Download application of the countercyclical capital buffer in lithuania

TEMINIŲ STRAIPSNIŲ SERIJA
No 5 / 2015
TRANSLATION
APPLICATION OF THE COUNTERCYCLICAL CAPITAL BUFFER IN
LITHUANIA
© Bank of Lithuania, 2015
Reproduction for educational and non-commercial purposes is permitted provided that the source is
acknowledged.
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Abbreviations
Basel Committee on Banking Supervision
countercyclical capital buffer
credit default swap
Capital Requirements Directive IV
Capital Requirements Regulation
debt-service-to-income ratio
European Banking Authority
European Central Bank
European System of Accounts 2010
European Systemic Risk Board
European Union
gross domestic product
Hodrick-Prescott (filter)
International Monetary Fund
loan-to-value ratio
monetary financial institutions
real estate
risk weights
risk weighted assets
2
Application of the Countercyclical Capital Buffer In Lithuania
BCBS
CCB
CDS
CRD IV
CRR
DSTI
EBA
ECB
ESA 2010
ESRB
EU
GDP
HP
IMF
LTV
MFIs
RE
RW
RWA
CONTENTS
3
Summary ........................................................................................................................................................................ 4
1.
2.
3.
Purpose of the countercyclical capital buffer ......................................................................................................... 5
1.1.
New capital requirements .............................................................................................................................. 5
1.2.
Purpose of the countercyclical capital buffer ................................................................................................. 6
1.3.
Reciprocity of the CCB rate ........................................................................................................................... 7
Impact of the countercyclical capital buffer on the economy ................................................................................. 7
2.1.
Impact transmission channels ....................................................................................................................... 7
2.2.
Impact on the economy ................................................................................................................................. 8
2.3.
Undesirable consequences of the application ............................................................................................... 9
Setting of the countercyclical capital buffer rate .................................................................................................... 9
3.1.
Setting of the CCB rate in periods of increasing systemic risk .................................................................... 10
3.2.
Reduction or release of the CCB rate .......................................................................................................... 16
3.3.
Practical implementation of decisions and their publication......................................................................... 17
References .................................................................................................................................................................. 19
Application of the Countercyclical Capital Buffer In Lithuania
Annex. Information on data used to compute the main and complementary indicators ................................................ 21
Summary
4
The main purpose of this paper is to inform market participants and the public about the principles for the use of a
countercyclical capital buffer — one of the instruments of macroprudential policy — in Lithuania. The application of the
CCB is regulated by the Rules for Capital Buffer Formation, approved by the Board of the Bank of Lithuania on 9 April
2015. The Bank of Lithuania takes a decision on setting the CCB rate on a quarterly basis. The decision and background
information for it are published on the Bank of Lithuania’s website.
A number of changes in financial sector’s supervisory and regulatory system have been implemented in response
to the implications of the global financial crisis and in attempt to prevent the build-up of imbalances within the financial
1
sector. At a global level, a huge importance is given to broad-based standards Basel III as one of the most significant
regulatory initiatives, as well as to active implementation of macroprudential policy. Along with other requirements, the
new Basel III framework provides for tighter requirements for the level and quality of bank capital. Most of the
2
components of Basel III to be applied at EU level are included in the CRD IV, which was approved on 26 June 2013.
The provisions of this Directive have been transposed into the national legal systems of EU Member States. Apart from
other supervisory requirements, the CRD IV regulates the application of the countercyclical capital buffer.
In Lithuania, the CCB is to be increased if credit growth becomes unsustainable, in order to build up a sufficient
capital buffer for covering potential bank losses during a crisis period, thereby reducing pro-cyclicality in credit supply and
strengthening the financial system’s resilience. The CCB rate above 0 per cent will be set in the event of excessive
growth of the domestic credit market, which could lead to the formation of systemic risks in the financial sector. The CCB
is an additional requirement which must be met by using banks’ Common Equity Tier 1 capital. The decision on setting
the CCB rate will be taken based on a comprehensive analysis of the credit and RE market situation, as well as various
complementary quantitative and qualitative information of relevance. In the case of Lithuania, two main and four
complementary indicators have been selected after carrying out an analysis. The CCB reference rates are computed by
using the main indicators. The credit-to-GDP gap has been chosen as the main indicator because of its credibility in
predicting financial crises. Reference rates are set above 0 per cent where the credit-to-GDP ratio is significantly higher
than its long-term development trend. Complementary indicators show sustainability within the most relevant financial
and economic areas for the use of the CCB, and provide additional information concerning the soundness of the CCB
rates. The final decision regarding the CCB rate for Lithuania will be based primarily on the main and complementary
indicators, but expert assessment is to be taken into account as well. In view of the dynamic development of the
macroprudential policy, there may be changes in the list of the main and complementary indicators used to set the CCB
rates and the CCB rate setting methodology in the future.
1
Basel Committee on Banking Supervision (2010; rev. 2011), “Basel III: A global regulatory framework for more resilient banks and banking systems”:
http://www.bis.org/publ/bcbs189.pdf.
2
Directive 2013/36/EU of the European Parliament and of the Council of 26 June 2013 on access of the activity of credit institutions and the prudential
supervision of credit institutions and investment firms, amending Directive 2002/87/EU and repealing Directives 2006/48/EU and 2006/49/EU.
3
Decision of the Board of the Bank of Lithuania of 12 March 2015 on the approval of the macroprudential policy strategy (No. 03-31).
Application of the Countercyclical Capital Buffer In Lithuania
Under the Law of the Republic of Lithuania on the Bank of Lithuania, as of 24 September 2014, the Bank of
Lithuania must implement macroprudential policy with the aim of contributing to the stability of the financial system,
including strengthening of the financial system resilience and preventing the build-up of systemic risks, thereby ensuring
sustainable contribution of the financial sector to economic growth. The Law instructs the Bank of Lithuania to
continuously monitor and evaluate threats arising for the domestic financial system and apply necessary macroprudential
policy measures. The Macroprudential Policy Strategy, approved by the Board of the Bank of Lithuania on 12 March
3
2015 , sets out the interim goals of macroprudential policy, its instruments, the procedures for decision-making and
making decisions public, as well as the principles for cooperation with other institutions. One of the most significant
interim goals of the macroprudential policy pursued by the Bank of Lithuania is limiting excessive credit growth and
leverage, and taking efforts to avoid them. The CCB is one of the instruments to achieve this goal.
1. Purpose of the countercyclical capital buffer
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1.1. New capital requirements
The global financial crisis has shown that excessive credit growth can lead to major losses and failures in the
functioning of the financial sector, as well as have a negative impact on the development of the real economy. Economic
recessions are considerably deeper and recoveries slower when the preceding growth is related to strong credit growth
4
(Jorda et al., 2013 ). At the same time it should be noted that too strong credit growth tends to be followed by economic
5
downturns. As suggested by Schularick and Taylor (2012) , excessive credit growth is a sufficiently good indicator for
forecasting financial crises.
