Proceedings of Annual Paris Economics, Finance and Business Conference
7 - 8 April 2016, Espace Vocation Haussmann, Paris, France
ISBN: 978-1-925488-04-3
A Multivariate Signal Extraction Framework for Predicting
Banking Crises in European Union
Romulus Mircea* and Ionut Dumitru**
Knowing if the financial stability deteriorates is essential for the authorities
to deploy the right macroprudential tools. This paper assesses the
usefulness of signal extraction models in identifying (i) the risk of a banking
crisis occurring in the short term, as well as (ii) the time-periods of
excessive build-up of systemic imbalances that may lead in the medium
term to a banking crisis. We evaluate the predictive power of a wide range
of economic and financial variables, under different specifications, in
signaling one of these two states across the banking sectors of the EU 28
member countries between 1970 and 2012, covering 33 episodes of
systemic banking crises. For each tested variable we derive the optimal
signal threshold by minimizing the noise-to-signal ratio conditional upon a
minimum hit-rate. We first start by estimating univariate signal extraction
models and then proceed to combine the best performing variables in a
multivariate framework. The stability of the estimated models is tested both
in-sample and out-of-sample under a bootstrapping framework. We find the
dynamics of household credit to GDP (or income) gap, real M3 growth, the
current account deficit as a percentage of GDP, residential property prices
gap, the real growth rate of equity prices and the inverse of long term
interest rates to be leading indicators for the likelihood of a systemic
banking crisis occurring in the medium term (4-20 quarters ahead). As
regards the probability of the occurrence of a systemic banking crisis in the
short term (1-4 quarters ahead), risk aversion indicators, such as LIBOROIS spread or the ECB’s composite indicator of systemic stress, the
dynamics of real government bond yields and the corporate credit to GDP
gap have the best predictive power.
JEL Codes: E51, G01, G21
1. Introduction
The recent global financial crisis has revealed two important aspects: (i) the
macroprudential framework for the banking system had to be overhauled to better manage
the risks that have been underestimated or neglected prior to the crisis and (ii) the financial
shocks amplify the imbalances in the real economy, increasing the negative consequences
of the systemic crises. Even though a recession can be regarded as a normal phase within
an economic cycle, it can generate significant economic losses, if it follows a period of
rapid credit growth.
As a result authorities have introduced after 2009 a new capital adequacy framework,
aimed at increasing the resilience of the financial institutions to systemic risks (both
cyclical and structural in nature). Among the new capital rules, the countercyclical capital
buffer (CCB) is one of the most important macroprudential instrument aimed at managing
the risks stemming from the credit cycle. It is aimed at strengthening the resilience of the
* Romulus Mircea, PhD student, Doctoral School of Finance, Bucharest University of Economic Studies,
Romania, Email: romulus.mircea@gmail.com
**Dr Ionut Dumitru, Professor, Finance and Banking, Bucharest University of Economic Studies, Email:
dumitruionut@yahoo.com
2
European banking sector to potential shocks resulting from a period of excessive credit
expansion and to reduce the amplitude of the credit cycle.
The timing and calibration of the CCB is a relatively new research field, with still many
avenues to be explored related (i) to the models and variables used to signal the
deployment or the release of the CCB or (ii) to the assessment of the CCB’s efficacy and
efficiency relative to other macroprudential instruments. This paper focuses on the former
topic, by expanding previous work done in the area of early warning models for banking
crises. More specifically, we developed models for signaling the time-periods of a build-up
in systemic cyclical risks (build-up phase), when the activation of CCB would be
warranted, as well as models for signaling the imminence of a banking crisis (release
phase), when authorities should act to reduce CCB. Our analysis covered 33 systemic
banking crises that occurred across the EU-28 member states over the time-period 19702012. We used the signal extraction approach in order to assess the explanatory power of
a wide range of macroeconomic and financial variables.
The rest of this paper is organized as follows. In section 2, we summarize the literature on
early warning models for financial crises, with focus on signal extraction models, as well as
the literature on the explanatory variables found to be relevant in predicting a crisis. In
section 3 we describe the employed estimation methodology and the data used. In section
4 we present our main findings and finally we conclude with a summary in section 5.
