IIR Working Paper No.58

Financial Cycles and Business Cycles in East Asian Emerging Economies:
Features, Interactions, and Changes

Masahiro ENYA
March 2012

Issued by：
大阪国際大学 国際関係研究所
大阪府守口市藤田町６丁目２１－５７
570-8555
Institute of International Relations
Osaka International University
6－21－57

Tohdacho, Moriguchi

Osaka 570－8555
Japan
この Working Paper の内容は、著者の文書による同意なくしては、
一部といえども、引用、再録されてはならない。

Financial Cycles and Business Cycles in East Asian Emerging Economies:
Features, Interactions, and Changes*

Masahiro ENYA**First draft
March 2012

Abstract
The purposes of this paper are to analyze the financial cycles in East Asian emerging economies (South
Korea, Hong Kong, Singapore, Taiwan, Thailand, Malaysia, and Indonesia) and examine synchronizations
between the business and financial cycles therein. The main question posed by this paper is whatstrengthens the synchronization of business and financial cycles? Our hypothesis is that economies withmore highly developed household credit see a higher degree of synchronization between financial andbusiness cycles. Our findings, obtained in terms of a concordance index between cycles after identifying
financial and business cycles, are that the patterns of changes in the degree of synchronization vary acrosseconomies and that the degree of synchronization between output cycles and credit, housing-price, or
equity-price cycles has increased in the 2000s at a rate exceeding that in the 1980s and 1990s for
economies with more highly developed household credits, for example, South Korea, Hong Kong, andMalaysia.

JEL Classification Numbers: E32; E44; F42*

This work was supported by Grant-in-Aid for Scientific Research (C) (23530348) from Japan Society for the
Promotion of Science (JSPS).
**
Associate Professor, Osaka International University. Authors’ E-Mail Addresses: enya@ef.oiu.ac.jp

1.

Introduction
Recent global financial crises have led researchers and policymakers to pay greater attention

to the linkage between financial activities and real activities. Financial crises have often coincidedwith booms and busts in credit markets, and/or asset markets (IMF, 2000), ever since financial markets
became more liberalized in the early 1980s.1 When financial market imperfections exist, intense
fluctuations in asset prices can play significant roles in business cycles. In developed and emerging
economies alike, we have often witnessed economic recessions being accompanied by a significant
fall in asset prices, especially following several periods of asset-price boom. Indeed, some well-known
cases are the savings and loans crisis in the United States in the 1980s, the crash of the bubble market
in Japan in the 1990s, and the crash of the dot-com bubble in the United States in 2000.
Some emerging economies also experienced the Asian economic crisis in the 1990s. Before
that crisis in 1997–1998, the financial system in East Asia had two prominent features: bank-centeredfinancial intermediation and corporate-focused lending (Mohanty and Turner, 2010). However, thesefinancial intermediation features have since changed. The investment decline of the corporate sector
was prolonged following the crisis; the corporate sector decreased its bank borrowing and often
shifted from external finance to internal finance. Mohanty and Turner (2010) find that while thecorporate sector has moved away from bank credit, the bank sector has expanded its provision of
credit to the household sector. Indeed, in South Korea, Malaysia, and Indonesia, the share of credit
going to the household sector has increased from about 25 percent of all credit in the 1990s to about
50 percent in 2005 (Mohanty and Turner, 2010). Similar changes have been seen in other emerging
Asian economies. How have changes in the patterns of financial intermediation affected interaction
between financial and business cycles? Household credit has mainly been used in housing andconsumer loans; under such conditions, housing assets have become major components of household
assets.
The purposes of this study are to analyze financial cycles and to examine synchronizations
between business cycles and financial cycles in seven East Asian emerging economies over the last
three decades.2 The following are the main questions posed by this study: What have been the basic
features of financial cycles in emerging Asian economies over the last three decades? Have thefinancial cycles changed over time? How strong are the links between financial and business cycles inemerging Asian economies? Have the links between business and financial cycles changed over

1 Although financial markets were highly regulated from the Great Depression until the 1980s, a deregulation offinancial markets began in the early 1980s in many developed and emerging economies. As deregulation, globalization,and innovation in the financial markets have progressed over the last three decades, financial crises have become more
frequent.
2
The sample economies are South Korea, Hong Kong, Singapore, Taiwan, Thailand, Malaysia, and Indonesia; thePhilippines was not examined owing to a lack of data availability for housing price. Also, our sample does not include
China and India because housing-price fluctuations within each of these two economies vary considerably on accountof the vastness of the economies.

