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WP/ 9 /2014
Working Paper
FINANCIAL CYCLE OF INDONESIA –
POTENTIAL FORWARD LOOKING ANALYSIS
Cicilia A. Harun, Aditya Anta Taruna, R. Renanda Nattan,
Ndari Surjaningsih
Desember, 2014
Kesimpulan, pendapat, dan pandangan yang disampaikan oleh penulis dalam
paper ini merupakan kesimpulan, pendapat dan pandangan penulis dan bukan
merupakan kesimpulan, pendapat dan pandangan resmi Bank Indonesia.
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Financial Cycle of Indonesia – Potential Forward Looking
Analysis*
Cicilia A. Harun†, Aditya Anta Taruna‡, R. Renanda Nattan§,
Ndari Surjaningsih**
Abstrak
Kebutuhan akan referensi yang baik dalam rangka implementasi
peraturan countercyclical menjadi alasan utama dari penelitian untuk
mengkonstruksi siklus keuangan. Penelitian ini merupakan penelitian
lanjutan Alamsyah et al (2014) yang telah menghasilkan siklus keuangan
Indoneisa dengan mengikuti tata cara pembuatan yang dilakukan oleh
Drehman et al (2012). Siklus keuangan pada penelitian ini akan
diperbaharui dengan penggunaan harga aset dan cara pengolahan data
lanjutan. Untuk meningkatkan kepercayaan dalam penggunaan siklus
keuangan sebagai referensi kebijakan di masa yang akan datang,
penelitan ini akan melakukan forecasting. Hasil dari forecasting
menunjukan bahwa siklus keuangan cukup robust dan cenderung untuk
mengikuti pola masa lalu. Hal ini memberikan dorongan untuk penelitian
terkait karakteristik dari siklus keuangan dan indikator tambahan sebagai
referensi untuk dapat menangkap kemungkinan terjadinya perubahan
struktural di masa yang akan datang.
Keywords: Financial cycle, countercyclical capital buffer,
financial crisis.
JEL Classification: G1, G2, F3
* Pendapat dan kesimpulan dalam paper ini merupakan pendapat penulis dan bukan
merupakan pendapat resmi dari Bank Indonesia. Penulis mengucapkan terima kasih kepada Dadang Muljawan, peneliti ekonomi senior, Departemen Kebijakan
Makroprudensial Bank Indonesia atas kontribusinya dalam memberikan metodologi untuk menganalisa siklus untuk keperluan forecasting. Tabel dan grafik merupakan
hasil pengolahan oleh penulis, terkecuali jika dinyatakan berbeda. † Peneliti Ekonomi Senior, Departemen Kebijakan Makroprudensial, Bank Indonesia,
email: [email protected] ‡ Peneliti Ekonomi, Departemen Kebijakan Makroprudensial, Bank Indonesia, email:
[email protected] § Research Fellow, Departemen Kebijakan Makroprudensial, Bank Indonesia, email:
Peneliti Ekonomi Senior, Departemen Kebijakan Makroprudensial, Bank Indonesia, email: [email protected]
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Financial Cycle of Indonesia – Potential Forward Looking
Analysis††
Cicilia A. Harun‡‡, Aditya Anta Taruna§§, R. Renanda Nattan***,
Ndari Surjaningsih†††
Abstract
The need to have a good reference for implementing countercyclical
measure has been the motive behind research for constructing the
financial cycle. The paper carries over the result done in Alamsyah et al
(2014) that constructed the financial cycle of Indonesia following the steps
done in Drehman et al (2012). The cycle is improved with the inclusion of
asset price and more advanced data treatment. In order to have better
confidence in using the cycle for a reference toward the policy that will be
implemented into the future, the paper also exercises forecasting. The
forecasting result shows that the cycle is quite robust and tends to be
persistently following the pattern formed from the history. This suggests
careful study toward the characteristics of the cycle and additional
indicators as references in order to capture the possibility of structural
break in the future.
Keywords: Financial cycle, countercyclical capital buffer, financial
crisis.
JEL Classification: G1, G2, F3
†† The opinions and conclusions written in this paper are of the authors and do not reflect
the stance of Bank Indonesia. Authors are grateful for the contribution of Dadang
Muljawan, Senior Economic Researcher of Macroprudential Policy Department, Bank
Indonesia for suggesting the methodologies for analyzing the cycles for forecasting
exercise. Tables and figures are authors’ calculations unless stated differently. ‡‡ Senior Economic Researcher, Macroprudential Policy Department, Bank Indonesia,
email: [email protected] §§ Economic Researcher, Macroprudential Policy Department, Bank Indonesia, email:
[email protected] *** Research Fellow, Macroprudential Policy Department, Bank Indonesia, email:
[email protected] ††† Senior Economic Researcher, Macroprudential Policy Department, Bank Indonesia,
email: [email protected]
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I. INTORDUCTION
It is well documented that the wide-spread impact of financial crises has
provided a motivation to launch several research works to identify the best indicators
to measure systemic risks and measure any indications that may be interpreted as
a rising probability of crisis. Several stress indicators have been developed by
financial authorities and researchers to help determine the state of the financial
system in order to anticipate a build-up of systemic risk that would allow the
financial authorities to prescribe risk mitigation steps early enough before the risk
escalates and materializes into crisis condition. However, the research on economic
crises having been done for several decades is still playing catch up with the crisis
events as economists are still figuring out the best way to have an early warning
exercise to avoid disastrous crisis to ever happen again.
The construction of a cycle to determine the state of the economy is already a
common practice for macroeconomic analysis. This cycle is known as business cycle
(Burns & Mitchell, 1946). However, the financial sector is always a missing piece in
most macroeconomic general equilibrium models. Even when it is included in a
macroeconomic model, financial sector is not considered as having a major role in
determining the state of the economy (e.g. Bernanke et al, 1999). It merely acts as a
wedge in the economy that slightly alters the path toward the equilibrium. The effort
to incorporate the financial sector within a macroeconomic model only started in the
late 2000s with the development of DSGE (Dynamic Stochastic General Equilibrium)
models. Some banking models in the past tend to consider more of the cross sectional
analyses result (e.g. Diamond & Dybvig, 1983). The latest Global Financial Crisis
(GFC) of 2007 - 2008 revealed the gap in the research of time series analysis of the
state of the financial system that needs to be close before the wave of financial
distress can pose another crisis event. The surge of research in financial cycle done
by scholars at the Bank for International Settlements tried to do exactly that,
especially in answering the need to have a reference to support the Basel III
countercyclical capital buffer policy (see Drehman et al, 2012; Borio, 2012; BIS 2010)
Countercyclical capital buffer (CCB) is raised during good time or boom and
released during bad time or bust. This is basically means that banks are required to
maintain additional level of capital during good time in order to have a larger risk
absorbing capacity during bad time. Financial authorities are encouraged to set a
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higher capital requirement during good time so that it can be lowered during bad
time. The CCB will provide wedge against risk taking behavior of banks when they
are facing boom. The lowered CCB (to the point it can be zero) during bust is aimed
to provide ‘room to breathe’ for banks so that they can still carry out their
intermediation function and not provide further distress to the economy. In other
words, CCB is a Basel III prescription to reduce the procyclicality of banks (see Borio
et al, 2001).
The CCB is suggested because banking crisis tends to be preceded with a
period of risk build up during which banks tend to expand their credit. On the other
situation, during downturn banks tend to exacerbate the crisis by reducing their
exposure from any intermediation and financing activities. In operationalizing the
CCB, the mechanism to determine the periods of boom and bust becomes important.
Do financial institutions follow business cycle or other cycles? What about financial
markets? Drehman & Borio (2009) provided the first hint of the construction of
financial cycle. They began with the fact that historically, unusually strong increases
in credit and asset prices have tended to precede banking crises. They use
combination of credit gap, asset price indicators (i.e. stock indices, housing price)
and a set of thresholds to signal banking distress before it materialized as banking
crisis. This combination turned out to be performing quite well as an early warning
exercise for banking crisis.1
Drehman et al (2012) provided a seminal paper on the construction of
‘financial cycle’. The paper delivered a financial cycle that was considered best to
represent the definition in Borio (2012) that is the self-reinforcing interactions
between perceptions of value and risk, attitudes towards risk and financing
constraints, which translate into booms followed by busts. This definition is also
close to the very definition of procyclicality. The length of a full financial cycle is
longer than a full business cycle. It is very likely that a financial cycle pass through
more than one business cycle. This is considered more realistic since the frequency
of financial crises is smaller than the frequency of booms in the economy. Drehman
et al (2012) found that fort the U.S. the length of business cycles is 1 to 8 years, while
it is 8 to 30 years for financial cycles. The peaks of financial cycles are associated
with the events of financial crises. This makes financial cycle one of the important
references for determining the timing of setting the CCB.