2. Literature Review
Banking crises generate significant costs, beyond those related to the public sector’s
intervention, leaving scars on the economy a long term after it ends. Laeven and Valencia
(2012) found that fiscal costs of banking crises amounted on average 10% of GDP in
emerging markets and 3.8% of GDP in developed economies between 1970 and 2012.
The indirect costs of a banking crisis are significantly higher than the direct ones. Output
losses, as measured by the negative GDP deviation from its pre-crisis trend, cumulate
32.9% of GDP in developed economies and 26% in emerging markets, 3 years after the
onset of the banking crisis. Unemployment rate increases on average by 7 p.p., 5 years
after the start of the crisis. The deterioration of the economic fundamentals creates a
vicious cycle, leading to an increase in non-performing loans (on average up to 25% of
total assets) and to higher public debt (+86% growth rate 3 years after the crisis started),
further restraining the recovery (Reinhart and Rogoff, 2009).
Leading indicators for banking crisis can be classified according to the time horizon over
which they signal a crisis: (A) medium term indicators and (B) short term indicators. The
first category of variables can guide the macroprudential authorities on the timing of an
increase in CCB, whereas the second type of indicators is useful in signaling a potential
release of CCB.
A. Rapid credit expansion, as a result of financial sector liberalization, deregulation,
financial innovation, low real interest rates and abundant foreign capital, lead to the buildup of macroeconomic vulnerabilities that over the medium term time may trigger a banking
crisis. The credit to GDP gap from its long term trend is able to capture the development of
a credit boom and is considered to be one of the most important leading indicators for
signaling a banking crisis over the medium term (Kaminsky and Reinhart, 1999, Borio and
Lowe, 2002, Borio and Drehmann, 2009, Alessi and Detken, 2011). A drawback of this
indicator is that it generates a higher number of false crisis signals in case of emerging
markets, where the rapid credit growth is associated to a larger extent with a real
3
convergence process and to a less extent with the accumulation of imbalances (Gersl and
Seidler, 2011).
Other macroeconomic indicators found in the literature to have predictive power for a
banking crisis over the medium term are real economic growth, public debt, unemployment
rate, broad money (M3), real effective exchange rate, current account balance and interest
rates (Borio and Drehmann, 2009, Drehmann et al, 2010,2011, Kauko, 2012a). Variables
that measure the dynamics of the real estate market (prices, rents, households’ income)
are also important in identifying build-up phases (Borio and Drehmann, 2009, Barrell et al,
2010a).
B. Within the second set of explanatory variables, Drehmann et al. (2010) and Juks and
Melander (2012) identify the systemic risk indicators, the increase in non-performing loans
and the real growth rate of private credit as having the highest predictive power in
signaling the release phase. The composite indicator of systemic stress (CISS) developed
by Hollo et al (2012) aggregates risk indicators across the five segments of the financial
markets (money market, bond market, equities, banking sector and foreign exchange) in
the Euro Zone and is able to signal a material deterioration in the risk appetite of investors,
which usual precedes a banking crisis. The spread between LIBOR and the overnight
index swap rate is another measure of risk aversion, capturing the investors’ perception on
the prime banks’ credit risk (Federal Reserve Bank of St Louis, 2009).
There are two types of models used in the literature to find leading indicators and forecast
banking crises: (i) signal extraction and (ii) discrete models. The former, on which we focus
on in this paper, are able to handle better non-linear relationship between dependent and
independent variables, as they do not impose any statistical assumptions on the
explanatory variables. The signal extraction methodology has been documented for the
first time in the crisis literature by Kaminsky and Reinhart (1996, 1999) and subsequently
has been analyzed in other empirical studies (Kaminsky et al, 1998, Borio and Lowe 2002,
Drehmann et al 2010, 2011, CGFS, 2012). For a given explanatory variable the signal
extraction model identifies a critical threshold, which delineates the boundary between
crisis and non-crisis episodes over a given time horizon. Once the respective variable
exceeds that threshold a crisis signal is issued. The optimal threshold is estimated by
minimizing the noise to signal function (NS) (Kaminsky et al 1998), calculated as a ratio
between false crises signals (noise) and correctly identified crises (signal or hit rate). The
signal extraction model presents computational challenges in a multivariate framework, as
the critical thresholds for all explanatory variables have to be jointly estimated. For this
reason the literature on multivariate signaling is limited to the bivariate case (Borio and
Lowe, 2002, Alessi and Detken, 2011, Drehmann and Juselius, 2014 and CGFS, 2012).