1

time—and if so, why and how? This study focuses on cycles in the housing, equity, and credit markets,that is, housing-price, equity-price, and credit cycles. We detect cycles using quarterly series over the
1981Q1–2011Q2 period. Our hypothesis vis-à-vis our main question is that emerging Asian
economies with more strongly developed household credit provisioning may have strengthened
synchronization between the financial and business cycles in the 2000s, following the Asian crisis. To
examine the degree of synchronization, we use concordance statistic proposed by Harding and Pagan(2002), which is useful in determining the degree of synchronization between cycles.
Many studies examine intense contraction phases in financial or business cycles or thesituations before and after financial crises. This study focuses on both contraction phases andexpansion phases in financial and business cycles. Understanding of financial cycles andsynchronization between business and financial cycles is still limited. Claessens, Kose, and Terrones(2011b) analyze financial cycles and the interactions between financial and business cycles in a large
sample that includes both developed and emerging economies. Although the current study’s interests
are similar to those of Claessens et al. (2011b), it differs from Claessens et al. in two respects: the first
pertains to the sample economies, and the second, to the methodology used to identify business andfinancial cycles. This study focuses on emerging economies in East Asia; because our sample
economies have grown greatly over the last three decades, we employ not a classical cycle, but agrowth cycle, which is identified in terms of deviations from trends.3
This paper is organized as follows. In the next section, we review the literature related tobusiness and financial cycles and determine the linkage between financial and real economies. In thethird section, we introduce our methodology for identifying cycles and examining interactions acrosscycles. In the fourth and fifth sections, we present the features of business and financial cycles, andexamine the interaction between business and financial cycles. Finally, in the concluding section, we
discuss the limitation of this study.

2.

Related literature: financial cycle and financial–real economy linkage

2-1. Financial cycle
A financial cycle is generally defined as a pattern of movement in a series that represents thestate of a financial market. Many studies focus on the equity market, housing market, or credit market.
Claessens, Kose, and Terrones (2011a) analyze financial cycles in 21 advanced OECD countries overthe 1960–2007 period, wherein cycles in housing price, equity price, and credit series are detected bymethods widely employed with respect to the business cycle. They find the following basic features offinancial cycles. In terms of the length of cycles, financial upturns tend to be longer than downturns:
financial downturns last about four to six quarters on average, while financial upturns vary across
3 Claessens et al. (2011a, 2011b) employ a classical cycle approach to identify business and financial cycles. A
classical cycle approach is based on the series level.

2

markets. Upturns are the longest in equity prices, lasting seven quarters on average; the shortest are incredit, lasting four quarters, although upturns in equity prices have become shorter over time. In termsof strength, downturns in equity prices have been much stronger than those in housing prices or credit;
the strength of the upturns has generally matched that of the downturns. In terms of synchronization,
credit and housing cycles have been highly synchronized within countries, while equity and housing
cycles have become more synchronized across counties.
Are there any differences in the features of financial cycles between developed andemerging economies? Claessens et al. (2011b) present the financial cycles of equity prices, housing
prices, and credit in 23 emerging economies, including Asian emerging economies, and those of 21
advanced countries. They find differences in the features of financial cycles between these two sets ofeconomies. Downturns are not necessary longer, but they are much sharper in emerging economies
than in advanced economies. Equity upturns are much longer in advanced economies than in emerging
economies, while credit upturns are much longer in emerging economies.4 Equity prices in advanced
economies and credit in emerging economies both seem to have upward trends.