1 Early Warning Exercise requires the indicator used to identify the risk of crisis with a lead sufficient to allow the authorities to take remedial actions.
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Alamsyah et al (2014) has constructed the financial cycle for Indonesia using
narrow and broad credit indicators (credit-to-GDP and credit growth). The paper
found the length of financial cycles in Indonesia (9 to 10 years) to be double the
length of the business cycles. The financial cycle using broad credit follows the
financial cycle using narrow credit. The paper also found that the amplitude of the
financial cycle is smaller after the 1997-1998 crisis. This last conclusion fits the fifth
empirical finding in Drehman et al (2012) that came out of the view in Borio & Lowe
(2002) that the amplitude, length and potential disruptive force of the financial cycle
are closely related to the financial, and possibly also monetary, regimes in place.
Indonesian financial system has significantly evolved after the East Asian crisis in
1998 after going through banking restructuring program and economic reform.
This paper is dedicated mainly for two purposes: 1) to enhance the
construction of financial cycle in Alamsyah et al (2014); and 2) to exercise a forward
looking analysis in order to increase the confidence of using the financial cycle as an
early warning exercise and reference for CCB mechanism. The enhancement of the
cycle construction involve the inclusion of structural breaks treatments to the
constructing indicators as well as the inclusion of the asset price indicators as
Drehman et al (2012) did suggest the minimum set of indicators to be indicators on
credit and asset price. The methodology for constructing the cycle follows closely
Drehman et al (2012), with some country-specific considerations and modification in
the weighting to differentiate the contribution of each indicator into the financial
cycle. The result of the financial cycle is not significantly different from the cycle
generated in Alamsyah et al (2014). The timing of both cycles is similar. The
difference comes in the amplitude that can be caused by the differences of the base
year of the normalization of data. The similarity is actually by construction since the
cycle downplays the influence of asset prices as we decided that credit indicators
should play more role in determining the financial cycle as the banking system still
dominates the Indonesian financial system. The decision is also backed up by the
fact that the asset price indicators included here usually influence the financial cycle
in a higher frequency domain, so that it is likely to be truncated from the cycle as it
is focused on the medium frequency domain.
The second objective of this paper is to provide forecasting exercise in order to
increase the confidence of the macroprudential authority in using the financial cycle
as one of the reference to set the CCB. The construction of the financial cycle is such
that an additional point of observation will alter the entire series of cycles. The
forecasting exercise provides a predictive power to the cycle in order to increase the
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number of observations into the future so that we can reconstruct the cycle using
the new points and therefore provide more information to decide on the CCB setting.
The rest of the paper will be arranged as the following. Chapter 2 will be about
the construction of the financial cycle emphasizing on the differences done in this
paper to enhance the result in Alamsyah et al (2014). The theoretical background of
the determinants of the financial cycle will be discussed in Chapter 3. Chapter 4 is
the forecasting exercise. Finally, Chapter 5 concludes.
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II. CYCLE CONSTRUCTION
The financial cycle is constructed from individual indicators as suggested in
Drehman et al (2012), which will be treated in order to be used as input to filtering
mechanism. The results of the filtering of all the indicators will be combined to
create a common cycle, which then will be called the financial cycle. As it is done in
Alamsyah et al (2014), there will be two filters discussed in this paper: the
frequency-based filter (FBF) and the turning point analysis (TPA), and therefore
there will be two financial cycles produced using different methods of combining.
However, the two financial cycles should be used to reconfirm each other instead of
contradicting each other.
FBF produces a cycle from which peaks and troughs can be identified. The
identification can be made through visual judgment when plotting the cycle or
through a computer program. On the other hand, TPA only presents position of
peaks and troughs. Both FBF and TPA are able to produce short and medium term
cycle. FBF and TPA are explained in more details bellow.
Frequency-Based Filter
The main idea of this analysis is to isolate a specific range of frequency of
macroeconomics data. FBF analysis makes use a band pass filter which is a
combination of high and low pass filter. Data is first changed from time domain to
frequency domain using Fourier Transformation then the filter process takes place,
passing only frequency higher than the low frequency threshold and lower than the
high frequency threshold.
Frequency threshold means the intended cycle length, with higher frequency
corresponds to lower threshold in time domain and vice versa. According to Comin
and Gertler (2003), who studied the behavior of medium-term macroeconomics for
the US economy, a band pass filter with duration of 5 to 32 quarters is used to isolate
a short-term cycle, which is popularly known as a business cycle. The duration of 32
to 120 quarters is used to isolate a medium-term cycle. Due to the availability of
economics data in Indonesia, the duration of a medium-term cycle is adjusted to 32-
80 quarters2.
The band pass filter employed here is suggested by Christiano and Fitzgerald
(1999) and the data filter is in annual growth rate. Under the assumption that the
2 This is also done in Alamsyah et al 2014.
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growth rates of macroeconomics series are stationary, the filter thus implies zero
trend (or drift). The frequency-based filter analysis in this paper is done using Eviews.
Turning Point Analysis
TPA yields peaks and troughs of a cycle trough Bry-Boschan (BB) Algorithm.
BB algorithm first identifies potential peaks and troughs which are higher and lower
respectively compared to their surroundings. Then potential peaks and troughs will
be subjected to various tests before final peaks and troughs are established.
In the first step, a potential peak is identified at time t if it obeys the rule (yt −
y(t−i)) > 0, with 𝑖 = (−2, −1, 0, 1, 2) for short term cycle while 𝑖 =
(−4, −3, −2, −1, 0, 1, 2,3,4) for medium term. Similarly, a potential trough occurs at time
t if it obeys the rule (yt − y(t−i)) < 0 with 𝑖 = (−2, −1, 0, 1, 2) for short term cycle while
𝑖 = (−4, −3, −2, −1, 0, 1, 2,3,4) for medium term.
Potential peaks and troughs will then be examined under censoring rules.
Censoring rules ensure that length of a phase (from peak to trough and vice versa)
and a cycle (from peak to peak or from trough to trough) meet the minimum
requirement. For short term cycle, the minimum length for a phase is 2Q and a cycle
is 5Q while for medium term cycle, the minimum length for a phase is 9Q and a cycle
is 20Q. Peaks and troughs resulted from turning point analysis will not change
though new data is added unlike frequency-based filter analysis. Data addition leads
to frequency addition thus alters the output in frequency domain.
Base Year
Normalization forces data to have normal distribution with zero mean and standard
deviation at time data used as base year.
𝐼 =𝑥𝑡 − �̅�
𝜎
In term of index range we can rewrite normalization formula as function of maximum
and minimum, so the formula will evolve to:
𝐼 =𝑥𝑡 − 𝑥𝑚𝑖𝑛
𝑥𝑚𝑎𝑥 − 𝑥𝑚𝑖𝑛
In the case we are going to compare to only a certain time in data, base year
commonly only uses one time as base year then 𝑥𝑚𝑖𝑛 equals to zero, which comply to
normal distribution function perquisite, and 𝜎 = 𝑥𝑚𝑎𝑥 − 𝑥𝑚𝑖𝑛 equals to the data value
at time used as base year. Further, the standard deviation in normal distribution (𝜎)
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can be calculated as 𝜎 = 𝑥𝑚𝑎𝑥 (where 𝑥𝑚𝑖𝑛 = 0). Applying all mathematical
manipulation into normalization, base year can be calculated using:
𝐼 =𝑥𝑡
𝑥
�̅� = 𝑥𝑚𝑖𝑛 = 0
The mathematical expression shows that base year method forces data to have
zero mean at time used as base year and standard deviation at time used as base
year. Judging the philosophy of base year, forcing data has to change “real mean” to
“normal distribution mean” (zero), we have to be sure that a significant alternation
to mean happen in the data before determine which time used as base.
2.1. Data, Indicators and Treatments
According to Aikman et al (2010) financial cycle can be illustrated from credit
cycle composed only by credit. While Minsky (1982), Kindleberger (2000), and
Claessens et al (2011) suggested financial cycle to be represented by the combination
of property prices and credit. Drehman et al (2012) and Borio (2012) suggested the
minimum indicators used in a financial cycle are credit representing funding risk
and asset price representing price and risk perception. Drehman et al (2012) used
five financial variables: (i) credit to private, non-financial sector, (ii) the ratio of credit
to GDP, (iii) equity prices, (iv) residential property prices, (v) an index of aggregate
asset prices. Referencing to the study, financial cycle in Indonesia will be composed
by those variables yet certain adjustments are to be made due to data availability.
The inclusion of asset price indicators in this paper is the first enhancement from
the construction of financial cycle done in Alamsyah et al (2014).