3. The Methodology and Model
3.1. Banking crises episodes and the dependent variable
We used the database on banking crises developed by Laeven and Valencia (2012) and
the ESCB Heads of Research Group in order to construct the dependent variable. A
banking crisis episode is defined, according to their methodology, as a period marked by
massive deposit withdrawals or significant losses in a banking system1, triggering public
sector intervention, either through recapitalisation or liquidity injections. We updated the
database with the qualitative assessments of ESRB, excluding banking crises episodes
that were not considered to be systemic or those that were not triggered by a domestic
credit cycle and including potential banking crises episodes that would have materialised,
4
had the authorities not intervened. The final database consists of 33 banking crisis
episodes, which occurred between 1970 and 2012 (quarterly frequency) across the 28
European Union member states (Chart 1).
We constructed two categorical variables (Y), based on the banking crisis database:
(1)
(2)
where i represents the country and T the quarters where country i experienced a banking
.
crisis and
Dummy variable (1) is used in calibrating the early warning model for the build-up phase,
defined as 20 to 5 quarters prior to a banking crisis. On the one hand, authorities should
have sufficient time to implement countercyclical macroprudential measures effectively
(ESRB recommends a period of at least 1 year for banks to comply with new capital
requirements). On the other hand, there are economic costs associated with measures
aimed at tightening the supply of credit too early in the credit cycle. For this reason,
Drehmann and Juselius (2013) recommend using a period of maximum 5 years prior to a
banking crisis, in order to define the build-up phase.
Dummy variable (2) identifies the the release phase (4 to 1 quarters prior to a banking
crisis). It is used in estimating the early warning models for the timing of the release in
CCB.
Chart 1: Number of EU 28 countries in a banking crisis
14
12
10
8
6
4
2
1970Q1
1971Q2
1972Q3
1973Q4
1975Q1
1976Q2
1977Q3
1978Q4
1980Q1
1981Q2
1982Q3
1983Q4
1985Q1
1986Q2
1987Q3
1988Q4
1990Q1
1991Q2
1992Q3
1993Q4
1995Q1
1996Q2
1997Q3
1998Q4
2000Q1
2001Q2
2002Q3
2003Q4
2005Q1
2006Q2
2007Q3
2008Q4
2010Q1
2011Q2
2012Q3
0
Source: ESRB (2014), own calculations
In Table 1 we summarised for each of the 28 EU member states the quarters prior to a
banking crisis, when the two dummy variables take a value of 1.
5
Table 1: Dependent dummy variables used in calibrating the early warning models
Q1 1970
Q1 1980
Q1 1990
Q1 2000
Q1 2010
Austria
Belgium
Bulgaria
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Malta
Netherlands
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Sweden
United Kingdom
Croatia
- 20 to 5 quarters prior to a crisis
- 4 to 1 quarters prior to a crisis
- the quarter when the banking crisis started
3.2. Explanatory variables
The list of potential explanatory variables is derived from a set of primary indicators
covering various characteristics of the real economy, price and credit dynamics, banking
sector and financial markets in the EU 28 member states between 1970 and 2012, with a
quarterly frequency (Table 2). To each variable calculated from the primary indicators we
apply several transformations: (i) level, (ii) annual change in level, (iii) annual change in
percentage and (iv) absolute or relative deviation from the trend estimated with a Hodrick
Prescott filter (recursive or with a moving window of 40 quarters). Overall, we tested the
signal power of 126 variables in identifying the build-up and the release phases.
