Using Financial Soundness Indicators to Assess Financial Stability
Presentation by Deputy Central Bank Governor Jarle Bergo
at an IMF Conference, Tuesday, September 17, 2002
The paper by Craig and Sundararajan
provides an excellent road map on how to work on assessing financial stability
by using FSIs, alone or in combination with other indicators or methods of
economic and financial analysis. It is a very important contribution of value
both to central banks that have just embarked upon macro-prudential analyses as
well as to those that have been working in this area for some years.
The authors give us a clear understanding
of the limitations of relying on FSIs alone and provide a thorough and highly
relevant discussion on how to combine different sources of information to
enhance financial sector surveillance. I appreciate the way the authors
carefully point out possible pitfalls of the different approaches and their
suggestions of tests and extensions of the analysis to overcome shortcomings.
This greatly adds to the paper's usefulness as a manual for central banks and
others charged with promoting financial stability. I found little to object to
in the paper. That being the case, and since the paper concentrates on giving a
general overview, probably the best I can do is to share with you some of the
practical experiences we at the Bank of Norway have gathered over the years in
our efforts to assess financial stability.
The particularities of the Norwegian
experience are perhaps of limited interest but I think there are elements in our
general approach and methodology that can be of value for other practitioners.
You will see that we have followed the
stages outlined in the paper, starting with simple FSIs for the financial
sector, later complementing them with non-financial FSIs and then on to linking
the FSIs to macroeconomic models to make forecasts and perform stress tests. I
will give my comments on specific points in the paper as I go through our
experience.
The Bank of Norway is a non-supervisory
central bank, but has responsibility for the payments system and is explicitly
charged with promoting a sound and efficient financial system.
Norway experienced a serious banking crises in the early
1990s. As a consequence of that we started work on early warning FSIs for the
banking sector.
The analysis compared developments in the
banks that needed financial support during these years with those that never
became problem banks. To ensure coverage of the most important aspects, it
followed the CAMEL (Capital Adequacy, Asset Quality, Management,
Earnings, Liquidity) system of banking supervision, the same
framework that the IMF has applied in its proposal for a Core Set . The result
of this study showed that it was not necessary to have a very extensive set of
banking sector indicators. Actually the study suggested just four banking
sector indicators for early warning purposes. Apparently a model with a small
set of indicators would provide the same information as a larger set of
indicators.
One can only wonder if the crises might
have been better contained had these indicators been used and correctly
interpreted in the years leading up to the crises!
However, we gradually came to the
conclusion that a set of FSIs for the banking sector only was too narrow. Even
if problems showed up clearly in the simple FSIs we wanted to know when
potential problems were mounting before they showed up in the banks' accounts.
As pointed out in the paper, FSIs for the non-financial sector can help
detecting weaknesses in the financial sector at an early stage. So in 1995 we
issued our first semi-annual Financial Stability Report where a number of
Encouraged set indicators for the non-financial sector was included.
Our experience indicates that it is
especially important to evaluate the effect of macroeconomic conditions on the
debt-servicing capacity of households and enterprises, and thereby on the
credit risk of banks. We have put major emphasis on assessing the potential
for losses on loans to households and enterprises. Of particular concern in
this regard is the enterprise sector, where about 85 percent of bank losses in
the banking crisis of the early 1990s occurred. But it would be a mistake to
neglect household behaviour and their debt burden in evaluating the credit risk
confronting banks. The assessment of the household sector's robustness and
possible spill-over effects to the enterprise sector in the event of a major
macroeconomic shock is very important. I will come back to this a little later.
Another point underlined in the paper and
where our experience supports the conclusion is the emphasis on collecting and
analysing disaggregated data, both for the household and enterprise sector. In
our view, this is of considerable importance in order to comprehend more fully
how these sectors are affected by macroeconomic shocks.
Just to give you an example: Norwegian
households' aggregate debt burden compared with the period just before the
banking crisis is still relatively low. When we look at disaggregated data, we
see that the high debt burden at that time was largely due to high debt to
income ratio of the 10 percent of households with the highest income. This can
to a large extent be attributed to the tax system at that time with high
deductions for interest rate payments for the high-income households. The tax
system has later been changed. The majority of households, however, actually
have a historically high debt burden today, and this could cause problems for
the financial sector in the event of a macroeconomic shock.
Chart 1: Debt in relation to household disposable income
As part of our Financial Stability report
exercise we survey on a regular basis some 30-40 FSIs. However, we have more
and more adopted a model-based approach to assessing financial stability. By
building the FSIs into our macro-economic models, we can make forecasts and do
stress tests. Let me give you a few examples of how we work in this area.
To help assess the risks associated with
loans to the enterprise sector, the Bank utilizes data from the annual accounts
of all Norwegian limited companies. (Admittedly, we are fortunate in Norway to
benefit from the availability of extensive and
high quality data.) This has enabled us to develop a credit risk model
estimated using data for these companies for the period 1990-1996. The model
predicts individual bankruptcy probabilities as a function of age, size and
industry characteristics, in addition to account variables like corporate
earnings, liquidity and financial strength. The reduced form of the motel is
set out in the chart and the signs of the parameters indicated.