The indicators used to represent the financial cycle in Indonesia are broad
credit (BC), ratio of broad credit to GDP (BC/GDP), Jakarta Composite Index (JCI),
and Jakarta Property Index (JAKPROP). The definition of BC follows Alamsyah et al
(2012). JCI constitutes equity prices while JAKPROP proxies residential property
prices. Indonesia residential property prices use more than one base year with
different number of cities surveyed thus converting the data to one common base
year is not possible. JAKPROP represents the prices of the stocks of the companies
in property sector, which is considered a good proxy for the movement of property
price in Indonesia. Business cycle is commonly represented by GDP. The table below
summarizes variables used for financial and business cycle.
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Tabel 1. The Constructing Indicators of the Financial Cycle
Source : Bank Indonesia, Bloomberg, OJK
Data are recorded quarterly and available from 1993Q1 until 2014Q1. Broad
credit is preferred to narrow credit because government foreign debt and outstanding
corporate bond are major sources for credit in Indonesia3. Banking credit to GDP is
varying around 30% in Indonesia, which strengthens the reason to use Broad Credit
indicator in this case. JAKPROP is the composite stock price of listed property
companies in Indonesia introduced in 1996. Data for JAKPROP with a number of
companies in property sector from 1993Q2 to 1995Q4 is calculated using the formula
below
𝑗𝑎𝑘𝑝𝑟𝑜𝑝𝑡 = ∑𝑝𝑡𝑖 × 𝑐𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑠𝑎𝑡𝑖𝑜𝑛𝑡𝑖
𝑡𝑜𝑡𝑎𝑙 𝑚𝑎𝑟𝑘𝑒𝑡 𝑐𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑠𝑎𝑡𝑖𝑜𝑛𝑡
𝑛
𝑖=1
with 𝑝𝑡𝑖 is the stock price of property company i at t. However, the formula above
cannot be used to calculate for JAKPROP in 1993Q1 since the raw data is not
available. The data for JAKPROP in 1993Q2-1993Q4 is constructed by extending the
JAKPROP using all the stocks of property companies.
Structural break analysis
All data values needs to be normalized using a base year of a point in time to
ensure comparability of the units. Drehman et al (2012) used 1985 Q1 as the point
of reference to normalize his data since 1985 Q1 is the financial liberalization in the
western world. The point of reference is determined such that it is the point in time
which data characteristics is altered, called a structural break. In Indonesia case,
the point of time for each indicator is detected using both Quandt-Andrew Test and
Chow Test for possible structural breaks as shown in a table below.
Table 2. Structural Break Candidates
3 Alamsyah et al 2014 provides a discussion on the comparison of using narrow credit and broad credit data for Indonesia case.
Variables Details Source
Broad Credit Nominal is sum of:
1. Narrow Credit Bank Indonesia (SPI)
2. Government Foreign Debt Bank Indonesia (DSTA)
3. Outstanding Corporate Bond CEIC-OJK
Broad Credit Nominal same as above
GDP Nominal Bank Indonesia (PPDI)
JCI Jakarta Composite Index Bloomberg
Jakprop Jakarta Propery Index Bloomberg
GDP Real Bank Indonesia (PPDI)
BC
BC / GDP
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* significance at 0% ≤ 𝛼 ≤ 1%
** significance at 1% < 𝛼 ≤ 5%
*** significance at 5% < 𝛼 ≤ 10%
Despite being proved to be one of the structural breaks in JCI and JAKPROP,
2009 Q2 will not be used as one of the base years used because the levels of
significance of 2009Q2 in Quandt-Andrew Test and Chow Test are higher or equal to
10%. Authors decided to use four potential structural breaks: 1) 1998 Q3; 2) 2004
Q2; 3) 2011 Q1; and 4) 2007 Q4. All of the structural breaks will be used as the point
of reference to normalize all the data. Comparisons and observations will be made to
judge which of the individual structural breaks will be used. The use of structural
break analysis also follows the suggestion of Drehman & Tsatsaronis (2014) about
the use of the analysis for Indonesia data.
The data of every indicator has to go through series of treatments before it is
analyzed using both frequency-based filter and turning point analysis. The various
treatments are listed below, however not necessarily applied to all indicators used in
constructing the financial cycle.
1. Seasonal Adjustment (SA): SA is applied on data level of all variables using
Eviews.
2. Logarithm (log): Log is applied to all variables except for ratio variables.
3. Normalization: The point of time used to pivot the data is one of the structural
dates of the variables.
4. Taking the growth
Data input to both band pass filter and turning point analysis is growth data.
Should the data have been in log, annual growth can be approximated by differencing
four quarterly data. On the other hand, common growth formula is applied. Various
sets of treatments can be arranged from the above list and choosing the most suitable
series of data treatment is crucial in capturing the natural characteristics of the data
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and more importantly should not create pseudo or unnatural characteristics to the
data. There are two sets of series of data treatments that have met the criteria
mentioned earlier.
First procedure: Data Level SA Log Normalization Differencing
(annualized)
Second procedure: Data level Growth Normalization
In Alamsyah et al (2014), the first procedure is used to process the data.
However, in this paper the second procedure is employed in processing the data as
we believe that data in growth do not have to be converted into log as they were
assumed to be stationary as the treatment used by Drehman et al (2012) and Comin
& Gertler (2003). Bearing in mind that the purpose of producing financial cycle is to
capture the perception of the people about the economy, applying SA will only
eliminate the seasonal outlier which can mean the people’ response on occasions.
Thus in this paper, SA will not be applied.
After normalization, data is ready for input to both frequency-based filter and
turning point analysis, as illustrated below. Under FBF, output of band pass filter
will be processed under BB algorithm for consistency checking. Output of BB
algorithm under this analysis will not be the same as under TPA. FBF produces a
cycle with peaks and troughs while TPA can only deliver peaks and troughs.
Frequency-based analysis: data (normalization) band pass filter cycle
Turning point analysis: data (normalization) bry boschan peaks and
troughs
Concordance Index (CI)
A selection among variables is needed to decide which variables will be used
to compose the financial cycle. Variables that do not co-move with the most potential
variable will cancel out the potential peaks and troughs of the financial cycle while
variables that co-move will reinforce the potential peaks and troughs of the financial
cycle. An index from Harding and Pagan (2006) called concordance index can
measure the co-moving degree of a variable toward another variable. This index does
not only measure the linearity of two variables but also the cyclicality thus it is
completely different from correlation. The index has a range value of 0% to 100%
with increasing index indicating better co-movement between two variables.
Before CI between two variables can be calculated, each variable has to
undergo FBF or TPA to obtain peaks and troughs of its cycle. An expansion phase is
defined to be an area ranging from after a trough to a peak and a contraction is an
13
area starting from after a peak until a trough. The concordance index, 𝐶𝐼𝑥,𝑦 between
variables x and y can be calculated using:
𝐶𝐼𝑥,𝑦 =1
𝑇∑[𝐶𝑡
𝑥 ∙ 𝐶𝑡𝑦
+ (1 − 𝐶𝑡𝑥) ∙ (1 − 𝐶𝑡
𝑦)]
𝑇
𝑡=1
where
𝐶𝑡𝑣 = 1, 𝑖𝑓 𝑣 𝑖𝑠 𝑖𝑛 𝑒𝑥𝑝𝑎𝑛𝑠𝑖𝑜𝑛 𝑝ℎ𝑎𝑠𝑒 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑡, 𝑣 = 𝑥, 𝑦
𝐶𝑡𝑣 = 0, 𝑖𝑓 𝑣 𝑖𝑠 𝑖𝑛 𝑐𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑝ℎ𝑎𝑠𝑒 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑡, 𝑣 = 𝑥, 𝑦
Combining the series
FBF will produce four individual cycles which are BC cycle, BCGDP cycle, JCI
cycle, and JAKPROP cycle. These four series can be combined to create a FBA
common financial cycle. The same process applies to TPA. However, the output of
FBF analysis and TPA are different hence giving rise to different methods used for
combining the series.
Frequency Based Filter
Individual cycles of indicators from FBF output are weighted to produce the
common cycle. Drehman et al (2012) and Alamsyah et al (2014) used equal weight
when producing the common cycle. In this paper, every individual cycle will have
different weight. Bigger weight means higher synchronization with the rest of the
indicators thus is rewarded with bigger role in composing the common cycle. This
will ensure that indicators will reinforce the presence of peaks and troughs.
Calculating the weight (𝑌𝑖) of an indicator involves 𝐶𝐼 of the indicator with the rest of
the variables.