Larger of the two rates is applied
(exceptions are available)
Chart 1 Changes in banks’ capital structure after implementing the CRD IV
Systemic risk buffer, 0–5*%
Global systemically important and other
systemically important institutions (G-SII and
O-SII) buffer, 1–3.5% and 0–2% respectively
Countercyclical capital buffer,
0–2.5%*
Capital conservation buffer,
Minimum requirement — 8%
2.5%
Tier 2 capital, 2%
Additional Tier 1 capital, 1.5%
Common Equity Tier 1 capital (CET1),
4.5%
The cycle of a build-up, fast growth of systemic risk and its consequences could also be observed in
Lithuania in 2004 to 2010. This cycle, first, was characterised by an extensive development of the credit market (the
highest annual credit growth rate recorded was 50%) and particularly strong real estate price increases (the annual price
increase was 20–70% in some sectors). Overoptimistic future expectations, aggressive development of the banking
sector, competition among banks, low real interest rates, and the prevailing favourable situation in the EU and global
financial markets were the main drivers of the growth rate of RE prices and bank loan portfolio being a few times higher
than that of the domestic economy. The downturn in the economic and financial cycle, which started at the end of 2008,
coincided with the most severe global financial crisis in the recent 80 years. All this led to sharp changes in the
development of the economic and financial sectors: domestic real estate prices fell by half; the GDP fell by 15 per cent in
2009, while unemployment rose to 18 per cent. Most likely, the downturn would have been less disastrous if
macroprudential measures, limiting systemic risks and credit market imbalances during the upswing of the financial
cycle, would have been applied both in Lithuania and other countries.
Before 2008, the regulation of the financial sector was based mainly on the supervision of individual financial
institutions and market discipline, while insufficient attention was given to the analysis of systemic imbalances and use of
various measures aimed at limiting systemic risks. Significant imbalances may build up in the domestic economy, which
4
Jorda, Ò., Schularick, M., Taylor, A. M. (2013). “When Credit Bites Back”, Journal of Money, Credit and Banking 45, p. 3–28.
Schularick, M., Taylor A. M. (2012). “Credit Booms Gone Bust: Monetary Policy, Leverage Cycles, and Financial Crises, 1870–2008”, American Economic
Review 102(2), p. 1029–1061.
5
Application of the Countercyclical Capital Buffer In Lithuania
Note: *Member States may set higher requirements.
will be difficult to notice by only analysing individual financial institutions. For instance, the true extent of loans issued by
the financial sector for the purchase of real estate only shows up when analysing the aggregate data, while the
assessment of individual financial institutions may not reveal the exceptional crediting of the real estate sector by these
financial institutions. Thus, in the assessment of the stability of individual financial institutions, their potential losses can
be underestimated should the adjustment of real estate prices occur. In view of this, significant changes have been made
in the regulation of the financial sector following the recent financial crisis: founding of institutions responsible for the
supervision of the entire financial system started. A number of initiatives at EU and global level have been launched to
create an effective system for the use of macroprudential measures. Basel III, the new capital formation standards, was
one of such initiatives. These standards have been approved at EU level; many of their proposals have been transposed
into the CRR and CRD IV, while the provisions of the latter two have been transposed into the national legal systems of
EU Member States.
6
The CRD IV, which is to be implemented at EU level, provides for new capital buffers: systemic risk buffer,
systemically important institutions’ buffer, and countercyclical capital buffer, that change basically the capital structure of
the banking sector (see Chart 1). These new capital buffers are applied taking into account country-specific (and their
financial institutions-specific) factors; therefore, countries will have a possibility to reduce systemic risk at a national level.
6
All of the new capital measures can be divided into two groups : those for the management of structural risk (e.g.,
systemic risk buffer, systemically important institutions’ buffers) and those for the management of cyclical risks (e.g.,
countercyclical capital buffers). The CCB is the key measure for managing cyclical risks within the financial sector. This
buffer is used to mitigate the impact of the financial cycle on the entire economy. To ensure the efficiency of the CCB
requirement within the internationally integrated financial systems, this measure is to be applied to banks’ exposures in
individual countries rather than to individual institutions. This will ensure the CCB efficiency as all credit institutions on the
territory of the lending country, irrespective of their legal status, will be affected.
1.2. Purpose of the countercyclical capital buffer
Chart 2. Use of the CCB: an illustrative example
Chart 3. Impact of the CCB: an illustrative example
Capital adequacy requirement
CCB accrual
CCB reduction
Total capital requirement
Financial cycle with
a buffer used
Additional
buffer
Fluctuations
Financial cycle without
a buffer used
The main purpose of using the CCB is to protect the banking system against potential losses that may be incurred
due to the pro-cyclical growth of systemic risk, thereby contributing to sustainable crediting of the real economy during a
financial cycle. To reduce the pro-cyclicality of the banking sector development considering the development of systemic
risk and credit developments, the size of this additional capital buffer may be changed. The CCB requirement is set
above 0 per cent in case of unsustainable credit development in the country (e.g., when it is significantly higher than
overall economic growth) (see Charts 2 and 3). This measure has a direct impact on the sector’s resilience as the capital
buffer would be accumulated during the period of unsustainable credit growth (see Chart 3) and would be reduced at the
time of economic recession. Moreover, this measure is important for reducing the cyclicality of the financial system. The
6
More information about the new measures under the CRD IV is available in the 2013 and 2014 Financial Stability Review of the Bank of Lithuania.
Application of the Countercyclical Capital Buffer In Lithuania
Minimum
capital
requirements
Base level
7
CCB rate may vary between 0 and 2.5 per cent or be above 2.5 per cent if necessary , and is to be set by the decision of
the Bank of Lithuania on a quarterly basis. The ability to both limit the causes of an emerging crisis (unsustainable credit
growth) and mitigate the effects of the crisis (decreasing crediting) is one of the major advantages of setting this buffer.
7
Unlike other macroprudential policy measures, the CCB is applied to domestic exposures of banks in Lithuania rather
than to individual institutions; therefore, this requirement also has a direct effect on bank branches and foreign banks
granting loans to Lithuanian residents. Individual institutions will have to calculate their institution-specific countercyclical
8
capital buffer according to the CCB rates applied in individual jurisdictions. This is particularly important, as cyclical
systemic risks mainly build up in individual countries where bank branches of other countries operate too. A possibility to
use this measure for asset exposures ensures the effectiveness of the CCB use within the financial markets of EU
Member States which are deeply integrated internationally.
1.3. Reciprocity of the CCB rate
Harmonising legal regulation in individual countries is very important as jurisdictional differences may prompt banks
to shift their operations into other jurisdictions. If the CCB rate were not recognised by other countries, banks —
operating in Lithuania as branches — would have an advantage as they, complying with the capital requirements of
another jurisdiction, will not have to comply with the CCB rate set in Lithuania. The principle of reciprocity has therefore
been enforced at EU level; its purpose is to ensure the effectiveness of the CCB applied by individual Member States
and equal competition conditions for domestic banks and foreign bank branches.
The Directive provides for immediate recognition internationally, at EU level, of the CCB rate not above 2.5 per cent.
If a Member State sets the CCB rate below 2.5 per cent, this rate will have to be applied to all exposures in that country,
including the exposures of bank branches and foreign banks. The Directive also provides that the portion of the CCB rate
exceeding 2.5 per cent can be recognised by Member States on a voluntary basis. In such a case, countries that find it
relevant (e.g., their banks have exposures in a country that sets the CCB) will have to consider the recognition of the
9
buffer portion that exceeds 2.5 per cent and publicly inform about their decision. It should be noted that the reciprocity
10
principle will only be used in full after the transition period set in the Directive (as of 31 December 2018). If Member
States provide for a shorter transition period and decide to start using the CCB before the timeline set in the Directive,
automatic recognition of the reciprocity will not be mandatory with the CCB rate below 2.5 per cent. At the same time, the
Directive provides that national competent authorities should consider recognition of the CCB rates set by Member
States which had decided on the early introduction of the CCB.