Chart 2: Bankruptcy prediction model
I will not go into details on the model
specification but refer to the references in the handout.
Since the model generates individual
probability estimates, it can be used in a number of areas related to credit
risk analysis. Multiplying the debt of individual enterprises by their
bankruptcy probability and adding them up provides an estimate of
"risk-weighted debt". This variable may be considered an indicator of expected
loan losses, given no collateral security. As the chart indicates, the
developments in long-term debt and risk-weighted long-term debt have been
different the last decade. Even though the debt level is now considerably
higher than before the banking crisis, the associated risk predicted by the
model is lower.
Chart 3: Long-term debt and risk-weighted long-term debt
As pointed out in the paper, a further
enhancement of the financial stability analysis is to introduce stress testing.
In order to assess risks both to the household and enterprise sectors, the Bank
also uses its own macroeconometric model RIMINI and the macroeconomic forecasts produced for the Bank's Inflation Report
to give forecasts of FSI, and to carry out stress tests. Stress tests can
indicate how exposed financial institutions are to macroeconomic changes, and
are becoming an important tool for central banks in their surveillance of
financial stability. In our latest Financial Stability Report, we estimated
econometric models for financial institutions' losses on loans both to the
household and enterprise sector and incorporated these into our main
econometric forecasts model.
Chart 4: Model for financial institutions' losses on enterprise sector borrowing.
Chart 5: Financial institutions' recorded and estimated losses
The model explains actual losses fairly
well and also has good out-of-sample properties.
Chart 6: Model for financial institutions' losses on loans to households
By doing this we were able to produce
estimates of the effects on financial institutions' loan losses as a result of
different shocks to the real economy. In the first alternative scenario, house
prices gradually fall by about 25 percent in relation to the baseline scenario
up to 2004. In the second alternative scenario, wage growth is 2 percentage
points higher than in the baseline scenario in 2002. To counteract the effect
this would have on inflation, the interest rate is assumed to be raised by 2
percentage points compared to the baseline scenario as from 2002.
Chart 7: Financial institutions' losses on loan to households
Chart 8: Financial institutions' losses on loan to enterprises
As you will see from the charts, the losses
on loans to households are guite small even when the household sector is
subject to a severe shock. However, the adjustment in the household sector hits
the enterprise sector hard through reduced demand and weakened profitability.
In addition we have assumed a reduction in the value of collateral in time with
the fall in housing prices.
Even though our work on stress testing is
at an early state, I fully agree with Craig and Sundararajan that such tests
are a valuable addition to the surveillance of FSIs. The paper could have
discussed in somewhat more detail, though, the limitations that are intrinsically
linked to stress tests.
As pointed out in the paper, the payment
system can be a source of large short-term credit exposures and liquidity
risks. The risk of loss arises if a participant is unable to meet obligations
and payment settlement is delayed or rejected. If a bank has large exposures to
other banks, a delay or cancellation of settlement can reduce the bank's
liquidity, thus preventing it from meeting its own obligations. In this way,
liquidity problems can spread through the payment system and at worst threaten
financial stability. In view of this central banks, other authorities and the
industry itself try to construct and regulate the payments system to reduce
risk to an acceptable level. But how do you check if you have succeeded?
If you have access to data on the
individual bank's position vis-à-vis other banks on a continuous basis, you can
check the robustness of the payment system. Norges Bank has analysed settlement
data from a 10-day period in 2000 and looked at the liquidity shortfall
connected with the failure of one counterparty to meet his obligations. The
potential liquidity shortfall is compared with the banks' available funds in
Bank of Norway Settlement system to determine the likelihood of problems
connected with the failure to pay. The analysis was published in our Financial
Stability Report no. 1 for 2001. In the Norwegian case, it indicates that banks
seldom have large exposures to several counterparties in the same netting or
large exposures to the same counterparty in several nettings.
Why do we do all this? Why do we devote
considerable resources to this exercise? Because, like Mr. Ferguson remarked
earlier this morning, financial stability is a fundamental objective of a
central bank. The objectives of monetary stability (low and stable inflation)
and financial stability are interdependent. High and variable inflation can
threaten financial stability. Financial instability would reduce the
effectiveness of monetary policy. At times, however, there could be a trade off
between monetary and financial stability.
I think it is fair to say that financial
stability is included in our loss function, but since a financial crisis
hopefully is a low probability event, it could be regarded as a constraint of
monetary policy that at most times would be non-binding.
How do our Financial Stability reports and
other studies and analysis contribute to financial stability? First, it
improves the authorities' awareness and insight in financial sector
developments. We present and discuss our reports in meetings with the
Supervisory authority and the Ministry of Finance. We also write a formal
letter to the Ministry summing up the conclusions of the forthcoming Financial
Stability report. We put a lot of effort into informing the financial institutions
of our assessments. We are transparent with regard to our analytical tools and
invite others to employ them. (Both the Supervisory authority and individual
banks have used our bankruptcy models to assess the quality of the portfolio in
individual banks).
Establishing a dialogue with the financial
services industry on factors that may create imbalances in the financial system
well in advance of any problems is of substantial importance. Our Financial
Stability Reports give us a suitable framework for this dialogue.
Charts in pdf-format