We excluded from the panel data the observations corresponding to a banking crisis
episode as well as the observations up to 6 quarters after the crisis ended, in order to
avoid a bias on the signal quality induced by the dynamics of the explanatory variables
after the onset of the crisis (Bussiere and Fratzscher, 2002).
The main sources of the data for the primary indicators were: IFS (IMF), ECB, Eurostat,
Bloomberg, BIS and central banks’ websites. For the measure of total credit we used data
from BIS database (Dembiermont et al., 2013) on credit originated to the non-financial
sector by both bank and non-bank sector (domestic and foreign based). We analysed
separately credit to non-financial companies and households, in order to investigate
whether the excessive indebtedness of a particular sector has more influence on triggering
a build-up or release phase.
The credit to GDP ratio is determined by dividing the measure of credit in a given quarter
to the annual GDP from the past 4 quarters. The gap is estimated with a Hodrick Prescott
6
filter with a smoothing parameter lambda of 400,000, which assumes that a financial cycle
is 4 times longer than an economic cycle (Drehmann et al, 2010). We have also estimated
the gap with different lambdas (26,000 and 130,000, which imply a ratio of 2 and
respectively 3 between the length of the financial and economic cycle), but the differences
are not significant in terms of the dynamics around the banking crisis episodes (Chart 2).
Table 3: Primary economic and financial indicators
Category
Real economy
Credit
Prices
Banking sector
Financial
markets
Primary indicator
Nominal GDP
Real GDP
Unemployment
Broad money (M3)
Real effective exchange rate
Current account balance
Employment compensations
Total number of employees
Private sector credit
Household credit
Non-financial companies credit
Public debt
Consumer price index
Residential price index
Rent prices index
Non-performing loans
Liquidity (short term asset/short term debt)
Short term interest rate
Long term interest rate
LIBOR-OIS spread
CISS
Equity prices
Credit to GDP gap estimated
with a recursive HP filter
Chart 2: Median credit to GDP gap around the banking crisis episodes
Number of quarters (T) before and after a banking crisis
Note: calculations are based on available data for EU 28 countries between 1970 and
2012
7
3.3. The estimation methodology
We used the signal extraction model to develop two early warning models: (i) for the buildup phase and (ii) for the release phase. The dependent and explanatory variables are
described in section 3.1. and 3.2.
The predictive power of an explanatory variable is assessed by the noise to signal (NS)
ratio evaluated at the optimal threshold.
For a single variable, the optimal threshold is derived by minimising the NS function over
the percentiles of the historical distribution of that variable, conditional upon achieving a
minimum hit rate2 of at least 2/3:
(3)
,
where c is the percentile of the country specific historic distribution of the analysed
is the NS function evaluated at percentile c and
is the hit rate for the
variable,
percentile c.
We defined the NS function as:
(4)
, where A, B, C and D are the possible outcomes of the signal issued at
threshold c by the tested explanatory variable (Table 4). For a variable with perfect
discriminatory power at threshold c,
will equal 0, whereas for an indicator issuing
random signals,
will equal 1.
Similarly, the hit rate S is defined as:
(5)
Table 4: The evaluation of the signal accuracy for a variable
Signal\ Outcome
Crisis signal
Non-crisis signal
Crisis occurs
A – number of correctly
identified crises episodes
C – number of false non-crisis
signals
No crisis occurs
B – number of false crisis signals
D – number of correctly identified
non-crisis episodes
In a multivariate framework, the NS function is minimised over all possible combinations of
the percentiles of the variables’ historic distribution. A crisis signal is issued when all the
explanatory variables are above their individual optimal thresholds.
(6)
,
where c1,c2,…,cn are the percentiles of the n variables in the multivariate analysis.
In a first step we estimated for each of the two early warning models the univariate
predictive power (the minimum NS ratio) of all explanatory variables described in section
3.2. Then we proceeded to estimate the bivariate predictive power of all possible
combinations of two variables with a correlation below 80% (as recommended by Mircea,
2007) and with the univariate NS ratio below 1. We limited our multivariate analysis to the
8
bivariate case, due to the limitations of the optimisation algorithm. The difficulty of solving
for the minimum NS ratio increases exponentially as the number of independent variables
increases.