The Law on Banks of Lithuania also regulates the recognition of the CCB rates of third countries. If a third country’s
authority responsible for setting the CCB has set and published the CCB rate to be applied in the third country, the Bank
of Lithuania may set a different buffer rate to be applied to exposures in that country, if it has reasonable grounds to
believe that the CCB rate set by a respective national authority of the third country is not sufficient to protect its
institutions against the systemic risk posed by excessive credit growth in that country.
2.1. Impact transmission channels
Additional CCB requirement has a multi-directional impact on the financial sector and the credit market (CGFS, 2012;
see Chart 4). After raising the capital adequacy ratio requirement banks may either: a) increase their capital, or b) reduce
their risk-weighted assets. There are two ways to increase capital: by attracting additional capital and/or accumulating
additional capital from retained profits (thereby reducing profitability and dividend pay-outs). Additional capital of a bank
would limit systemic risk as financial institutions would be more resilient to adverse economic situations and have larger
buffers for absorbing potential credit losses. Should commercial banks decide to keep their capital unchanged, but
reduce their risk-weighted assets, this would likely lead to the reduction of credit volumes and/or tightening credit
standards.
7
If the Bank of Lithuania assessed that the CCB rate of 2.5 per cent is insufficient considering emerging risks.
Institutions have to build up an institution-specific countercyclical capital buffer that is equal to the total sum of their risk-weighted exposures multiplied by
the countercyclical capital buffer rates set in individual countries.
9
It should be noted that the need to have the CCB rate above 2.5 per cent should not be seen often. For comparison, at the time of the peak of a credit
cycle in Lithuania, when annual credit growth rate was 50 per cent, the CCB rate, calculated based on the credit-to-GDP gap, would be about 5.1 per cent.
It should be noted that these retrospective calculations are based on the assumption that crediting would have remained unchanged even with a higher
CCB rate.
10
This is provided for in Article 160 of the CRD IV.
8
Application of the Countercyclical Capital Buffer In Lithuania
2. Impact of the countercyclical capital buffer on the economy
It should be noted that an additional CCB may affect market participants’ expectations. For instance, real estate
prices may be expected to decelerate following an increase of the CCB rate. If economic agents rely on unreasonable
expectations and anticipate strong growth in real estate prices or the economy in the future, they may have incentives to
lend to non-profitable businesses or loss-bearing projects. With the change of an economic cycle, the possibilities for the
repayment of such loans would decrease significantly. For instance, if expectations regarding further fast RE price
increases are not based on fundamental factors, changed expectations may lead to a fall in RE prices. Such a situation
would reduce the profitability of RE projects and the ability of RE purchasers to repay their loans. Such excessive nonproductive lending creates the conditions for the build-up of various systemic risks (e.g., unsustainable increase in RE
prices). One of the objectives of macroprudential policy is to limit non-productive credit, thereby ensuring crediting of
productive activities.
8
Chart 4. Channels for the CCB impact transmission
Potential impact
Spillover to
non-banking
sector
↑ Cost of
borrowing
cost
↓ Dividends
Loan
repricing
Impact on credit cycle
Higher capital requirements
↓Voluntary
capital buffers
Loan market
↓ Credit
demand
↑ New
capital
↓ Assets,
primarily with
higher risk
weight
↓ Credit
supply
Asset prices
Expectation channel
↑ Loss covering
possibilities
Tighter risk
management
Higher stability of the financial system
Potential bank
response
Potential market
response
↓ Decreases
↑ Increases
2.2. Impact on the economy
As the CCB is a new instrument, the empirical research of its impact on credit and economic development is rather
limited. Hence, indirect research can be relied upon to analyse the impact of a quantitative increase of capital adequacy
ratios on bank lending. The results of such studies vary depending on the methodology and assumptions used (whether
a temporary or permanent increase of capital requirements is analysed), but many of them have revealed that tightening
of capital requirements have only a modest impact on an increase in the cost of credit and/or a decline in credit volume.
11
Based on available research (MAG, 2010) , increasing of capital requirements by 1 p.p. in the long-term leads to an
increase in bank lending interest rates of approximately 12.2 basis points and a decrease in the amount of loans of 1.47
per cent. The MAG (2010) suggests that raising the bank capital adequacy ratio by 1 p.p. will change the cost and
12
volume of credit, which, depending on the assumptions, will reduce the GDP level by 0.10 per cent in the long term.
Despite the observed negative impact of increased capital requirements on GDP growth in the short term, the
positive effect on long-term economic development as a result of the reduced impact of crises would be markedly higher
11
12
For more details, see Macroeconomic Assessment Group 2010.
The long-term is perceived as the impact after 48 quarters (median of estimates).
Application of the Countercyclical Capital Buffer In Lithuania
Source: CGFS (2012).
13
(Arregui et al., 2013). It should be noted that potential losses from financial crises can be particularly high. Various
studies suggest a correlation between a higher capital adequacy requirement and a lower probability of a bank’s
bankruptcy (and systemic risk) (see Miles et al., 2012; Sveriges Riskbank, 2011; Kragh-Sørensen, 2012). The depth of
an economic crisis also directly depends on the severity of the banking crisis and failures in credit operations. The
literature referred to (e.g., ESRB, 2014, and Aikman, D. et al., 2013) often presents the economic losses of a banking
crisis as the cumulative GDP loss compared with long-term trends of economic development observed before the crisis.
For instance, the BCBS study (2010), which presented an overview of various studies of the impact of crises, indicates
that accumulated (and discounted) losses of banking crises account for an average 63 per cent of the highest pre-crisis
GDP level. As a matter of fact, the estimate of crisis losses as presented by various authors varies a great deal because
of the different assumptions about the duration of the impact of a crisis and the size of the long-term impact of the crisis.
According to some authors (e.g., Haldane, 2010), the losses of a crisis may account for even 350 per cent of the precrisis GDP. Considering the scale of these potential losses and the relatively modest negative impact from the use of the
CCB, in the conduct of macroprudential policy, using the CCB quite actively is worthwhile even when there is uncertainty
about unbalanced current credit growth (IMF, 2013).
9
According to Jimanez et al. (2012), since, in times of an economic upturn, there are quite many substitutes for credit
(e.g., firms have a possibility to issue bonds), in times of economic growth the impact of macroprudential instruments
(CCB) may be lower than expected because lower supply of bank credit could be replaced with credit supply from other
institutions or other forms of financing could be used (e.g., debt securities). During a recession, unlike during a credit
boom, access to credit substitutes decreases along with credit availability. In that case, the easing of macroprudential
policy through the reduction of additional capital buffers may have a positive effect on crediting.
2.3. Undesirable consequences of the application
While the primary objective of the CCB is to promote sustainable development of the credit market and strengthen
the stability of the financial system, the use of this instrument may have certain undesirable consequences either. After
the CCB rate is increased, banks may attempt to reduce their risk-weighted assets or change the structure of their
lending in order to meet tighter capital requirements. To reduce the need for capital requirement, banks may tend to lend
to sectors to which lower risk weights are applied (e.g., grant loans for house purchase) and suspend lending to sectors
14
to which higher risk weights are applied (e.g., to lend to businesses ). Moreover, banks may attempt to refocus their
operations from regular lending to areas that are less regulated in macroprudential terms, such as loan securitisation or
investment in non-bank financial institutions engaged in the lending activity. In case these effects were substantial, this
could cause an undesirable impact on the real economy because of lending redistribution among sectors, the shift of
risky operations or the development of shadow banking. However, Lithuania’s financial system is based on a traditional
banking model and the likelihood of these effects to be significant is low.