2-2. Empirical literature: synchronization between business and financial cycles
How can the degree of synchronization between two cycles be measured? Harding andPagan (2006) propose a concordance index for measuring the degree of synchronization between two
cycles. In that concordance index, one examines the ratio of periods in which two series coincide in
the same phases of cycles, across all sample periods. Some studies that focus on the synchronizationof cycles often use the index (e.g., Avouyi-Dovi and Matheron, 2005; Avouyi-Dovi, Kierzenkowski,
and Lubochinsky, 2006; Cotis and Coppel, 2005; Claessens, Kose, and Terrones, 2011b). Avouyi-Doviand Matheron (2005) examine the links between business and stock market cycles among developed
economies, based on the sample period of 1978Q1–2002Q3; they use two approaches to measure
co-movements between them. They find a strong dependent link between them, but only in the UnitedStates—that is, no link is found in France, Germany, the United Kingdom, or Italy.
Recent studies have focused on interactions between housing price and business cycles indeveloped economies (IMF, 2006; IMF, 2008; IMF, 2011; Bracke, 2011). The International Monetary
Fund (IMF) (2008) suggests that innovations in housing finance systems in advanced economies havestrengthened the role of the housing sector in the business cycle, by virtue of housing wealth effects
and financial accelerator effects. Bracke (2011) analyzes the duration of housing-price cycles in thelast 40 years among OECD countries, including South Korea. He detects housing-price cycles by
4 According to Claessens et al. (2011b), the durations of upturns and downturns within cycles are as follows. For
developed economies, the durations of upturns and downturns are 14.2 and 8.4 quarters in housing-price cycles, 21.9
and 6.6 quarters in equity-price cycles, and 8.0 and 5.5 quarters in credit cycles. Likewise, for emerging economies, thedurations are 8.2 and 7.9 quarters in housing-price cycles, 12.3 and 5.9 quarters in equity-price cycles, and 12.6 and 6.6
quarters in credit cycles.

3

using a classical cycle approach that is based on the level of the housing-price series. He finds that, on
average, upturns last longer than downturns, but the difference disappears once one excludes the mostrecent housing upturn.
The studies that analyze emerging economies thus are still small in number. Phang (2002)
examines the links between housing prices and aggregate consumption in the case of Singapore. He
concludes that the results of recent studies of OECD countries—in which housing wealth change wasfound to have a positive effect on aggregate consumption—cannot be generalized to Singapore.
Claessens et al. (2011b) investigate interactions between business and financial cycles and detectbusiness and financial cycles in 21 advanced and 23 emerging economies over the period of
1960Q1–2007Q4. As mentioned, they employ three measures—namely, credit, house price, and equity
price—as financial cycles. In calculating the concordance index between business and three different
financial cycles, they find that in developed economies, the business cycle tends to exhibit a highdegree of synchronization with cycles in credit and housing price. Moreover, they also find that inemerging economies, the business cycle tends to synchronize not only with the credit cycle but alsowith the equity-price cycle. However, the statistical significance values of these concordance indices
are weak, given the same trends within the series.
This main concern of the current study closely resembles that of Claessens et al. (2011b).
However, the former differs from the latter in some aspects; the main difference is in their methods for
detecting cycles. The current study employs a growth cycle approach used in the business cycle
literature, while taking into account that some series in emerging economies have an upward trend. In
particular, upward trends in output and in credit have seemed to be strong in East Asian emerging
economies over the last three decades. When both cycles have an upward trend, the concordance index
between these cycles can tend to be high, although the statistical significance therein is weak.

3.

Methodology

3-1. Definition of “business cycle”
A business cycle is defined as a pattern of movement in a series representing aggregate
economic activity. There are two approaches to identifying a business cycle. One defines the cycle
directly by analyzing the series level; this is referred to as the “classical approach.” The other defines
the cycle by analyzing deviations from trends; it is referred as the “growth approach.” Which approachis preferable? Which is the optimal approach depends on the characteristics of the time series, or the
purpose of analysis.
The current study uses the growth approach to identify business cycles. The economies thisstudy examines have experienced rapid economic growth over the last three decades. We are interested
in the cyclical movements of real activities, not in trends. This study focuses on quarterly real grossdomestic product (GDP) as a series representing aggregate output activity; it also decomposes real
4GDP series into a cyclical component and a structural component, via a Hodrick–Prescott (HP) filter.
Then, we detect the turning points—that is, the peaks and troughs—in the cyclical component series.
The peaks are the periods with local high points in the cyclical component series, while the troughs arethose with local low points.
The BB algorithm—so called because it was set out by Bry and Boschan (1971)—is awell-known algorithm for detecting peaks and troughs in monthly observations. The BBQ
algorithm—a quarterly version of the BB algorithm, suggested by Harding and Pagan (2002)—is also
used, with some censoring rules. We use the BBQ algorithm to detect peaks and troughs in thequarterly cyclical component series of real GDP. At first, the local peaks (troughs) are identified as
occurring at time t whenever {yt > (<) ytk}, k = 1,…, K, where K is set to 2. Secondly, if two peaks(troughs) are within three quarters in a series, the larger of the two is selected as a peak (trough). The
second step implies that a phase must last at least three quarters.5