The explanatory variables related to macroeconomic developments are introduced in the
models with a lead of one quarter to reflect the real-time availability of data. The
explanatory variables are also introduced with a negative sign in the models in order to
assess a possible negative relationship with the dependent variable.
In order to test the stability of the models’ predictive power we run 100 bootstrapping
iterations. At each iteration we determine the optimal threshold(s) and the minimum NS
ratio on a sample of 14 randomly drawn countries from the set of EU 28 countries and then
we estimate the predictive power of the model out-of-sample on the remaining 14
countries. In this way we can determine confidence intervals for the NS ratio and for the
optimal thresholds.
4. The Findings
4.1. Early warning models for the build-up phase
In table 5 we summarised the results for the univariate models with best predictive power.
The dynamics of credit to households as a percentage of GDP or disposable income,
calculated under various transformations, have the lowest NS ratio out of all explanatory
variables tested. For example, in case of household credit to GDP ratio a signal is issued
when the indicator exceeds the 67% percentile of the historical distribution of a country.
The NS ratio for this variable is 0.36, equivalent to a hit rate of 67% and a type II error
(noise rate) of only 24%.
The variables measuring the dynamics of credit to non-financial companies have a low
predictive power in signalling the build-up phase – NS ratio is close to 1 in most cases. A
potential explanation for this is the fact that most of the banking crisis episodes used in
calibrating the models occurred during the 2008 global financial crisis, which was caused
to a large extent by the accumulation of imbalances at the household sector.
Within the category of macroeconomic variables, the real growth rate of broad money (M3)
and the current account balance as a percentage of GDP have a satisfactory NS ratio. The
findings are in line with other results from the literature (Adalid and Detken, 2007, Kauko,
2012a). The dynamics of other macroeconomic variables that measure the economic
cycle, such as real economic growth or unemployment rate, perform poorly in signalling
the vulnerability period. As the economic cycle is shorter than the credit cycle, such
variables tend to have to a higher noise rate.
The rapid decrease in the cost of financing and an exuberant equity market are also signs
of a build-up phase. The inverse of long term interest rates and the growth rate of the
stock markets have a low NS ratio.
The dynamics of real estate market are important in signalling the build-up phase.
However, the tested variables are less stable and generate higher noise rates. In some
cases real estate prices start to decline before the banking crisis sets in, which implies that
the sign of the relationship between the explanatory and the dependent variable changes
from positive to negative.
9
Table 5: Explanatory variables with lowest NS ratio
Bootstrapping in-sample NS
Variable
sign
Variable
Variable category
Optimal
threshold
Bootstrapping out-of-sample NS
NS
5%
percentile
Median
95%
percentile
Median
5%
percentile
95%
percentile
Household credit/ GDP
Household credit/ disposable
income
67%
0.36
39%
23%
70.1%
41.0%
31.1%
51.1%
63%
0.42
45%
24%
71.6%
47.2%
37.6%
62.3%
62%
0.44
43%
28%
61.4%
43.7%
37.0%
51.6%
61%
0.45
44%
28%
67.3%
49.0%
41.4%
58.4%
+
Total credit/ GDP
Household credit/ GDP
absolute gap from recursive HP
trend
Household credit/ GDP
absolute gap from rolling
window HP trend
61%
0.46
47%
32%
69.3%
49.6%
43.4%
60.8%
-
Current account balance/GDP
53%
0.62
62%
40%
93.8%
65.5%
58.2%
90.1%
+
Real M3 annual growth rate
45%
0.72
69%
51%
85.4%
74.4%
64.4%
88.1%
+
Real GDP annual growth rate
Unemployment rate relative gap
from recursive HP filter
Real effective exchange rate
annual growth rate
Inverse long term real interest
rates
Inverse short term real interest
rates
46%
0.75
76%
66%
90.2%
76.6%
70.6%
84.7%
43%
0.81
80%
68%
91.9%
84.8%
75.6%
100.2%
39%
0.82
82%
74%
91.1%
83.5%
78.0%
91.7%
55%
0.59
60%
39%
80.5%
61.7%
53.9%
81.3%
54%
