How quickly do savings run-off in times of liquidity stress?

The answer is: very quickly.

For the liquidity risk management of the bank it is important to include client behavior in the liquidity stress tests. Interestingly, a new BIS paper on liquidity stress testing presents a number of case studies on liquidity stress testing, reflecting the stressed liquidity circumstances of banks across the globe, some small banks with local impact (Northern Rock), but also international banks with systemic impact (Lehman Brothers).

The implicit message is: for liquidity stress testing it is important to study client behavior during severe liquidity stress rather than normal behavioral during going concern. The paper states on the relevance of the nine banks that are presented in the case studies:

The aim is to gain a better understanding of the key drivers of liquidity stress and to identify issues that are important for sound liquidity stress-testing design. These case studies generate a number of insights that can be used to better evaluate banks’ own internal liquidity stress tests or applied by supervisors in crafting horizontal liquidity stress tests.

About the run-off of savings the BIS paper concludes:

For commercial banks, deposit run-off was an important source of funding stress. Unsurprisingly, the insurance status of deposits was critical to deposit run-off rates.

A case study: SNS Bank before nationalization

In the Netherlands SNS Bank has been nationalized on February 1, 2013, after a substantial run-off of savings.

The SNS case can serve as a case study for liquidity stress testing for other banks.

When we want to model the run-off, we need to have some data points. For SNS we have two. In the 2012 annual report (published in July 2013), the total savings volume is reported as EUR 32.8 bln. We also know that from January 16, 2013 until January 31, 2013, EUR 2.5 bln of savings ran off.

That means that, supposing that the run-off started on January 16, 2013, in 2 weeks (10 business days) 7.6% of the savings volume ran off.

We can regard this as a small dataset containing two data points: (day 1, 100) and (day 10, 92.4).

Are two data points sufficient for building a model?

A 3-parameter model for savings run-off during liquidity stress

For the savings volume we can use the following formula: S(t) = alpha * exp(beta * t) + gamma.

This equation has three unknowns. When we only have 2 data points, we have one ‘free’ parameter.

With the help of some algebra, we can find values for alpha and gamma, and use beta as a parameter that determines the steepness of the decline in savings volume. The fit using beta values ranging from -0.1 to -1.0 is shown below:

7.6% Decline of a savings volume of 100 during 10 business days

7.6% Decline of a savings volume of 100 during 10 business days

Several liquidity stress tests can be conducted with the help of the lines depicted in the graph. For a sudden decline in savings volume that rapidly stabilizes at a lower volume, one of the lower lines can be used. For a liquidity stress test that continues beyond the 10 day period with fairly stable net deductions per day, one of the higher curves will be applied.

Core vs non-core and insured vs non-insured deposits

The distinction between core and non-core deposits has not been adopted. According to the BIS paper, this distinction is less relevant in times of liquidity stress:

Institutions’ definitions of “core” deposits proved to have little bearing on actual deposit run-off.

In this analysis we did not make a distinction between insured and non-insured deposits. However, as stated above, this distinction is relevant. The LCR uses a 30-day run-off rate for insured (‘stable’) deposits equal to 3%, while less stable deposits run-off at a rate of 10%.

More general than LCR

In fact, we can show that our stress test is a more general approach to the LCR stress test for less stable deposits. When using 21 business days (= 1 month = 30 calendar days), and beta values in the range of -0.1 and -0.2, we can show that a 30 day run-off equal to 10% can be reached during the LCR horizon of one month (21 business days).

7.6% Decline of a savings volume of 100 during 10 business days and 10% run-off during LCR horizon

7.6% Decline of a savings volume of 100 during 10 business days and 10% run-off during LCR horizon

In this way, one of the key messages of the BIS paper can be implemented:

An example of the added value of stress testing beyond reliance on a single metric can be found in the LCR’s 30-day horizon, which does not preclude intra-30-day timing mismatches. In a stress test, shorter and longer horizons can be explored to assess whether a bank’s outcomes are sensitive to this issue.

The science and art of liquidity case studies

Case studies will continue to be relevant for liquidity stress testing. Stretching going concern standard deviations will lead us nowhere. Liquidity stress testing calls for analysis of behavior during extraordinary situations.

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Over Folpmers
Financial Risk Management consultant, manager van een FRM consulting department, bijzonder hoogleraar FRM

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