𝑌𝑖 =𝐶𝐼𝑖,𝑗 + 𝐶𝐼𝑖,𝑘 + 𝐶𝐼𝑖,𝑙
2 ∗ (𝐶𝑖,𝑗 + 𝐶𝑖,𝑘 + 𝐶𝑘,𝑗 + 𝐶𝐼𝑖,𝑙 + 𝐶𝐼𝑘,𝑙 + 𝐶𝐼𝑗,𝑙), 𝑖 ≠ 𝑗 ≠ 𝑘 ≠ 𝑙
𝑤ℎ𝑒𝑟𝑒 𝐶𝐼𝑎,𝑏 = 𝐶𝐼𝑏,𝑎 ∀ 𝑎, 𝑏 ∈ {𝑖, 𝑗, 𝑘, 𝑙}
Turning Point Analysis
For every peak (trough) in an individual series, an area of 12 quarter before
and after the peak (trough) will be marked and named for instance a grey peak
(trough) area. An overlapping peak (trough) area will be produced when all grey peak
(trough) area of all variables overlap. An overlapping peak (trough) area marks the
potential common peak (trough) of the combined cycle. Decision of potential common
14
peak (trough) area can be made based on the nearest point to the most influential
individual peak (trough) or the median of the overlapping peak (trough) area.
Crisis Period
According to Drehman et al (2012), the peak of financial cycle is often
associated with a crisis as proved in Australia, Germany, Japan, Norway, Sweden,
the United Kingdom, and the Unites States. Financial cycle can be utilized as an
early indicator of a crisis. However, as it is proven to signal incoming crisis accurately
in advanced countries, it has to be tested in Indonesia case. In order to test for
accuracy of the financial cycle produced, there has to be an exact period of crisis.
Unlike America which releases official date or period of crisis through National
Bureau Economic Research (NBER), Indonesia does not have official statement of
data or period of previous crises. We close the gap by conducting a survey of an
expert panel to canvass academicians’ and practitioners’ views on previous crisis
periods can be the solution. According to 30 respondents, crises after 2000 took
place in 2005Q2-Q4 and 2008Q3-2009Q1.
2.2. The Financial Cycle Result of Frequency Based Filter
BC, BC/GDP, JCI, and JAKPROP are processed with FBF using various base
years to yield medium term cycles. The following graphs are the results of using
different base years in each variable. Using more than one base year produces
medium term cycles with different magnitude and direction, for instance all the
cycles of all variables, except broad credit and broad credit to GDP. Medium term of
JAKPROP and JCI in base year 1 go in different direction as it produces a trough first
while the rest base years show a peak.
Figure 1. Medium Term Cycles of Broad Credit
Figure 2. Medium Term Cycles of Broad Credit/GDP
15
Figure 3. Medium Term Cycles of
JAKPROP
Figure 4. Medium Term Cycles of JCI
Besides looking at the movement of different base year cycles per indicator,
observation will be continued by analyzing the movement of different variables per
base year. The higher the degree of synchronization between the variable cycles the
better the financial cycle produced due to the reinforcement of peaks and troughs.
Despite the use of CI to measure the co-movement of variable cycle, the following
graph can serve as the visual judgment before the quantitative measure calculated.
Figure 5. All Cycles – Base Year 1 Figure 6. All Cycles – Base Year 2
Figure 7. All Cycles – Base Year 3
Figure 8. All Cycles – Base Year 4
Concordance index is used to filter which variables should be included in
composing the financial cycle. Individual series that goes in synchronization with the
other series will reinforce the peaks and troughs thus resulting in a more distinct
16
financial cycle. In this case, we set the broad credit as the main component of the
financial cycle. Therefore, other variables must have high CI with broad credit in
order to be used for composing the financial cycle. The CI between four variables for
4 base years is recorded in a table below.
Table 3. Concordance Index for Base year 1
MBCN1 MBCGDPN1 MJCIN1 MJAKPROPN1 SUM
MBCN1 100.0%
326% MBCGDPN1 65.4% 100.0%
MJCIN1 46.9% 56.8% 100.0%
MJAKPROPN1 56.8% 29.6% 70.4% 100.0%
Table 4. Concordance Index Base year 2
MBCN2 MBCGDPN2 MJCIN2 MJAKPROPN2 SUM
MBCN2 100.0%
346% MBCGDPN2 65.4% 100.0%
MJCIN2 53.1% 43.2% 100.0%
MJAKPROPN2 43.2% 70.4% 70.4% 100.0%
Table 5. Concordance Index Base year 3
MBCN3 MBCGDPN3 MJCIN3 MJAKPROPN3 SUM
MBCN3 100.0%
346% MBCGDPN3 65.4% 100.0%
MJCIN3 53.1% 43.2% 100.0%
MJAKPROPN3 43.2% 70.4% 70.4% 100.0%
Table 6. Concordance Index Base year 4
MBCN4 MBCGDPN4 MJCIN4 MJAKPROPN4 SUM
MBCN4 100.0%
346% MBCGDPN4 65.4% 100.0%
MJCIN4 53.1% 43.2% 100.0%
MJAKPROPN4 43.2% 70.4% 70.4% 100.0%
As broad credit is assumed to be the main component of financial cycle, the
filter process starts from the second column. Indicators on the first column, with CI
to broad credit less than 50% will be eliminated. Looking at Table 2.6 with base year
4, only BCGDP and JCI pass the first filter with CI to broad credit above 50%. Then,
CI of BCGDP and JCI has to be above 50% in order for both variables to be included
in the financial cycle. Since CI of BCGDP and JCI is below 50%, either BCGDP or JCI
can be included and BCGDP is preferred to JCI because CI of BC and BCGDP is
17
higher than CI of BC and JCI. However, using CI will result in elimination of
variable(s) which actually may be crucial in constructing the financial cycle.
According to CI rule, only BC and BCGDP construct the financial cycle in base year
4 forgoing the crucial role of JCI and JAKPROP. The low CI of JCI and BC/BCGDP
might be due to the shallow financial market in Indonesia at the moment. JAKPROP
is an important component to convey the perception of risk and price from the
society. Housing price, proxied by JAKPROP, also reflects the standard of living of
residents of Indonesia that will also affect the appetite toward financial products in
the market. As a result, all variables will be included and CI will not be used as a
filter but to assign weight of every variable in combining the series as the formula
shown in the previous sub chapter.
Moreover CI will be used to determine which base year of structural break
should be employed. The summation of CI of indicators per base year is shown in
the last column of every table above. The bigger the summation, the higher the degree
of synchronization between variables, and the better the financial cycle produced.
According to the tables above, the biggest summation of CI is calculated in year base
year 2, 3, and 4.
Common Cycle
First, we produce the financial cycles under FBF by averaging the cycles of the
four indicators with uniform weights. The common cycles in all base years are
presented in Figure 9.
Figure 9. Common Cycles with Uniform Weight
In accordance with the assumption that the peak of financial cycle is often
associated with incoming crisis, Indonesia financial cycle should have peaked before
1997/1998. Referring to the graph, CC at base year 1 is automatically dropped as it
is too late to serve as a signal. For now, the potential acceptable base years are 2, 3,
18
and 4 which are of the same result deducted by the elimination procedure using CI
measure.
In base year 2, 3, and 4, BC, BCGDP, JAKPROP and JCI will be used to
construct the common cycle. Instead of using the uniform weight like before, all
variables will be assigned different weight according to CI measure.
Table 7. Weight Assignment for Each Indicator
Weight Indexing
Indicator Base year
2 Base year
3 Base year
4
SUM 345.7% 345.7% 345.7%
BC 23.4% 23.4% 23.4%
BCGDP 25.9% 25.9% 25.9%
JCI 24.1% 24.1% 24.1%
JAKPROP 26.6% 26.6% 26.6%
Using the different weight for every variable, the new common cycle is plotted
in Figure 10.
Figure 10. Common Cycles with Non-Uniform Weights for Base year 2, 3 and 4
According to Figure 10, base year 2 (CC2) peaked at 1996Q4 and 2007Q3,
and troughed in 2002Q1 and 2010Q1, while base year 3 (CC3) peaked at 1996Q2
and 2007Q1 and troughed in 2001Q4 and 2009Q3. Lastly, base year 4 (CC4) peaked
at 1996Q4 and 2007Q3, and troughed in 2002Q2 and 2009Q4. The results were
tested using BB algorithm and were proven to be consistent although the phase has
been set at 5, 7, and 9Q and the cycle is at 20Q.
2.3. The Financial Cycle Result of Turning Point Analysis
TPA uses BB algorithm to produce peaks and troughs of a variable. Every
variable is processed with BB algorithm with different phases: 5, 7, and 9 quarter
19
and same cycle 20Q. Since there are only 3 potential base years according to the
previous result, the result of turning point analysis for every variable will only be
shown in 3 base years which are N2 (2004Q2), N3 (2011Q1), and N4 (2007Q4).