0.60
59%
44%
78.5%
64.0%
56.3%
77.0%
40%
0.74
72%
64%
81.6%
75.6%
67.6%
83.2%
40%
0.77
75%
61%
85.9%
81.3%
71.7%
98.3%
45%
0.78
72%
43%
101.0%
80.4%
65.9%
99.2%
39%
0.88
87%
75%
101.9%
92.8%
85.4%
109.1%
38%
0.88
88%
71%
101.7%
91.9%
85.0%
102.1%
38%
0.90
90%
77%
101.9%
93.6%
86.0%
104.6%
38%
0.90
89%
78%
101.8%
94.5%
84.7%
107.1%
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Stock index annual growth rate
Stock index annual real growth
rate
Residential property prices
annual growth rate
Residential prices/ Disposable
income relative gap from
recursive HP trend
Residential prices/ Disposable
income relative gap from rolling
window HP trend
Residential prices/ Rent prices
relative gap from recursive HP
trend
Residential prices/ Rent prices
relative gap from rolling window
HP trend
Credit
Macroeconomy
Financial Market
Real estate
For the multivariate analysis, we estimated bivariate models between all possible
combinations of two variables with the best univariate predictive power, as described in
section 3.3.
The best performing bivariate models include combinations between the dynamics of
household credit to GDP, M3, current account balance, real estate and equity prices and
long term interest rates (Table 6). The combination between household credit to GDP and
the real growth rate of M3 has the lowest NS ratio (0.16), reflecting a hit rate of 68% and a
noise ratio of only 11%. The findings are similar to other results from literature (Borio and
Drehmann, 2009, Drehmann and Juselius, 2014).
The NS ratio for these models is lower than the NS ratio for the individual univariate
models, which demonstrates that the interaction between the explanatory variables
improves the signal quality. Also, the optimal thresholds are lower than in the univariate
models. Intuitively, this can be explained by the fact that the probability for two variables to
jointly exceed the optimal threshold is lower than the probability for each variable to
exceed that threshold.
10
Table 6: Bivariate models with lowest NS
Model
no.
Variable 1
Household
1 credit/ GDP
Variable 2
Real M3 annual
growth rate
Household credit/
GDP absolute gap
Household
from rolling window
HP trend
2 credit/ GDP
Household
Stock index annual
real growth rate
3 credit/ GDP
Household
Current account
balance/GDP
4 credit/ GDP
Household
Inverse long term
real interest rates
5 credit/ GDP
Residential property
prices relative gap
Household
from rolling window
HP trend
6 credit/ GDP
Inverse long term Real GDP relative
real interest
gap from recursive
HP trend
7 rates
Residential property
Inverse long term prices absolute gap
real interest
from rolling window
HP trend
8 rates
Credit to GDP
Stock index
absolute gap from
annual real
rolling window HP
trend
9 growth rate
Inverse long term
real interest
Stock index annual
growth rate
10 rates
Optimal
Optimal
threshold
threshold
variable
variable 2
1
Bootstrapping in-sample NS
NS
Median
Bootstrapping out-of-sample NS
5%
95%
5%
95%
Median
percentile percentile
percentile percentile
Sign
Sign
variable variable
1
2
0.64
0.30
0.16
0.24
0.04
0.55
0.24
0.07
0.49 +
+
0.62
0.4
0.20
0.23
0.07
0.52
0.22
0.06
0.44 +
+
0.66
0.24
0.25
0.25
0.16
0.45
0.26
0.17
0.38 +
+
0.66
0.04
0.32
0.31
0.17
0.47
0.35
0.23
0.51 +
-
0.66
0.08
0.34
0.36
0.21
0.59
0.37
0.22
0.53 +
+
0.66
0.14
0.35
0.34
0.20
0.59
0.39
0.25
0.62 +
+
0.48
0.2
0.51
0.50
0.31
0.70
0.54
0.37
0.73 +
+
0.46
0.28
0.51
0.51
0.24
0.92
0.56
0.31
0.86 +
+
0.32
0.34
0.51
0.50
0.31
0.68
0.54
0.38
0.74 +
+
0.52
0.16
0.52
0.49
0.29
0.72
0.53
0.37
0.72 +
+
Real annual growth rate of broad money (M3)
Chart 3: Discriminatory power of the bivariate model with the lowest NS ratio
Crisis
Crisis signal
area: area:
Hit
rate=68%
Hit rate = 68%
Noise
(type
II error)=11%
Type IIrate
error
= 11%
Household credit/ GDP
Note: 1 – build-up phase, 0 - release phase
4.2. Early warning models for the release phase
In table 7 we summarised the results for the univariate models with the best predictive
power in signalling the release phase. NS ratio for these models is lower than in case of
the univariate models for the build-up phase. The shorter forecasting horizon increases the
accuracy of the model.