Furthermore, it is complicated to precisely project the start of the adjustment of accumulated imbalances, and
therefore uncertainty is to be faced when reducing the CCB. Reducing the CCB may also have certain negative
consequences. Should the CCB be reduced after a financial crisis has gained momentum, it might only modestly
contribute to credit encouragement due to persistent negative expectations. On the other hand, there is the risk of
reducing the CCB too early and thus prompting the growth of imbalances, for example, decelerating price growth may
accelerate again.
3. Setting of the countercyclical capital buffer rate
The Bank of Lithuania sets the CCB rate based on a comprehensive analysis of the situation in the financial and real
estate sectors, for which various quantitative and qualitative data are used. By setting the CCB rate in Lithuania on a
13
These studies provide only an approximate estimate of the impact of the CCB as actual data on the application of the CCB are not available; they focus
on changes in capital requirements for institutions, whereas the CCB rate is applied to the whole banking system’s exposures in a certain jurisdiction. In
addition, some of the studies (Aiyar et al., 2012; Francis et al., 2012) look into a permanent increase of capital requirements, in contrast to the cyclical one
in case of the CCB.
14
For instance, in Switzerland, the CCB is linked to sector-specific capital reserves and is only applied to housing loans, thereby attempting to avoid
undesirable changes in the composition of bank loan portfolio.
Application of the Countercyclical Capital Buffer In Lithuania
The Bank of Lithuania is prepared to monitor and assess the response of financial institutions to the additional capital
requirements that are set and to take extra measures to reduce undesirable consequences if necessary. It should be
noted that the slowdown of lending due to the use of the CCB cannot be viewed only as a negative (undesirable) effect.
This impact should be compared to benefits arising from the lower likelihood of a financial crisis and lower amplitude of
the financial cycle.
quarterly basis, the Bank of Lithuania follows the ESRB guidelines and recommendations on the application of the
15
CCB. The CCB rate cannot be below 0 and is calibrated in steps of 0.25 p.p. or multiples of these steps. The CCB
increase and reduction phases differ. The CCB would be increased in order to accumulate capital during the times of
unsustainable credit growth. The CCB increase would be based on the principle of “guided discretion”, i.e., on the pre-set
indicators and the CCB reference rates, as well as the analysis of all available and relevant information, while the
decision will be taken by the Board of the Bank of Lithuania. The CCB rate would be reduced or fully released when the
decreasing overall risk in the financial sector or heightened financial market tensions signal an upcoming downswing in
the financial cycle. Since this phase is related to uncertainty, all information available and expert assessment is to be
used.
10
3.1. Setting of the CCB rate in periods of increasing systemic risk
The purpose of increasing the countercyclical capital buffer is to accumulate an additional capital buffer in times of
unsustainable credit growth in the country, which may lead to systemic losses for the banking sector in the future. It is
important to determine accumulating imbalances in advance and gradually increase the CCB in order to accumulate a
sufficient capital buffer to be used when a crisis hits. In the case of Lithuania, the CCB is set based on indicators that
were the most useful in providing early signals of the build-up of imbalances during the recent crisis. These indicators are
discussed in more detail below. The selection of indicators used for setting the CCB has been analysed in more detail in
an occasional paper of the Bank of Lithuania (Valinskytė, 2015), and the data used to calculate them are explained in the
annex to this paper.
3.1.1. Setting of the CCB reference rate
The Bank of Lithuania calculates the CCB reference rate on a quarterly basis. The calculation of this rate is based on
the deviation of the credit-to-GDP ratio from its long-term trend, taking into account, inter alia, credit growth in the country
and existing ESRB recommendations. In Lithuania, two CCB reference rates are computed, using the selected main
indicators. The size of the CCB reference rates is estimated based on the size of the credit-to-GDP gap from its longterm trend. These two CCB reference rates differ in that the long-term trend of the credit-to-GDP ratio and the gap from it
are computed in two ways — in accordance with the standardised method proposed by the BCBS and a method adapted
to the Lithuanian data (by using a forecast of the credit-to-GDP ratio).
Chart 1. Long-term trend of the credit-to-GDP ratio and the gap,
computed in accordance to the standardised Basel method
(Q1 2000–Q4 2014)
Percentage points
60
Percentage
100
80
45
60
30
40
15
20
0
0
–20
2000
2002
2004
2006
2008
2010
2012
80
30
60
20
40
10
–15
20
0
–30
0
2014
Crisis period
Credit-to-GDP gap (right-hand scale)
Credit-to-GDP ratio
Average ratio (Q4 1995–Q4 2014)
Long-term trend of credit-to-GDP ratio (based on the standardised Basel method)
Sources: Statistics Lithuania and Bank of Lithuania calculations.
Note: The long-term trend is computed by applying a one-sided HP filter with a
smoothing parameter set to 400,000.
15
It is provided for in Article 136(3) of the CRD IV.
Percentage points
40
Percentage
100
–10
2000
2002
2004
2006
2008
2010
2012
2014
Crisis period
Credit-to-GDP gap (right-hand scale)
Credit-to-GDP ratio
Average ratio (Q4 1995–Q4 2014)
Long-term trend of credit-to-GDP ratio (computed using a forecast)
Sources: Statistics Lithuania and Bank of Lithuania calculations.
Note: The long-term trend is computed by applying a one-sided HP filter with a
smoothing parameter set to 400,000; before applying the filter, the credit-toGDP ratio is modelled for the next five-year period using a four-quarter
weighted average.
Application of the Countercyclical Capital Buffer In Lithuania
(Q1 2000–Q4 2014)
Chart 2. Long-term trend of the credit-to-GDP ratio and the
gap, computed using a forecast
When calculating the value of the CCB reference rate, the credit-to-GDP gap (the difference between the actual
indicator and its estimated long-term trend) would be calculated first, and the derived value of the gap would be related to
the CCB rate. This rate may vary due to different credit definitions or different methods used to calculate the long-term
trend (e.g., for accuracy purposes, the trend used is augmented with a simple forecast or the long-term trend is
calculated by using different methods).
11
Reference rates are set based on the broad credit definition, when all sectors (not only MFIs) are considered
creditors, including non-residents. This is done in order to take into account the incentives of credit market participants to
avoid the impact of the instrument by diverting lending via other (non-banking) institutions. The CCB reference rates are
set above 0 if the total credit-to-GDP gap is higher than 2 p.p., and the CCB rate is set at 2.5 per cent when the gap
16
exceeds 10 p.p. When the gap interval is 2–10 p.p. , the CCB reference rate will vary linearly. Countries may set
17
thresholds that suit their credit cycle the best. The evaluation of Lithuania’s financial cycle suggests that the thresholds
proposed by the BCBS and the CRD IV (when the lower threshold is 2 and the higher one is 10 p.p.) would have been
adequate during the crisis in Lithuania in 2008, i.e., additional capital could have been accumulated a few years before
the crisis started.