Figure 11. Turning Point Analysis - Broad Credit
Looking at Figure 11, different phase shows different peaks and troughs. For
instance, phase 9Q indicates 2 peaks (1998Q2 and 2008Q4) and 2 troughs (1999Q2
and 2010Q1) while phase 5Q only shows 2 peaks (1998Q2 and 2012Q2) and a trough
(2010Q1). All of the three phases indicate a peak at 1998Q2 and a trough at 2010Q1
at all three graphs.
Figure12. Turning Point Analysis - Broad Credit to GDP
Compared to BC figures, BCGDP figures (Figure 12) show more peaks and
troughs especially phase 9Q and all the three phases only agree at a peak at 1998Q2.
Phase 5Q and 7Q report the last turning point is a trough thus the incoming turning
point must be a peak intrepreted as we are in the expansion period now. On the
contrary, phase 9Q reports the last turning point to be a peak showing that we are
in the contraction period at the moment. Choosing the (most likely) correct phase is
important since different analysis will produce different policy.
20
Figure13. Turning Point Analysis – JAKPROP
Looking at Figure 13, all phases of JAKPROP indicate a common peak
(2007Q3) while all phases in BCGDP graphs show a peak (1998Q2). The peak is
associated with the global financial crisis of 2008 and the structural break of
JAKPROP, which is 2007Q4.
Figure14. Turning Point Analysis - JCI
All phases in all three base years for JCI show the same turning point
throughout the cycle.
Table 8. Standard Deviation of the Constructing Indicators
Indicator Standard Deviation
N2 N3 N4 BC 2.13 2.05 2.02 BC/GDP 3.51 7.09 14.84 JCI 0.74 1.03 0.64 JAKPROP 1.29 3.84 0.59 GDP 1.05 0.73 0.73
21
Table 9. Ratio of Standard Deviation of Constructing Indicators to GDP
Indicator Ratio to GDP
N2 N3 N4 BC 2.03 2.79 2.76
BC/GDP 3.33 9.66 20.24 JCI 0.71 1.40 0.87
JAKPROP 1.23 5.23 0.81
Table 8 and 9 provide the standard deviations and ratios of standard deviation
of each constructing indicator to the standard deviation of GDP. Ratios of standard
deviations of JCI to GDP and JAKPROP to GDP are smaller than 1, which means JCI
and JAKPROP should employ shorter phase than the other indicators. As a result,
especially for TPA, each JCI and JAKPROP will use a shorter phase length of 5Q while
other variables remain at 9Q.
Common Cycle
Authors decided to use base year 4 and eliminate base year 2 and 3. This is
based on the comparing the exercises of finding the common cycle from the results
of TPA and FBF from all the base years. Base year 4 provide a more sychronized
common cycle between the FBF and TPA results. To illustrate the process of finding
the common cycle using base year 4, we describe the following exercise.
The potential area for 2 peaks and 2 troughs are recorded in Table 10. The
potential area is resulted from 4 overlapping area of 4 indicators.
Table 10. Overlapping Area
In helping to justify the exact time for a peak or a trough to occur, turning
points of every indicator can be used as a reference. However, potential turning point
area for peak 2 and trough 2 do not contain any individual indicator peaks and
troughs because of the large spread of peaks and troughs of the individual indicators.
Table 11. Individual Indicators Turning Points
22
Individual turning points from four indicators, potential peak area, potential
trough area, and crisis are plotted in the graph bellow. There are indicators which
have common peak for instance BCGDP (orange line) and BC in 1998Q2 and have
trough such as JCI (pink line) and JAKPROP in 2000Q4. The first potential peak
area, colored red, contains crisis area colored purple, BC peak, and BCGDP peak
while the second potential peak area does not contain any peak yet exactly beside
JAKPROP peak. The first green trough area is much bigger than the second trough
area, since it contains 4 troughs: 2 in 1999Q2 and 2 in 2000Q4 yet the second area
does not have any.
Figure 15. Potential Areas, Peaks and Troughs
Figure 15 helps to determine the final peaks and troughs of common cycle
according to TPA. Final peaks and troughs are established as the median of potential
peak areas and troughs areas respectively.
Table 12. Final Peaks and Troughs of TPA
2.4. Final Common Cycle
The results of the abovementioned exercise to find the final common cycle can
be obtained in Figure 16. Frequency-based analysis yields a cycle while turning point
analysis produces peak and troughs. Peaks of turning points tend to occur after or
at the same time with peaks of frequency-based filter while troughs of turning points
tend to occur before the troughs of frequency-based filter. This means that the peak
of the financial cycle is reached before the values really reached the bottom values
23
Figure 16. Final Common Cycle
The final common cycle is generating the following summary of cycle attributes
(Table 13).
Table 13. Attributes of the Financial Cycle
Finally, in order to provide comparison of the financial cycle as the result of this
paper to that constructed in Alamsyah et al (2014), we provide Figure 17. The results
are almost similar. The only difference is the amplitude of the cycle which is basically
caused by the difference in the base year used for the normalization process in the
data treatment. By construction, the similarity should happen since the new
weighting treatment downplays the influence of asset prices as we decided that credit
indicators should play more role in determining the financial cycle as the banking
system still dominates the Indonesian financial system. This is also backed up by
the fact that the asset price indicators included here usually influence the financial
cycle in a higher frequency domain, so that it is likely to be truncated from the cycle
as it is focused on the medium frequency domain.
24
Note: In this figure, FC1 is the financial cycle produced by Alamsyah
et al 2012, FC2 is that of this paper. Both are using Frequency-
Based Filter.
Figure 17. Comparing the Cycles
The timing of peaks and troughs generated from the financial cycle using FBF
in this research (FC2) is slightly different from that from Alamsyah et al (2014) (FC1).
Using FBF, FC2 shows the same peaks and troughs with FC1, except for the second
trough yet with only one quarter difference. On the other hand, there are no exact
peaks and troughs shown by FC1 and FC2 employing TPA. This can be resulted from
the inclusion of the asset price data so that it influences the decision of the common
turning points in the overlapping peak or trough areas. Nevertheless, the difference
peaks (troughs) shown by FC1 and FC2 using TPA are either two or three quarters.
The complete comparison of the peaks and troughs is shown in Table 14.
Table 14. Peaks and Troughs Comparison
Note: In this table, FC1 is the financial cycle produced by Alamsyah
et al (2014), FC2 is that of this paper.
25
Special mentions on the terms of the financial cycle
Drehman et al (2012) mentions that the medium term is much more
meaningful when we see that the standard deviation of an indicator in the medium
term is larger than that in the short term. However, a quick check to the standard
deviation (see Table 2.15) reveals that this is not the case in Indonesia. In other
words, the ratio of the standard deviation of the medium term to the standard
deviation of the short term is less than 1.
Table 15. Checking the Standard Deviations
The condition can be explained as the following.
1. The role of the shorter term of the financial cycle of Indonesia may still be
important in determining the course of the cycle. Possible reason for this is the
fact that credit-to-GDP ratio is still small (around 30%) compared to that from
developed economies. This can also be explained by the shallowness of the
financial products.
2. Drehman et al (2012) is using the medium term of 8 to 30 years. For Indonesian
data, because of data limitation, we cannot go upto 30 years. We use a maximum
of 20 years instead. This may result in a smaller average amplitude for medium
term cycle.
3. We did some exercises of filtering longer than 20 years: (a) 20 to 80 years: the
ratios of the standard deviations are larger but mostly still below 1; (b) 40 to 80
years reveals smaller ratios; (c) 32 to 80 years excluding the period between
1997q3 and 2000Q1 (crisis and recovery period): the ratio for BC is slightly above
1, and the ratio for BC/GDP is close but still below 1.
26
III. DETERMINANTS OF CYCLES
In order to have a forward looking analysis, we need to have a well-designed
model for forecasting exercise. Since the literatures that cover the forecasting
financial cycle has yet to emerge as research on financial cycle is still very new, we
have to rely on our own innovation to determine the best models to use for forecasting
the financial cycle. This will involve trial and errors exercises using a few forecasting
models available that may best suit the characteristic of the exercise. In this case,
we use Univariate and Multivariate Bayesian Vector Autoregressive (BVAR) and
Univariate and Multivariate Ordinary Least Square.
3.1. Independent Variables
For the multivariate estimation, we choose a few macroeconomic indicators
that can influence the perception of market players and financial indicators. We
determine a set of indicators comprising current account, CDS, Third Party Fund,
Exchange rate, Financial Account of the Balance of Payment, GDP or Income per
capita, and M2/GDP.