11
Variables measuring investors’ risk aversion have the highest signal quality in predicting a
systemic banking crisis in the short term. A generalised increase in risk aversion prior to
the onset of a banking crisis is usually reflected in all financial markets (money market,
bond market, FX and equities) through a significant rise in the risk premium demanded by
the investors to hold financial assets.
The spread between LIBOR and the overnight index swap (OIS) rate has the lowest NS
ratio (0.18) from all the tested variables, corresponding to a hit rate of 72% and a type II
error of only 13%. A crisis signal is issued when the spread exceeds the 84% percentile.
ECB’s CISS indicator and the dynamics of long term interest rates have also a good
prediction power (NS ratio of 0.41 and 0.46).
The dynamics of equity markets are important in predicting a banking crisis in the short
term. However, the type II error for this variable is relatively high (40%), as sharp falls in
equity prices are not always correlated with economic fundamentals.
Among the variables that measure the financial stability of the banking sector, the
dynamics of liquidity has the lowest NS ratio. However, the statistical relevance of this
variable is low, due to the short time-series. In line with other results from the literature
(ESRB, 2014), we find that the non-performing loans (NPLs) have a low prediction power
in signalling a banking crisis. This is due to the fact that NPLs do not always increase prior
to a crisis, as debtors with a deteriorating financial position can still draw on their credit
lines to service the debt.
Total credit to the private sector as a percentage of GDP, calculated under various
transformations (level, absolute/ relative gap for recursive/rolling window HP trend), has
also a satisfactory explanatory power. However the NS ratio is lower than in case of the
univariate models for the build-up phase. The reason for this is that the credit dynamics
across debtors becomes more heterogeneous as a crisis approaches. In some of these
cases, total credit may continue to expand (as debtors draw on their credit lines), while the
GDP growth rate decelerates, resulting in an increase of the credit/GDP ratio. There are
also situations, when the growth rate of total credit decelerates faster than economic
activity, as banks tighten lending standards, determining a stabilisation or even decrease
in credit/GDP ratio.
In table 8 we summarised the results for the bivariate models with the best discriminatory
power. The combination between LIBOR-OIS spread (maximum quarterly level) and the
annual change in long term real interest rates has the lowest NS ratio (0.12),
corresponding to a hit rate of 68% and a type II error of only 8%. The interaction between
the LIBOR-OIS spread and various measures of credit dynamics (models 3-5 from table 8)
generates the highest hit rate (around 80%) from all estimated models. The dynamics of
credit to non-financial companies appears to be relevant for signalling the release phase
(model number 5). The firms’ behaviour of drawing on their credit lines as their financial
situation deteriorates (in the proximity of a crisis) may be an explanation for this.