3-2. Definition of “financial cycle”
A financial cycle is generally defined as a pattern of movement in a series that represents thestate of a financial market. Like Claessens et al. (2011a, 2011b), we focus on three financial markets:the housing market, equity market, and credit market. We assume that the state of the housing, equity,and credit markets are represented by housing prices, equity prices, and credit series, respectively.
These three financial series differ in dimension: the two asset-price series constitute a price dimension,
while credit is a stock dimension. Price series tend to fluctuate more frequently than stock series.
Moreover, credit in Asian emerging economies seems to have an upward trend. We use the classicalapproach for identifying the two asset-price cycles, and the growth approach for identifying the credit
cycle. We take the cyclical component of credit via a HP filter, while we identify financial cycles using
the BBQ algorithm.

3-3. Features and synchronization of cycles
Two phases are identified in the cycles. The expansion phase is identified as the periods
from the trough to the next peak in the series, while the contraction phase is identified as the periods
from the peak to the next trough in the series. The main features of the cycles are their duration,
amplitude, and the slope of cyclical phases. The duration of an expansion phase is the number ofquarters within the expansion phase in a series; likewise, the duration of a contraction phase is thenumber of quarters within the contraction phase. The amplitude of an expansion phase is the rate ofchange of the level of an observation from a trough to the next peak; likewise, the amplitude of acontraction phase is the rate of change of the level of an observation from a peak to the next trough.

5

The BB algorithm for monthly observations requires that a phase must last at least six months.

5Finally, the slope of any phase is the amplitude of a given phase divided by the duration of that phase.
To measure the degree of synchronization between cycles, this study uses the concordanceindex set out by Harding and Pagan (2006), according to whom the concordance index between cycles
in series xt and series yt—that is, CIxy—is defined as the following calculation:
CIxy =T{∑T S S

∑T

1

S

1

S

},

(1)

where Sxt represents the cycle phase of series x at time t, Syt represents the cycle phase of series y attime t, and T is the sample size. Sit takes a value of 1 if the business cycle phase of series i at time t isin an expansion phase, whereas Sit takes a value of 0 if it is in a contraction phase. CIxy takes a valueof 1 if the two series, x and y, are always in the same phases, and a value of 0 if they are always in the
opposite phases. That CIxy is equal to 0.5 implies a lack of synchronization between the two cycles.
A problem with the value of CIxy can be seen when both Sxt and Syt take values close to 1 or
0 in most sample periods. That both Sxt and Syt take values close to 1 in most sample periods meansthat both x and y have a strong upward trend over the sample period. For example, when both Sxt and
Syt take the value of 1 in all sample periods, CIxy has a maximum value of 1. To preclude these trend
problems, Harding and Pagan (2006) recommend testing for the significance of the concordance index.
It is useful to rewrite this index thus:

CIxy = 1 +T∑T S S– µSx – µSy= 1 + 2 ρSσxσy + 2µSx µSy - µSx - µSy,
where µSx and µSy are the means of Sxt and Syt, respectively; σx and σy present standard deviations of
Sxt and Syt, respectively; and ρS is a correlation coefficient between Sxt and Syt. ρSσxσy suggests a
covariance between Sxt and Syt. To obtain the correlation coefficient ρ S, the following linear
relationship is estimated via the ordinary least squares method, augmented by the heteroskedasticity
and autocorrelation consistent (HAC) procedure, taking into account that the serial correlations in theresidual under the null hypothesis ρS = 0 (Avouyi-Dovi and Matheron, 2005):
σy–1 Syt = c + ρS σx–1 Sxt + ut
3-4. Data
We identify business and financial cycles for emerging economies in East Asia over the
1981Q1–2011Q2 period. We focus on the seven economies of South Korea, Hong Kong, Singapore,
Taiwan, Thailand, Malaysia, and Indonesia, taking into account data availability. To identify abusiness cycle, we use the quarterly real GDP series, derived from IFS and Abeysinghe and Rajaguru
6