Current account is included to represent the balance of payment condition of
the country as this is the indicator that is usually referred by the global investors
when considering one country for investment destination. CDS is included to
represent the foreign investors’ perception toward domestic financial system. The
third party fund represents the source of financing for the economy. Exchange rate
is expected to influence not only market sentiments but also represent the automatic
adjustment of the financial system based on the fundamentals of the economy and
the spillover from the global markets. Financial account represents the additional
source of financing that came from outside the country, and therefore can be used
as a substitute for financing from bank credits or from foreign debts. GDP and
Income per capita takes into account the business cycle influence toward the
financial cycle. Lastly, M2/GDP represents the depth of the financial system.
With the exeption of CDS and exchange rate, all indicators are expected to
provide positive impact toward the financial cycle. CDS should provide the opposite
sign. The exchange rate is ambiguous in this case since the impact will depend on
how large the exposure of the financial system to exchange rate risk. Some financial
institutions or market players may gain profit – and therefore causes the expansion
of financial cycle – from exchange rate depreciation, while others may experience loss
– and therefore causes the contraction of financial cycle. However, exchange rate is
27
deemed necessary to be included here in order to proxy the impact of the forex market
exposure toward the financial cycle.
One may think that interest rate is a good candidate that can be included as
an independent variable. However, it is considered that interest rate will work
through Third Party Fund and M2/GDP. Authors also consider including banking
capital as an independent variable, especially when we want to ensure that additional
CAR will have an impact in the financial cycle. However, the decision on the level of
CAR usually depends on individual bank need. When CCB is on, for example, some
banks may need to increase the level of capital, some may not, although the increase
of CCB does reduce the bank’s capacity to expand credit. Therefore, authors decided
to focus on the macrofinancial indicators for the independent variables.
In this part we exercise signal correspondence between independent variables
and dependents variables. Because the financial cycle is in filter form, the
independent variables have to be in the forms of filtered data as well. The exercise
conducted using Bayesians Vector Autoregressive, BVAR. In real time, a data value
is not exactly the real value when the data is captured. In other word, the value of
data depends on when and how we capture the data. Muljawan et al (2013)
mentioned that time lag should be kept small particularly to the signals that involves
high frequency or small time period. Lack of integrity to the data capturing process
carries the risk of the system become unstable with the possibility of generating the
wrong policy prescription.
Each data has its own characteristics. In this paper we see the characteristics
from its probability distribution. The probability of the data to resemble the real value
is highly dependent on its historical behaviour and the uncertainties surrounding
the data sources. The uncertainties can be in the form of market behavior, changes
in policy rate, natural disaster, etc.
The financial cycle was constructed using our data set. The cycle can be
different when produced with other data set. The way the data was captured is really
important when constructing financial cycle. The probability distribution of the
captured data is best captured when we see each variable’s behavior using BVAR.
In frequency domain, any information contained in the data will determine
data characteristics. One simple way to read the frequency domain is whichever the
data is sensitive to shock in a short period or a long period. Figure 3.1 shows the
differences between two data with low frequency and high frequency response4. The
4 The data transform into frequency domain using Laplace, for further information about Laplace please refer to Bryant (2008).
28
credit (interest) rate data presented monthly from Monthly Banking Report shows
that the data response in the frequency domain is quite insensitive. A shock to credit
rate will be responded longer than one month. In case of interbank money market
(interest) rate, any shock to the interbank market will cause market to move in
minute window. In Indonesian interbank money market, in order to provide effective
influence, the shock will have to be responded in approximately ten minutes after.
Source: Muljawan et al (2013)
Figure 18. Data Behavior in Frequency Domain
The data used in the estimation model are in filtered form, which basically
means the data had been truncated (filtered) using FBF. In signal language, the data
have lost information in the range of a particular frequency that was used as
parameter in the filtering process. The information contained in the data is important
as this information may change the coefficients in the estimation. There is a
possibility that the truncated information in the filtered form actually provides some
information, but it is not represented the relationship between independent and
dependent variables.
Drehman et al (2012) did mention that all indicators, including constructed
financial cycles and models are subject to error and the future is, by no means,
unknown. We can say the uncertainty is a big factor when seeing and constructing
the model. The paper also mentioned that the result of data filter is influenced by the
starting period and ending period as well as the lower bound and upper bound of the
filter. Forecasting a data that have to be constructed from a certain set of data will
not represent the ‘true’ forecasted data. Remember that to construct a filtered data
29
we have to recalculate from all the data sample. Basically, the 𝑏(𝐿) is calculated as a
sum of each data in the sample. Ideally, band pass filter (or frequency base filter) is
constructed using following formula
𝑦𝑡 = 𝑏(𝐿)𝑥𝑡
Where
𝑏(𝐿) = ∑ 𝑏ℎ𝐿ℎ
∞
ℎ=−∞
, 𝐿ℎ𝑥𝑡 = 𝑥𝑡=ℎ
The BVAR will be used as signal processing between dependent and independent
variables rather than forecasting the financial cycle. Information loss in the data will
make the forecasting process more of a signal processing.
Biases are something that we have to take into account when seeing a data.
The value of the data as mentioned before resembles the data only if the data
captured in a perfect conditions: within a certain time window and using a high
integrity data capture methods. The distribution for each data can be a good lead as
to how the data will influence other data behavior, if these two sets of data are
believed to be related with each other. The prior belief about the data connectivity
should be able to be translated into the economics model.
Bayesians Vector Autoregressive (BVAR) treats the dependent and
independent variables as variables with a distribution of a fixed sample of data. The
estimates are being calculated by the simulation and the distributions can be used
to evaluate forecast uncertainty. The estimator in BVAR uses linear regression
estimator. Equation (1) shows basic autoregressive formula. The variable B is to be
estimated based on historical data set.
𝑌 = 𝐵𝑋 + 𝑣 (1)
The B will be estimated using likelihood between 𝑌 and 𝐵. The classic
approach of the likelihood can be writen as follow
𝐹(𝑌|𝐵) = (2𝜋𝜎2)−𝑇/2𝑒𝑥𝑝 (−(𝑌 − 𝐵𝑋)′(𝑌 − 𝐵𝑋)
2𝜎2)
(2)
If we maximize the formula (2), the 𝐵 can be estimated as follow
𝐵 = (𝑋 ′𝑋)−1
𝑋 ′𝑌 (3)
In Bayesian analysis, the subjective belief of variable B is calculated in the
estimation of 𝐵. The prior belief is then translated as a distribution called prior
distribution 𝑃(𝐵) where B is belief to be normally distributed, 𝐵~(𝐵0, Σ0). By using
30
the assumption, the prior distribution will be corrected after using conditional
posterior distribution. The conditional posterior is calculated using combined data
and sample information using the following formula.
𝐻(𝐵|𝑌) 𝛼 𝐹(𝑌|𝐵) × 𝑃(𝐵) (4)
The distribution of the posterior 𝐻(𝐵|𝑌) conjugated with the likelihood 𝐹(𝑌|𝐵)
will result the same distribution of the posterior as the prior distribution. This means
the result will always depict a normal distribution. The posterior conjugated can be
described as follow:
𝐻(𝐵|𝑌, 𝜎2)~𝑁(𝑀∗, 𝑉∗) (5)
Where
𝑀∗ = (Σ0−1 +
1
𝜎2𝑋 ′𝑋)
−1
(Σ0−1𝐵0 +
1
𝜎2𝑋 ′𝑌)
−1
(6)
𝑉∗ = (Σ0−1 +
1
𝜎2𝑋 ′𝑋)
−1
(7)
In 𝑀∗ formula, note that 𝐵𝑂𝐿𝑆 = (𝑋′𝑋)−1
𝑋′𝑌 , is a weigthed average of the prior
and OLS. Without the prior belief (prior distribution used in estimating the 𝐵)
formula (6) is simply OLS estimation.
3.2. Signal Processing in Financial Cycle
In this section we will try to do the signal processing using OLS and BVAR. Both
methodologies will use univariate and multivariate regression. The signal processing
will be conducted to produce eight points into the future. The data used are
composite financial cycle, Current Account, Credit Default Swap, USDIDR exchange
rate, Third Party Fund, Financial Account, Income per Capita, and M2/GDP. All
variables are in quarterly basis. The period is from first quarter of 1994 to last quarter
of 2013. All simulations were done using Eviews7.2©.
All the independent variables have gone through data processing steps:
converted to growth, normalized using the base of 2007Q4 and filtered using
frequency based filter. We name the processed data as data cycle, for example
Current Account will be CACYLCE which stands for current account cycle, and the
same nickname applies for the rest of variables used, with the exceptions: the cycle
for Third Party Fund is called DPKCYCLE and the cycle for Income per capita is called
PKCYCLE.