The credit-to-GDP gap as recommended by the ESRB and BCBS has been chosen as the main indicator for
calculation of the CCB reference rate due to several reasons. Firstly, the results of many studies (Drehmann et al., 2010;
Drehmann and Juselius, 2013; Behn et al., 2013) have shown that it is one of the most reliable indicators for early
prediction of an upcoming financial crisis caused by excessive credit growth. Secondly, this indicator is directly related
with the CCB goals, as it may be used to determine whether the growth of credit to the real economy is consistent with
the development of the real economy (when calculating the credit-to-GDP ratio, the impact of credit demand, which is
driven by general economic growth, is eliminated). The ratio’s long-term trend may be increasing due to the deepening of
the financial market, for instance, more efficient financial intermediation. If the indicator significantly exceeds its long-term
trend, it is the first signal of credit growth being too strong and potentially reflecting the build-up of imbalances.
The issue related to uncertainty may be reduced by using forecast-augmented indicators, where even a simple
forecast reduce estimate errors (Gerdrup et al., 2013). Comparison of various methods for the estimation of the longterm trend (without a forecast, and augmented with various simple forecasts) has showed that the most appropriate way
to compute the gap for Lithuania’s data would be to augment the credit-to-GDP ratio with forecasts calculated as a
19
weighted average of observations for the last four quarters (see Chart 6), these conclusions are in line with the results
of studies by other authors (e.g., Gerdrup et al., 2013). This is the method used to compute the second CCB reference
rate that is based on the other main indicator. It differs from the method proposed by the BCBS as the HP filter is applied
to the ratio augmented by its forecast for the next five-year period. The forecast has been computed automatically by
20
extending the indicator with a four-quarter weighted average. Such a procedure helps to reduce errors in the long-trend
estimate at the endpoint of the data series.
16
AKR t = min {2,5%;
2,5%
H-L
*(max{gapt ; L}– L)} ; where H is the gap threshold that corresponds to the maximum CCB rate (2.5%); L is the gap threshold which
has to be reached for starting to accumulate the CCB; gapt is the gap between the credit-to-GDP ratio and its long-term trend at period t.
17
For more information, see Valinskytė, 2015.
18
According to Van Norden (2011), Drehmann et al. (2011), the gap calculated by applying a one-sided HP filter performs as well as (even possibly
outperforms) the gap considered to be actual one and calculated by applying a two-sided HP filter. Empiric research suggests that it is true, for instance, for
data in UK and Norway.
19
This conclusion is applicable to many other indicators, the gap between which and their long-term trend is computed. For more information, see an
occasional paper on the methodology for the application of the CCB (Valinskyte, 2015).
20
The forecast for the indicator over period T+p is calculated as X T+p = 0,4X T + 0,3X T-1 + 0,2X T-2 + 0,1X T-3, 𝑝 = 1, … ,20, where T is the number of actual
observations, which are available in real time. Applied weights (0,4, 0,3, 0,2, and 0,1) correspond to the weights of the preceding four actual observations at
the end of the forecast period when the forecast is computed as a moving four-quarter average with an undesirable fluctuation in the projected series at the
beginning of the projection period avoided.
Application of the Countercyclical Capital Buffer In Lithuania
The main indicators are defined as the gap from the long-term trend. A long-term trend is estimated by applying a
one-sided HP filter with the smoothing parameter λ = 400,000 (according to the methodology presented in the BCBS,
2010, and ESRB, 2014, see Chart 5). While this method is good enough for Lithuania’s historic data, higher uncertainty
is faced due to the HP filter specifics when estimating the real-time gap. Such tendency is particularly pronounced during
the change of a cycle phase. Although the uncertainty surrounding the long-term trend estimate does not necessarily
18
influence the usefulness of the credit-to-GDP gap as a leading indicator, estimates of the long-term trend and the gap
should be as precise as possible to ensure credible macroprudential policy decisions. This issue (for more information,
see Valinskytė, 2015) has been discussed in literature analysing the assessment of long-term trends, while adjusted
(e.g., augmented with a forecast) long-term trends are used also by other countries (such as Norway).
3.1.2. The use of complementary indicators to determine the validity of the CCB reference rate
12
Due to the properties of the above-discussed credit-to-GDP ratio and its gap from the long-term trend as well as
scarce international practice in the use of macroprudential instruments it would not be wise to rely on one indicator.
Therefore, to increase the efficiency in setting the CCB, it is recommended to choose more indicators that would be
closely monitored and would help adopt fully justified decisions on setting the CCB rate and its value. Complementary
indicators would help assess the level, development and prevalence of financial imbalances, and estimate the adequate
rate of CCB more precisely.
In the case of Lithuania, apart from the main indicators showing the sustainability of borrowing by the non-financial
sector, four complementary indicators have been chosen, which inform about the situation in areas important for the
application of the CCB, such as the RE market, and bank financing and liquidity:
1.
2.
3.
4.
MFI loans-to-GDP gap;
House price-to-household income gap;
MFI loan-to-deposit ratio;
Current account deficit-to-GDP ratio.
Other countries that have published information about the CCB application principles, such as Norway and
Switzerland, also use complementary indicators similar to those presented above. The ESRB recommends to monitor
them; moreover, in the case of Lithuania (Valinskytė, 2015), these indicators were among the most useful in revealing
the build-up of financial imbalances in 2002–2008. It should be noted that because of the inertia of the gap indicators,
i.e., their change is slow after their development trend reverses, they are only suitable in analysing the buffer
accumulation period during a gradual build-up of systemic risk. To determine the moment when the systemic risk
materialises and when it would be appropriate to reduce the CCB rate, other, more dynamic indicators of the market
situation will be used.
The ratio of MFI loans to the private non-financial sector to GDP
Percentage points
40
Percentage
80
60
30
40
20
20
10
0
0
–20
–10
2000
2002
2004
2006
2008
2010
2012
2014
Crisis period
Loan-to-GDP gap (right-hand scale)
Loan-to-GDP ratio
Average ratio (Q4 1995–Q1 2015)
Long-term trend of loan-to-GDP ratio
Sources: Statistics Lithuania and Bank of Lithuania calculations.
Note: The long-term trend is computed by applying a one-sided HP filter with a
smoothing parameter set to 400,000; before applying the filter, the loan-to-GDP
ratio is modelled for the next five-year period using a four-quarter weighted
average.
Chart 4. Dynamics of loans to the private non-financial sector
(Q1 2000–Q1 2015)
Percentage
Percentage
100
10
80
5
60
0
40
–5
20
–10
0
–15
–20
–20
2000
2002
2004
2006
2008
2010
2012
2014
Crisis period
Credit impulse* (right-hand scale)
Annual change in nominal loan stock
Loan-to-GDP ratio
Long-term trend of loan-to-GDP ratio
Sources: Statistics Lithuania and Bank of Lithuania calculations.
Note: The long-term trend is computed by applying a one-sided HP filter with
the smoothing parameter set to 400,000; before applying the filter, the loan-toGDP ratio is modelled for the next five-year period using a four-quarter
weighted average.
* The ratio of the difference between the annual change in the loan portfolio
and the change in the loan portfolio in the preceeding year, and GDP.
The ratio of MFI loans to the private non-financial sector and GDP is a preliminary indicator for the development of
21
the credit-to-GDP ratio. This indicator is useful as it may be computed on a monthly basis and with a small lag , which
allows for more urgent response to the market situation. Credit granted by banks (or MFIs) is also referred to as the
narrow credit. At the end of the fourth quarter of 2014, this type of credit (loans) accounted for 72 per cent of the broad
credit, with the historic trends of both credit series being similar. Moreover, this indicator allows for a more precise
21
The MFI balance statistics is published on a monthly basis 28 days after the end of the current month, while the Financial Accounts of Lithuania, used to
calculate leading indicators, are published approximately 100 days after the end of a current quarter.
