correct. Balance sheet analysis, profitability analysis, cash flow analysis form the cornerstone of such kind of risk analysis though maturity time frame defining the payment/obligation closure time frame of liabilities and obligations also play an important role in defining this kind of liquidity risk. Apart from data correctness challenge, typically data availability should ensure fresh and right data when such risk reports are run and generated which translates to the fact that data “contemporariness” is also vital to paint right liquidity scenario, stale data obtained from overnight batch may loose the “contemporariness” aspect. The normal batch architecture followed in most banks to pass data from one system to another one may not be the right solution as it can provide “stale” data specially when one intends to run liquidity reports daily or after every few hours. Real time data architecture ensures data is transferred across systems real item would be the apt solution for such kind of requirements. This kind of architecture demands more funding apart from changes in technology ,people, process approach to embrace it.
Asset liquidity risk would require in depth analysis of market, more systemic and macro analytical approach in this case would enable the determination of market depth or scenario. This would also require understanding of vital parameters like interest rate and its movement, volatility of market which clearly becomes unpredictable driven by lack of liquidity. Measuring and monitoring of liquidity level is as important as liquidity uncertainty. Liquidity uncertainty is characterized by episodes in which the liquidity faced by a buyer or seller of a financial instrument virtually vanishes, reappearing again a few days or weeks late a phenomena defined as liquidity black hole. This is evident in unstable environment. Systemic and more macro analysis with specifics like liquidity black hole would enable a bank to understand Asset liquidity appropriately.
Measurement of both these kind of liquidity risks would demand highly sophisticated analytic backbone which can slice and dice data in different dimensions apart from intrinsic requirement of handling and processing large volume data.
Last but not the least understanding of domain is a big challenge. In today’s world, there are many self claimed, epistemic risk experts who are out to confuse everybody. I met recently a product head of risk IT product company who was lamenting lack of proper liquidity risk analytic framework. He went on further to indicate that how his company has wasted money building analytics around profitability, balance sheet and cash analysis, I was wondering what stops him in extending this existing analytic framework to build a holistic liquidity risk analytic framework, he is so close but yet so far.
Posted by kb0905 at January 9, 2011 07:12 AM
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