Univariate Simulation
31
OLS is used first to test how the signal in the composite cycle will be translated
into forecasted horizon using its own amplitude and phase.
The model for Univariate OLS is as follows.
𝑦 = 𝛼 + 𝛽𝑦𝑡−𝑖 + 𝜀
𝑖 = 1, … , 𝑛
Where 𝛼 is intercept, 𝛽 is the estimated coefficient for the lag data used and 𝜀
is residual.
In this case, the forecasting exercise will focus on the inertia of the cycle. In
estimating Univariate OLS, we focus in reduction residual (𝜀) outlier. The dummies
whether frequent base or point based are determined using residuals outlier
analysis.
As for the multivariate OLS the models used as follows,
𝑦 = 𝛼 + ∑ 𝛽𝑖𝑦𝑖𝑘1 + 𝜀
where 𝑖 = 1, … , 𝑛
The estimation result shows that the composite cycle depends on the past
trend and past behavior. Table 16 shows that past behavior will determine how the
cycle will move. First and second lag show significant coefficient value.
Table 16. Estimation Output for Univariate OLS
Dependent Variable: CC4
Method: Least Squares
Sample (adjusted): 1994Q3 2013Q4
Included observations: 78 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
D1 0.019 0.006 3.010 0.004
D2 0.021 0.006 3.285 0.002
D3 0.021 0.006 3.428 0.001
D4 0.019 0.006 3.078 0.003
@TREND -0.001 0.000 -4.177 0.000
@TREND*@TREND 0.000 0.000 4.530 0.000
CC4(-1) 1.945 0.008 250.106 0.000
CC4(-2) -0.967 0.008 -128.009 0.000
R-squared 0.9999 Mean dependent var 0.283
Adjusted R-squared 0.9999 S.D. dependent var 1.315
S.E. of regression 0.0109 Akaike info criterion -6.096
Sum squared resid 0.0084 Schwarz criterion -5.854
Log likelihood 245.73 Hannan-Quinn criter. -5.999
Durbin-Watson stat 1.7443
32
In this case, D#s are dummy variables for each quarter. This is used to remove
residual outlier associated with conditions related to quarterly effect. 2nd order trend
is used to represent the sinusoidal trend in frequency domain.
In the univariate case, the financial cycle represents the perception of financial
agents that manifests in the indicators of credit and credit to GDP ratio, stock price
and property price proxied by JAKPROP. If the behavior of each financial agent does
move sparingly like past data and ignore other information surrounding the data that
was truncated in the filtering process, financial cycle indicator shows Indonesian
financial system is heading into a boom period. Figure 3.2 shows financial cycle in
the next eight horizon showing an increase to boom period.
Note: the blue line are signal processed to the next eight points/quarters
Figure 19. Forecasted Financial Cycle using Univariate OLS
Figure 20 shows the standard deviation from the model in univariate
estimation also indicates the possibility range of the financial cycle.
Figure 20. Financial Cycle Standar Deviation Progress
Using the same model in univariate case, the model went through Bayesian
process. Figure 21 shows the probability of the cycle will move upward taking into
account the declining biases. The condition in the future eight quarters obtained
-3
-2
-1
0
1
2
3
94 96 98 00 02 04 06 08 10 12 14
CC4 (Baseline) CC4
-3
-2
-1
0
1
2
3
94 96 98 00 02 04 06 08 10 12 14
CC4F ± 2 S.E.
33
from BVAR shows the possibility that the financial agents’ perception is capable of
driving the financial cycle into a bust period
Figure 21. Univariate BVAR
Both simulations provide us with an idea that when economics agents are left
to pursue each individual goal given individual perception regarding the state of the
financial system, the financial cycle will move to a deeper bust period. The probability
distribution of each constructing indicators of the financial cycle will drive the cycle
to return to its long term average.
Multivariate Estimation
In this case, we take into account the above mentioned macrofinancial
indicators as independent variables. It is likely that if an indicator shows distress
signals, other indicators will come under distress during the same time or sometime
in the future. Multivariate estimation is used to test how the financial cycle is driven
by the other six indicators.
Multivariate OLS simulation shows the result was similar to the univariate
OLS. In this case, the multivariate case shows different result from the univariate
OLS model. The future estimated points show that the cycle is going into a bust
period. Figure 22 illustrates this result.
Note: Blue line is the forecasted points for the financial cycle.
Figure 22. The Forecast of Financial Cycle using Multivariate OLS
-3
-2
-1
0
1
2
3
94 96 98 00 02 04 06 08 10 12 14
CC4 (5,95 %range)
(10,90 %range) (20,80 %range)
-3
-2
-1
0
1
2
3
94 96 98 00 02 04 06 08 10 12 14
CC4 (Baseline) CC4
-3
-2
-1
0
1
2
3
94 96 98 00 02 04 06 08 10 12 14
CC4F ± 2 S.E.
34
The estimation output for multivariate model, Table17, describes the
association of the individual cycle of the independent variables to the financial cycle.
Volatility in the financial market will depress financial cycle. The coefficients of CDS,
Third Party Fund, and Financial Account are consistent with the expectation. The
coefficient of Exchange Rate turned out to be negative, which means depreciation of
exchange rate is associated with a period of bust. The coefficient for Income per
Capita (PKCYCLE) is not significant in the result. However, when it is not included
in the regressions, the overall result would produce inconsistent results to the
expected signs mentioned earlier. In this case, we can say that the coefficient serves
as control to the impact of individual income level to the financial cycle. Another
interesting result is in the estimation for the coefficient of M2/GDP cycle to financial
cycle (expressed as M2GCYCLE): a bullish behavior in the M2GCYCLE will depress
financial cycle.
Table17. Multivariate OLS Estimation Result
Dependent Variable: CC4
Method: Least Squares
Sample (adjusted): 1994Q2 2013Q4
Included observations: 79 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
C 0.007 0.001 7.798 0
CC4(-1) 0.725 0.045 16.068 0
PKCYCLE(-1) 0.000 0.003 0.138 0.891
EXCCYCLE(-1) -0.063 0.010 -6.360 0
CDSCYCLE(-1) -0.526 0.068 -7.764 0
DPKCYCLE 0.428 0.064 6.667 0
FACYCLE 0.573 0.068 8.424 0
M2GCYCLE(-1) -0.816 0.102 -8.023 0
R-squared 0.99999 Mean dependent var 0.29
Adjusted R-squared 0.99999
S.D. dependent var 1.31
S.E. of regression 0.00498 Akaike info criterion -7.67
Sum squared
resid 0.00176
Schwarz criterion
-7.43
Log likelihood 311.01390 Hannan-Quinn criter. -7.58
F-statistic 769113.70 Durbin-Watson stat 1.96
Prob(F-statistic) 0
The lead and lag of each individual cycles indicate individual characteristics
in medium term window. Based on our exercise, M2/GDP cycle shows a similar
behavior to the financial cycle. In fact, M2/GDP medium cycle leads financial cycle.
Figure 23 shows medium term cycle is highly associated to the liquidity of the
35
financial system. In this case, liquidity is represented by Third-party fund (expressed
as DPKCYCLE) and M2/GDP.
Figure 23. Lead and Lag in Medium Term Cycle
Assuming that Third Party Deposit and M2/GDP can also be considered as
the liquidity of the financial system, the correlations of the financial cycle (CC4) and
the cycles of the independent variables also confirm the fact that financial cycle is
highly associated with liquidity. This provides us with a hint of the use of
macroprudential adjustment to Liquidity Coverage Ratio as another good
countercyclical measure. Table 18 describes that the correlation between Third-party
Fund (expressed as DPKCYCLE) and financial cycle and the correlation between
capital flow (expressed as FACYCLE) and financial cycle are both high. Liquidity is
-3
-2
-1
0
1
2
3
94 96 98 00 02 04 06 08 10 12 14
CC4 FACYCLE
-25
-20
-15
-10
-5
0
5
10
15
94 96 98 00 02 04 06 08 10 12 14
CC4 EXCCYCLE
-3
-2
-1
0
1
2
3
94 96 98 00 02 04 06 08 10 12 14
CC4 CDSCYCLE
-3
-2
-1
0
1
2
3
94 96 98 00 02 04 06 08 10 12 14
CC4 GDPBAMCYCLE
-3
-2
-1
0
1
2
3
94 96 98 00 02 04 06 08 10 12 14
CC4 DPKCYCLE
-3
-2
-1
0
1
2
3
94 96 98 00 02 04 06 08 10 12 14
CC4 M2GCYCLE
36
important for credit extension which was one of the financial cycle constructing
indicators.