Application of the Countercyclical Capital Buffer In Lithuania
Chart 3. Long-term trend of the ratio of MFI loans to the private
non-financial sector and GDP, and the gap
(Q1 2000–Q1 2015)
evaluation of the impact of bank lending on credit development. It should be noted that MFIs account for the largest
share of credit in Lithuania; therefore, this indicator is reliable enough to forecast the development of the main indicators.
If GDP declines faster than credit, the credit-to-GDP ratio may increase even during an economic recession (as it was
the case in Lithuania in 2009, see Chart 7). In this case, it is important also to take into account credit growth and the
credit impulse (see Chart 8) in order to assess the overall credit market situation.
13
House price-to-household income ratio
RE market has a huge effect on the credit market. It should be noted that housing loans account for a significant
share of total bank lending, and their percentage has been growing in many industrial countries for a few recent decades
(Jorda et al., 2015). Growth in housing loans is often related to the increase in real estate prices, while the increase in
property prices may prompt credit growth (Harvey, 2010). In view of this, the RE market situation should also be
analysed when setting the CCB.
Lending to households for house purchase and developments in residential property prices are closely interrelated.
Growth of house prices which relies too heavily on borrowed funds rather than fundamental factors, such as general
economic growth, employment growth, etc., cannot be seen as sustainable. Too easy access to borrowing and
unjustified anticipation of property price increases may lead to a build-up of self-fulfilling expectations, when house
purchases are booming because of anticipated growth of house prices. Residential property is often used to secure bank
loans; therefore, it is an attractive instrument for mitigating credit risk when house prices are increasing rapidly. In such
cases, it becomes even easier to get a loan for house purchase, which creates the conditions for even faster build-up of
imbalances. Moreover, trends in property market have a significant impact on the operational stability of MFIs, as more
than half of the loan portfolio is directly related to this market. It is therefore important to assess trends within the property
market and their impact on the loans market.
The house price-to-household income ratio sums up information on the sustainability of real estate prices. The
interaction between credit and house prices may prompt the build-up of imbalances in the credit market and real
economy. With property prices going up, a possibility arises to get a larger loan because of the increasing value of the
collateral. When RE prices go down, banks suffer both direct losses due to a decline in the collateral value and indirect
losses due to unemployment growth, lower demand, and a slump in economic activity. To compute this complementary
22
indicator, only residential property prices are taken into account, as data on commercial RE are less reliable. The ratio
gap from its long-term trend is also frequently mentioned as a useful early warning indicator of a crisis (ESRB, 2014;
Behn et al., 2013; see Chart 9).
Chart 5. Long-term trend of the house price-to-household
income ratio and the gap
(Q1 2000–Q4 2014)
Points
60
120
40
80
20
40
0
0
–20
2000
2002
2004
2006
2008
2010
2012
2014
Crisis period
Ratio gap (right-hand scale)
House price-to-household wages and salaries ratio
Average ratio (Q4 1998–Q4 2014)
Long-term trend
Sources: Statistics Lithuania and Bank of Lithuania calculations.
Note: The long-term trend is computed by applying a one-sided HP filter with a
smoothing parameter set to 400,000; before applying the filter, the ratio is
modelled for the next five-year period using a four-quarter weighted average.
22
Due to a short data series, it is impossible to explore the usefulness of the indicator in forecasting a crisis in Lithuania based on historic data and
determine its objective value that could show imbalances.
Application of the Countercyclical Capital Buffer In Lithuania
2010 = 100
160
MFI loan-to-deposit ratio
14
The MFI loan-to-deposit ratio shows the share of loans issued by banks in the domestic market deposit portfolio. The
non-deposit asset backing is often cheaper and less stable, and related to foreign capital flows, while its unbalanced
growth is quite a good indicator of excessive crediting (see Chart 10; a similar indicator is used by Behn et al., 2013).
The largest banks operating in Lithuania are subsidiaries and branches of foreign banks, and parent institutions in some
cases can provide cheaper financing to them, compared with the cost of deposits in the domestic market, thereby
encouraging unbalanced credit growth within the country. Excessive growth of the loan-to-deposit ratio may show
unsustainable credit growth.
Chart 6. MFI loan-to-deposit ratio
(Q1 1998–Q1 2015, seasonally adjusted)
Percentage
250
Chart 7. Current account balance in Lithuania and the build-up of
risk23
(Q1 1996–Q4 2014, four-quarter moving sums)
Percentage
3
0
200
–3
150
–6
100
–9
50
–12
–15
0
1998 2000 2002 2004 2006 2008 2010 2012 2014
Crisis period
Loan-to-deposit ratio
Average ratio (from Q4 1993)
Long-term average +/– 2 standard deviations
Source: Bank of Lithuania calculations.
Note: The development of the ratio is considered to be balanced if it does not
deviate from its long-term average by more than two standard deviations.
Standard deviation has been computed on the basis of Q4 1993–Q1 2006
data covering only the period of the ratio’s moderate dynamics.
–18
1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
Ratio of current account balance to GDP
Average (Q4 1995–Q4 2014)
Sources: Statistics Lithuania and Bank of Lithuania calculations.
Note: The risk level is identified based on Reinhart, S.M. and Reinhart, V.R.
(2008), “Capital flow bonanzas: An encompassing of the past and present”,
NBER Working Paper, 14321.
The MFI loan-to-deposit ratio shows the share of loans issued by banks in the domestic market deposit portfolio. The
non-deposit asset backing is often cheaper and less stable, and related to foreign capital flows, while its unbalanced
growth is quite a good indicator of excessive crediting (see Chart 10; a similar indicator is used by Behn et al., 2013).
The largest banks operating in Lithuania are subsidiaries and branches of foreign banks, and parent institutions in some
cases can provide cheaper financing to them, compared with the cost of deposits in the domestic market, thereby
encouraging unbalanced credit growth within the country. Excessive growth of the loan-to-deposit ratio may show
unsustainable credit growth.
The results of research of financial crises (e.g., Kaminsky and Reinhart, 1999; Kauko, 2012) suggest that widening of
a current account deficit can also be associated with the increase of systemic risk within the financial sector. However,
unlike the credit-to-GDP gap, it is difficult to associate this indicator directly with specific values of the CCB. According to
the ESRB (2014), the monitoring of the current account deficit may help avoid false alarms arising from the credit-to-GDP
gap. Moreover, the study performed by S.M. Reinhart and V.R. Reinhart in 2008, which presents an analysis of 181
countries divided into groups by their income level (low, average, and high income), revealed that the current account
deficit widens before financial crises in many countries. Based on this study, according to the current account deficit
24
trends in crisis and pre-crisis (2 years before a crisis) periods, 6 reference thresholds were determined, which would
allow to assess the sustainability of the current account balance and materialisation of a potential crisis (see Chart 11).
Estimate of the probability of a crisis
To assess how various sets of indicators suggest the formation of financial imbalances, discrete choice models can
be used. Combinations of indicators in discrete choice models (e.g., logit, probit models) are turned into an estimate of
23
In this case, thresholds have been established as the current account deficit across low- and medium-income countries during the crisis period and the
pre-crisis period of 2 years.
24
These thresholds are reference ones, and in case the indicator comes close to an individual threshold, this would not necessarily mean a
material risk increase.
