Service overview

Market and liquidity risks assessment

Figure 2. Example of a crisis’ effect on the credit system - extra losses from main risks in a negative macroeconomic situation.

A bank needs to take customers’ money to give credit. Loans’ interest rates and lifetime differ. Possible debit-credit problems bring about so-called liquidity risk. There is always a possibility greater than zero that a bank will be unable to meet its obligations at some point during the year. This is called cash gap and usually happens when a bank’s customers demand their money back and the bank is short of ready cash. To minimize this risk banks adopt special borrowing policies, e.g. match funding – a way to balance assets and liabilities across loan lifetimes and currencies.

Besides liquidity risk, there are also market risks. In case of retail credit banks face both interest and currency risks. Spreading these risks across lifetimes and currencies helps avoid the worst surprises. To develop a good strategy for attracting funds banks must evaluate and forecast asset structures. This way deals can be planned in a sensible order. To protect against interest risk banks try to attract “long money” when interest is low and create buffers against losses when interest rises high.

RRAS has a in-built assessment and forecasting function for finding effective funding strategies, reducing market and liquidity risks.



The author presents the methods for studying credit portfolio behavior in the Modeling and Stress-Testing Credit Portfolio Behavior are partly based on so-called “dual time dynamics” method. This work suggests using dual time dynamics not for decomposing scalar values but for decomposing matrices. The author considers a credit portfolio as a process described by a first-order heterogeneous Markov chain. Starting from this premise, the author uses vintage analysis and the theorem of strong convergence of modified fixed-point algorithms to arrive at transition matrix decomposition. This method makes highly accurate forecasts of credit portfolios possible. Reserves can be estimated with excellent precision and relevant values for stress-testing obtained.

In his later article The Theory and practice of Retail Credit the author considers some successful practical applications of his methods described in the article “Credit portfolios behavior modelling and stress-test”.