Table 18. Cycles Correlations
Figure 24 also shows how the other data affect the financial cycle
simultaneously. When all the data drive the financial indicators; credit, credit/GDP,
stock price and property price; the financial cycle could move toward high bias
possibility. The green areas show how financial cycle responds to other 8 indicators
in Table 3.3.
Figure 24. Chaotic Behavior using Multivariate BVAR
Back to BVAR theory, the distribution of each independent variable is used to
estimate the regression coefficient and the distribution of independent variables
determine the outcome of Bayesian estimation5. Figure 25 shows that there are some
data not normally distributed. The effect of this distribution is most likely the cause
of the chaotic behavior shown in the BVAR estimation.
5 Assuming that all the data is normally distributed whilst not all the distribution is really
in normal distribution, might in log distribution, exponential distribution, gamma distribution etc.
CACYCLE CDSCYCLE DPKCYCLE EXCCYCLE FACYCLE GDPBAMCYCLE M2GCYCLE PKCYCLE CC4
CACYCLE 100%
CDSCYCLE 76% 100%
DPKCYCLE 13% -48% 100%
EXCCYCLE -4% 46% -93% 100%
FACYCLE 42% -22% 83% -59% 100%
GDPBAMCYCLE 44% 40% -8% 31% 38% 100%
M2GCYCLE -4% -55% 90% -96% 55% -49% 100%
PKCYCLE -4% 26% -50% 64% -14% 81% -79% 100%
CC4 13% -32% 75% -59% 81% 53% 43% 19% 100%
-3
-2
-1
0
1
2
3
94 96 98 00 02 04 06 08 10 12 14
CC5 (5,95 %range)
(10,90 %range) (20,80 %range)
37
Figure 25. The Distribution of the Data
Note that not a single independent variable causes the chaos in the financial
cycle. The financial agents in financial cycle will always response to any change in
market. Figure 26 shows how market drive the financial cycle. The possibility of
chaotic result is just another proof that if there is not enough information and the
degree of uncertainties is high, financial agents would tend to be confused and can
cause chaos to the financial market or choose to exhibit herding behavior.
0
1
2
3
4
-.8 -.6 -.4 -.2 .0 .2 .4 .6 .8
De
nsi
ty
CACYCLE
0.0
0.4
0.8
1.2
1.6
2.0
2.4
2.8
-.8 -.6 -.4 -.2 .0 .2 .4 .6 .8
De
nsi
ty
CDSCYCLE
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
De
nsi
ty
DPKCYCLE
.00
.02
.04
.06
.08
.10
-30 -25 -20 -15 -10 -5 0 5 10 15 20 25
De
nsi
ty
EXCCYCLE
0.0
0.4
0.8
1.2
1.6
2.0
2.4
2.8
-.8 -.6 -.4 -.2 .0 .2 .4 .6 .8
De
nsi
ty
FACYCLE
0.0
0.4
0.8
1.2
1.6
2.0
-.8 -.6 -.4 -.2 .0 .2 .4 .6 .8
De
nsi
ty
GDPBAMCYCLE
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
-3 -2 -1 0 1 2 3 4
De
nsi
ty
M2GCYCLE
.0
.1
.2
.3
.4
.5
.6
.7
-4 -3 -2 -1 0 1 2 3 4
Histogram Kernel
De
nsi
ty
PKCYCLE
38
CDS
Third-party Fund
Exchange rate (USD to IDR)
Current Account
Financial Account
GDP
M2/GDP
Figure 26. Chaotic Financial Cycle
3.3. Signal Processing with Different Scenarios
In order to test the financial cycle robustness, financial cycle is forecasted into
the future points from 2014Q3 to 2015Q4, and individual indicator data had to be
forecasted using various models and scenarios. Broad credit (BC) is forecasted using
BAMBI (Banking Model of Bank Indonesia), and GDP for ratio of BC to GDP uses
ARIMBI (econometric model used in Bank Indonesia). BC, GDP, JCI and JAKPROP
are forecasted using scenarios, univariate model and deterministic model. The
-4
-2
0
2
4
6
8
94 96 98 00 02 04 06 08 10 12 14
CC4 (Baseline) (5,95 %range)
(10,90 %range) (20,80 %range)
-4
-2
0
2
4
6
94 96 98 00 02 04 06 08 10 12 14
CC4 (Baseline) (5,95 %range)
(10,90 %range) (20,80 %range)
-20
-10
0
10
20
30
40
50
94 96 98 00 02 04 06 08 10 12 14
CC4 (Baseline) (5,95 %range)
(10,90 %range) (20,80 %range)
-3
-2
-1
0
1
2
3
94 96 98 00 02 04 06 08 10 12 14
CC4 (Baseline) (5,95 %range)
(10,90 %range) (20,80 %range)
-3
-2
-1
0
1
2
3
4
5
6
94 96 98 00 02 04 06 08 10 12 14
CC4 (Baseline) (5,95 %range)
(10,90 %range) (20,80 %range)
-4
-2
0
2
4
6
8
94 96 98 00 02 04 06 08 10 12 14
CC4 (Baseline) (5,95 %range)
(10,90 %range) (20,80 %range)
-6
-4
-2
0
2
4
6
8
94 96 98 00 02 04 06 08 10 12 14
CC4 (Baseline) (5,95 %range)
(10,90 %range) (20,80 %range)
39
scenarios in deterministic model as follows (i) annual growth of JCI and JAKPROP
remain the same till 2015Q4, (ii) annual growth of JCI and JAKPROP decrease 5%
per quarter and (iii) annual growth of JCI and JAKPROP increase 5% per quarter. In
the univariate model, value of JCI and JAKPROP are determined using econometrics
model. The model is used to capture the behavior of JCI and JAKPROP if there is no
shock considered. The value of BC and BCGDP remain the same through the
simulations (ceteris paribus), and on the other hand the values of JCI and JAKPROP
change according to the 10 scenarios as follows,
A. 0% growth per quarter of JCI and JAKPROP
B. -5% growth per quarter of JCI and 0% growth per quarter of JAKPROP
C. +5% growth per quarter of JCI and 0% growth per quarter of JAKPROP
D. 0% growth per quarter of JCI and -5% growth per quarter of JAKPROP
E. 0% growth per quarter of JCI and +5% growth per quarter of JAKPROP
F. baseline value of JCI and JAKPROP
G. -5% growth per quarter of JCI and baseline value of JAKPROP
H. +5% growth per quarter of JCI and baseline value of JAKPROP
I. baseline value of JCI and -5% growth per quarter of JAKPROP
J. baseline value of JCI and +5% growth per quarter of JAKPROP
Each scenario was used to construct financial cycle. Each constructing
indicator of the financial cycle will be assigned the same weights stated in the
previous chapter. The weights used for different forecasted quarters are kept the
same as we saw the scenario will not cause a change in the weighting. Figure 3.10
shows 10 financial cycles resulted from 10 different scenarios stated above.
Figure 27. Testing the Robustness of the Financial Cycle
According to the above figure, the ten financial cycles constructed using
deterministic and econometrics model overlap each other. The results show that the
financial cycle is robust. However, this also pose a concern that financial cycle is by
40
construction will be highly influenced by the cycle’s inertia or by the pattern of the
cycle in the past. Therefore it is important to understand the attributes and
characteristics of the cycle in order to gauge the strength and weaknesses of using
the financial cyle as reference for the CCB mechanism.
41
IV. CONCLUSION
The financial cyle provides interesting attributes to represent the behavior of
the financial agents. It can depict the booms and busts of the financial agents’
perception. It is associated with the financial crisis events, and therefore can be used
as an early warning system as well as a good reference for the countercyclical capital
buffer policy.
The construction of financial cycle in this paper is able to include asset prices.
It is an improvement over Alamsyah et al (2014). Despite the additional asset price
data, the timing of both cycles is similar. The difference comes in the amplitude that
can be caused by the differences of the base year of the normalization of data. The
similarity is actually by construction since the construction process tends to
downplay the influence of asset prices as we decided that credit indicators should
play more role in determining the financial cycle as the banking system still
dominates the Indonesian financial system. This suggests that the role of banking
system is still major in influencing the perception of the financial agents.
The forecasting exercise mostly shows that the cycle is enough information to
predict the future as the multivariate models deliver similar results to the univariate
case using the cycle’s lag indicators with the exception of univariate OLS. Therefore,
it is important to understand the characteristics of the cycle as well as understand
the construction mechanism, especially with regard to the filtering methods. This
finding is also important to realize not to rely on only the financial cycle to determine
the countercyclical capital buffer policy. The use of high frequency indicators such
as the stress indicators can be a good practice to confirm our belief about what to
decide with the buffer.
42
43