Application of the Countercyclical Capital Buffer In Lithuania
Current account deficit
25
the probability of a crisis, which, after applying relevant thresholds, is considered a signal indicating a financial crisis.
The results of such models provide additional information compared to the use of separate indicators, as they include
significant complementary variables, while the most informative variables are given the highest weight. Along the above
described main and complementary indicators for setting the CCB reference rate, the estimate of the probability of crisis
gives additional quantitative information on the need to apply the CCB.
15
Chart 12. Composite early warning indicators of crisis for
Lithuania, computed using the best logit models assessed and
selected by the ESRB
(Q1 2001–Q1 2015)
Estimate of the probability of a crisis
1,0
0,8
0,6
0,4
0,2
0,0
2001
2003
2005
2007
2009
2011
2013
2015
Model I
Model II
Model III
Model IV
Sources: ESRB and Bank of Lithuania calculations (see Table 1).
It should be noted that the accuracy of estimates of the probability of a crisis depends on approaches and the
accuracy of their specification; therefore, it is recommended to use cross-country datasets covering a large number of
26
crisis events. The study on the CCB application by the ESRB working group that looked into the properties of both
single early warning indicators of financial crisis and their combinations covered all 28 EU Member States with data
series available from 1970, thereby ensuring sufficient robustness of the results.
Table 1. Specifications of logit models
Bank credit2-to-GDP gap
Annual change in equity prices
Debt service
Annual change in house price-toincome ratio
Constant
Model I
0.142
–
–
Model coefficients1
Model II
Model III
0.153
0.133
0.016
2.384
2.394
Model IV
0.140
0.016
2.575
–
–
0.050
0.050
–1.709
–2.479
2.340
–2.653
Source: Detken et al. (2014), Table F1 of the Annex. The models presented here correspond to the following models: Model I with Model 1, Model II with
Model 11, Model III with Model 13, and Model IV with Model 15. These models have been identified by the authors as the best ones in analysing their
signalling potential. All the coefficients in the table are significant at 1 per cent significance level.
1
Notes: logit models and the interpretation of their coefficients differ from e.g., the interpretation of linear regression models. The form of the logit model is
1
as follows: p =
,, where: p is the probability of an event, a is a constant, b is a vector of n coefficients of independent variables, and X is the
-(a+bX+ε)
1+e
(n × T) matrix of independent variables , where: n is the number of independent variables, T is the number of observations. If a + bX = 0, then p = 0.5. If
(a + bX) → +∞, 𝑝 → 1.
2
In Lithuania, bank credit to the private non-financial sector is effectively equal to the stock of MFI loans.
The above-mentioned research by the ESRB primarily determines the best early warning indicators of a crisis (which
include the previously described core and complementary CCB indicators selected for Lithuania: credit-to-GDP gap
based on the Basel method, MFI loans-to-GDP gap, house price-to-household income gap, current account deficit-toGDP ratio). Among them, the ESRB selected 15 most informative variables that were used in various specifications of
logit models, of which the most useful models, characterised by the highest prediction accuracy, were selected. These
models also meet requirements according to which the probability of a crisis estimated based on these models should
start giving consistent early signal 1–5 years prior to the crisis, and the model’s prediction quality should be better
compared to the use of single indicators. The selected models cover the following data series: bank (MFI) credit-to-GDP
25
26
If the probability exceeds the set threshold, it means a positive signal that a financial crisis will occur in the period of up to 5 years.
See footnote 23.
Application of the Countercyclical Capital Buffer In Lithuania
Variable
27
gap, annual change in equity prices, debt service ratio , and absolute annual change in house price-to-household
income ratio. Models, the specifications for which are presented in Table 1, contain different combinations of these
indicators. Chart 12 presents the probability estimates received after application of the model’s coefficients obtained in
the research to Lithuania data.
16
The analysis of single main and complementary indicators suggests that the CCB rate would have had to be
gradually increased from Q4 2003, although the analysis of single indicators does not allow for a quantitative evaluation
of the simultaneous development of the indicators, i.e., if all or many indicators change, the probability of a crisis
increases more than where only one indicator changes. Composite indicators were used in order to take into account the
correlations between separate indicators. Four selected models of composite indicators were used to estimate the
probability of a crisis (see Table 1). Optimal probability thresholds are between 0.23 and 0.26, and they were reached in
the period from Q2 2003 to Q2 2004. If these values were reached, increasing the CCB rate would be considered even if
the main and other complementary indicators did not suggest accumulation of systemic risk.
3.2. Reduction or release of the CCB rate
The CCB rate should be reduced or fully released when the decreasing overall risk in the financial sector or stress in
the financial markets signal of an upcoming downswing in the financial cycle. The credit-to-GDP gap is a very inert
indicator; therefore, it is not suitable for determining stress in the financial sector and a period when the CCB should be
reduced. After the first signs of a moderate downturn are observed, the CCB should be reduced gradually in order to
mitigate the negative impact on the financial system. It may be expedient to release the whole accumulated CCB, if any
financial system instability is noticed, thereby reducing the impact of potential tightening of credit standards on the real
economy. In such a case, it is crucial to accumulate the CCB to the right level before a crisis: if the accumulated capital
buffer were too small, the CCB reduction would have an insufficient effect.
Basis points
400
350
300
250
200
150
100
1,0
1,0
0,8
0,8
0,6
0,6
0,4
0,4
0,2
0,2
0,0
0,0
–0,2
–0,2
–0,4
1999
50
0
2009
Chart 8. The ECB’s Composite Indicator of Systemic Stress (CISS)
28
and its factors
(January 1999–May 2015; weekly data)
2010
2011
2012
SEB
DNB
Nordea
Danske
Swedbank
Markit iTraxx Europe Financial Senior
Source: Bloomberg.
2013
2014
–0,4
2001
2003
2005
2007
2009
2011
2013
2015
Equity market
Foreign exchange market
Bond market
Financial sector
Money market
Correlation
Composite indicator of systemic stress (right-hand scale)
Source: ECB.
Note: For more information, see Holló, D., Kremer, M., Duca, M., ‘‘CISS – a
composite indicator of systemic stress in the financial system’’, Working Paper
Series, No 1426, ECB, March 2012.
Indicators, on the basis of which the CCB would be reduced, will depend on financial instability or the nature of a
crisis. Consistent change in the financial cycle can be determined from indices signalling about the build-up of risk to the
financial system — credit to GDP gap or indices related to real estate. However, these indicators can decline at a rather
slow pace or do not decline at all in case of instability of the financial system. It should be noted that, during an economic
recession, the GDP may contract faster than credit, thus the credit-to-GDP gap may even increase during the peak of the
crisis (this was observed in Lithuania in 2009: the GDP fell faster than credit). Hence, to determine the trough point in the
credit cycle growth indicators such as the rate of credit growth, the rate of RE price increase, growing bank credit losses,
27
Debt repayment and interest payment amount, as a percentage of GDP.
ECB’s Composite Indicator of Systemic Stress is an aggregate of 5 sub-indices representing equity, foreign exchange and bond markets, financial sector
and money market — a total of 15 individual indicators showing situation in different market segments. Also, this composite indicator allows for timevariation in the correlation between sub-indices, as stress is higher and more significant if it prevails in several financial market segments.
28
Application of the Countercyclical Capital Buffer In Lithuania
Chart 13. Five-year credit default swaps (CDS) of Scandinavian
banks
(1 January 2009–1 June 2014)
etc., could be more useful than the relative ones. The usefulness of these indicators is also analysed in empiric research
(e.g., ESRB, 2014). In addition, credit price indicators could be used. For instance, significant increases in lending
margins would also signal about tighter credit risk assessment and worsening credit accessibility.