12
Table 7: Explanatory variables with lowest NS ratio
Bootstrapping in-sample NS
Variable
sign
+
+
+
+
+
Variable
LIBOR-OIS spread
end of quarter obs
LIBOR-OIS spread
maximum level
recorded during a
quarter
CISS end of quarter
CISS maximum level
recorded during the
quarter
Long term interest
rate, yoy change
-
Long term real
interest rate, yoy
change
Stock index, real
annual growth rate
Liquid assets to
short term liabilities,
yoy growth rate
+
Nonperforming loans,
yoy growth rate
+
-
+
+
+
Total credit/ GDP
Total credit/ GDP,
absolute gap from
rolling window HP
trend
Total credit/ GDP,
relative gap from
rolling window HP
trend
Variable
category
Financial
market
Banking
sector
Credit
Optimal
threshold
5%
percentile
Median
Bootstrapping out-of-sample NS
95%
percentile
Median
5%
percentile
NS
0.84
0.18
29%
14%
84.0%
28.2%
16.3%
76.0%
0.84
0.18
27%
13%
92.9%
27.0%
16.7%
66.8%
0.7
0.41
54%
32%
96.0%
54.3%
40.0%
96.7%
0.7
0.41
47%
30%
67.4%
47.8%
39.6%
60.7%
0.67
0.46
46%
37%
55.6%
49.4%
40.2%
68.2%
0.62
0.50
49%
40%
57.9%
53.2%
45.0%
65.7%
0.59
0.58
60%
33%
84.3%
66.7%
56.3%
107.9%
0.56
0.50
...
0.49
0.50
...
0.63
0.52
51%
41%
64.3%
57.0%
48.7%
66.9%
0.63
0.52
56%
42%
92.1%
56.9%
47.6%
75.2%
0.58
0.53
57%
43%
92.8%
60.0%
48.3%
72.1%
...
...
...
...
...
...
...
...
...
...
Note: CISS – ECB’s composite indicator of systemic stress
Table 8: Bivariate models with lowest NS
Model
no
1
2
Variable 1
LIBOR-OIS spread
maximum level
recorded during a
quarter
LIBOR-OIS spread
maximum level
recorded during a
quarter
95%
percentile
Variable 2
Optimal
threshold 1
Optimal
threshold 2
NS
Variable
sign 1
Variable
sign 2
Long term real interest
rate, yoy change
0.84
0.58
0.12
+
+
Stock index, real annual
growth rate
0.84
0.06
0.12
+
+
0.84
0.38
0.12
+
+
3
LIBOR-OIS spread
end of quarter obs
Household
credit/disposable
income, absolute gap
from rolling window HP
trend
4
LIBOR-OIS spread
end of quarter obs
Household credit/GDP
0.84
0.44
0.15
+
+
5
LIBOR-OIS spread
end of quarter obs
Credit to non financial
companies/ GDP,
absolute gap from
recursive HP trend
0.84
0.2
0.17
+
+
13
5. Conclusions
In this paper we built early warning models for identifying the time period when European
macroprudential authorities should react to increase (build-up phase) or reduce (release
phase) the CCB, in order to strengthen the resilience of the banking sector to potential
shocks resulting from a period of excessive credit expansion and to reduce the amplitude
of the credit cycle. We used the signal extraction methodology to evaluate the optimal
crisis threshold and the predictive power for a wide range of economic and financial
variables in the EU 28 member states. We found the dynamics of household credit to GDP
(or income) gap, real M3 growth, the current account deficit as a percentage of GDP,
residential property prices gap, the real growth rate of equity prices and the inverse of long
term interest rates to be leading indicators for the likelihood of a systemic banking crisis
occurring in the medium term (4-20 quarters ahead). As regards the probability of the
occurrence of a systemic banking crisis in the short term (1-4 quarters ahead), risk
aversion indicators, such as LIBOR-OIS spread or the ECB’s composite indicator of
systemic stress, the dynamics of real government bond yields and the corporate credit to
GDP gap have the best predictive power. The confidence intervals of the optimal threshold
and the NS ratio for some of the estimated models are relatively wide, implying that
prudence should be exercised when interpreting the signals issued by the leading
indicators at individual country level.
End Notes
1 - Non-performing loans above 20% of total assets or the liquidation of a number of
banks that exceeds 20% of total banking sector’s assets.
2 - Borio and Drehmann, 2009, restrict the optimization of the NS function to a minimum hit
rate of 2/3, in order to avoid the possible cases, where the minimum value of the NS
function occurs when both the noise and the hit rate are at low levels.
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