17
Chart 9. Indicator of financial stress in Lithuania
(1 January 2009–1 May 2015)
1,0
0,8
0,6
0,4
0,2
0,0
–0,2
–0,4
2007 2008 2009 2010 2011 2012 2013 2014 2015 01
07
2014
Correlation effect
Financial sector
Bond market
Stock market
Foreign exchange market
Money market
Lithuania's financial stress indicator
01
2015
Sources: Bloomberg and Bank of Lithuania calculations.
Note: For more information, see Holló, D., Kremer, M. , Duca, M.
(2012),"CISS – a composite indicator of systemic stress in the financial
system", Working Paper Series, No 1426, ECB, March 2012.
When considering a decision on the CCB reduction, the ESRB (2014) recommends using indicators showing the
availability of bank credit resources (such as the Libor-OIS spread and bank CDS premia) and indicators reflecting the
general financial market situation (such as the ECB CISS (Composite Indicator of Systemic Stress) indicator, proposed
by Holló, D. et al., 2012, after adapting it to the Lithuanian market; see Chart 15). These indicators cannot fully reflect the
market situation in Lithuania, as the financial market is not developed well enough; consequently, other indicators could
also be used to ensure credibility.
Most of Lithuania’s financial market is managed by Scandinavian capital bank groups. Naturally, the financial market
situation in Scandinavian countries has an indirect impact on the financial market situation in Lithuania. An increase in
various financial stress indicators such as CDS premia in Scandinavian and other EU countries is likely to contribute to
the rise of financial stress in Lithuania. Against this background, data of five-year credit default swaps of Scandinavian
parent banks (see Chart 13) and the ECB CISS indicators (see Chart 14) could be used for indication purposes,
assuming that changes in these indicators would have an impact on the activities of banks operating in Lithuania.
3.3. Practical implementation of decisions and their publication
The Bank of Lithuania takes a decision on setting the CCB rate on a quarterly basis. Each decision and the
background information for it are published on the Bank of Lithuania’s website. After taking a decision on the CCB, the
Bank of Lithuania will publish information on its website about the set level of the CCB rate, credit-to-GDP ratio and its
deviation from the long-term trend, calculated reference rates of the buffer, and additional indicators. The basis for the
need to apply a respective CCB rate and the date of a decision to come into effect for financial institutions will be
published too.
Generally, decisions on increasing the CCB rate would come into effect after one year, while reduction decisions —
immediately. This is done for banks to have time to accumulate the required additional capital and for crediting to be not
Application of the Countercyclical Capital Buffer In Lithuania
When reducing the CCB during financial crises, data available at a higher frequency, reflecting the deterioration of
credit availability conditions and financial market distress, could be referred to. Financial market indicators such as CDS
spreads, risk premiums of corporate bonds, credit margins, etc. are published on a daily basis and can reflect prevailing
trends in these markets. Empiric research has confirmed that these indicators are suitable for the use as indicators
signalling about the need to reduce the CCB rate (Drehmann et al., 2011). However, they can be highly variable; hence,
they should be interpreted with caution in taking decisions on changing the CCB. It should be noted that the signalling
capacity of such indicators can be weak in countries with a relatively shallow financial market, as is the case of Lithuania;
thus, there is no need to set any specific critical values of indicators, which, if reached, would require reducing the CCB
rate.
18
Application of the Countercyclical Capital Buffer In Lithuania
significantly restricted during a financial recession phase. In exceptional cases, the Bank of Lithuania has the right to set
a period shorter than 12-months for the CCB rate to take effect, presenting also the motives for the urgency of the
decision. After the decision on the reduction of the CCB rate is taken, it can be implemented immediately, i.e., banks will
be able to use the accumulated buffer for covering their losses. After the CCB rate is reduced (or released), the period
during which the CCB rate is not to be increased will also be published, thereby ensuring regulatory stability.
References
19
Angelini, P., Neri, S., Panetta, F. (2012), “Monetary and macroprudential policies”, ECB Working Paper No. 1449.
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Application of the Countercyclical Capital Buffer In Lithuania
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Annex. Information on data used to compute the main and complementary indicators
Indicator
Period
Description
Source
21
Credit-to-GDP gap, MFI loans-to-GDP gap
GDP
Q4 1995–Q1 2015
Market value of Lithuania’s GDP (according to ESA 2010), at
current prices, four-quarter moving sum.
Statistics Lithuania
MFI loans
Q4 1993–Q1 2015
Loans of other MFIs operating in Lithuania granted to the
private non-financial sector (Lithuanian residents). The
private non-financial sector comprises non-financial
corporations and households, as well as non-profit
institutions serving households (residents only). End-ofperiod balance.
Bank of Lithuania
(balance sheet statistics
of other MFIs)
Credit
Q4 2003–Q4 2014
Credit granted by all sectors (both residents and nonresidents) to the private non-financial sector (Lithuanian
residents). Credit granted is defined as loans granted to the
private non-financial sector and debt securities issued by
non-financial corporations (other than equity). The private
non-financial sector comprises non-financial corporations
and households, as well as non-profit institutions serving
households (residents only). End-of-period balance
(according to ESA 2010).
Bank of Lithuania (
Financial Accounts of
Lithuania)
Q4 1993–Q3 2003
Extrapolated according to annual credit data and relative
changes of quarterly-frequency in MFI loans29 (the sum of
separately extrapolated series of credit to households and
non-financial corporations)
Statistics Lithuania
(National accounts of
Lithuania), Bank of
Lithuania ( balance sheet
statistics of other MFIs)
House price-to-household income gap (index, 2010 = 100)
Housing price index
Household income
Q1 2006–Q4 2014
Housing price index, 2010 = 100
Statistics Lithuania
Q1 1998–Q4 2005
Extrapolated according to relative changes in the average
house price (1 sq. m.)
State Enterprise Centre
of Registers
Q4 1995–Q1 2015
Salaries and wages at current prices (from GDP by income
approach, according to ESA 2010), seasonally adjusted.
Statistics Lithuania
(National accounts of
Lithuania)
Loans
Q4 1993–Q1 2015
Loans of other MFIs operating in Lithuania granted to
Lithuanian residents: non-financial corporations, households,
and non-profit institutions serving households and financial
intermediaries. End-of-period balance.
Bank of Lithuania
(balance sheet statistics
of other MFIs)
Deposits
Q4 1993–Q1 2015
Deposits accepted by other MFIs operating in Lithuania from
non-financial corporations, households and non-profit
institutions serving households and financial intermediaries.
End-of-period balance, seasonally adjusted.
Bank of Lithuania
(balance sheet statistics
of other MFIs)
Current account deficit-to-GDP ratio
Current account
deficit
Q4 1993–Q4 2014
Current account balance (with minus), four-quarter moving
sum
Bank of Lithuania
(Balance of Payments
statistics)
GDP
Q4 1995–Q1 2015
Market value of Lithuania’s GDP (according to ESA 2010), at
current prices, four-quarter moving sum.
Statistics Lithuania
29
According to the methodology for building long time series of the international credit data base of the Bank for International Settlements (see
http://www.bis.org/publ/qtrpdf/r_qt1303h.pdf)
Application of the Countercyclical Capital Buffer In Lithuania
Loans-to-deposits ratio