(2004). Quarterly GDP series from the 1980s are not available for Thailand, Malaysia, and Indonesia;
however, we obtained the disaggregated quarterly real GDP series estimated in Abeysinghe andRajaguru (2004), to use in their place.6 Quarterly real GDP series are seasonally adjusted by Census
X12, and transformed into natural logarithms. The series for the business cycle extend over the
1981Q1–2011Q2 period for all sample economies.
Housing prices, equity prices, and credit cycles are identified as comprising financial cycles.
To identify these cycles, we focus on housing prices, equity prices, and credit. The housing-priceseries correspond to the indices of residential houses or land prices, as obtained from the CEIC
database. The CEIC database reports various price indices of property in terms of sources, property
type, area covered, and so on. We use a more sophisticated index for any given economy. The equity
price series comprise share-price indices, from the IFS or the CEIC database. The credit series areaggregate claims on the private sector by deposit money banks, and these data are also from the IFS.
These three series are deflated by consumer price index, seasonally adjusted by Census X12, andtransformed into natural logarithms. The share price and credit series cover the 1981Q1–2011Q2
period for all economies, excluding the share-price series for Singapore (1985Q1–present) andIndonesia (1983Q2–present), and the credit series for Hong Kong (1994Q1–present). The
housing-price series cover the 1981Q1–2011Q2 period only for Hong Kong and Singapore, due todata-availability issues. The starting dates of the housing series are 1986Q1 for Korea, 1991Q1 for
Thailand, 1991Q3 for Taiwan, 1994Q4 for Indonesia, and 1998Q4 for Malaysia.

4.

Basic features of cycles

4-1. Business cycles
Figure 1 shows the fluctuations of the cyclical component of real GDP and the expansion
phases in the business cycles for each economy. In Figure 1, the periods of an expansion phase areshaded; in contrast, the periods of a contraction phase are not shaded. Some economies are often foundin the same phases in business cycles, at the same periods. Many economies are in a contraction phasefor 2008–2009, in the early 2000s, and in 1998; these contraction phases might reflect the Lehman
Brothers shock, the crash of the dot-com bubble, and the Asian economic crisis, respectively. The
business cycles that we identify seem to fit with well-known economic boom-and-bust events. In the
1980s and 1990s, the periods within a contraction phase tended to vary across economies.
Table 1 shows the basic features of the expansion and contraction phases in the businesscycles. The figures pertaining to number, duration, amplitude, and slope refer to sample means, andthey refer to the frequency, length, strength/depth, and violence of each phase, respectively. We
6 Abeysinghe and Rajaguru (2004) provide quarterly real GDP estimates from 1975Q1 for the ASEAN 4 group, andfrom 1978Q1 for China. They disaggregate annual GDP into quarterly figures, using the related series to predict thequarterly GDP, such as exports, imports, money stock, and so on.

7identify 81 expansion phases and 80 contraction phases in 7 emerging economies in East Asia over the
1981Q1–2011Q2 period. The expansion and contraction phases in the business cycles occur more
frequently in the 2000s than in the 1980s or 1990s. The number of expansion (contraction) phases is
30 (32) in the 2000s, 28 (24) in the 1990s, and 23 (24) in the 1980s. Both business-cycle phases haveoccurred more frequently over time.
Expansion and contraction phases last about seven and five quarters on average, respectively,
with the former generally lasting longer than the latter. This tendency is consistent with the findings ofBracke (2011) and Claessens et al. (2011b). The durations of both phases seem to tend to be stable
over time. Do the two crises in the 1990s and 2000s affect the means of duration of each phase? The
contraction phases in the Asian and global crises last 6.3 and 5.1 quarters, respectively. Intense
contractions that accompany crises are a little longer than no-crisis contractions. Likewise, theexpansion phases followed by contractions in the Asian and global crises are 10.1 and 8.1 quarters,
respectively. Expansions followed by intense contractions that accompany crises are much longer thanother expansions. The durations of the expansion phases in the 1990s and in the 2000s—excluding theintense expansion phases followed by crises—are 6.0 quarters and 5.9 quarters on average,
respectively, and tend to be stable over time. An expansion phase lasting more than two years tends tobe an intense expansion phase, followed by an intense contraction.
On average, real GDP increases by about 13 percent in an expansion phase; in contrast, it
increases by about 1 percent in a contraction phase. The strength of a contraction phase is muchsmaller than that of an expansion phase; even in a contraction phase, real GDP increases, thus
reflecting the strong growth trend of real GDP in our sample economies. The strength and violence ofan expansion phase have become smaller over time; on the other hand, those of a contraction phasehave become deeper and more violent over time. There is less difference in slope between the
expansion phases that are followed by crises, and other expansion phases. However, the amplitude andslope in the contraction phase accompanying a crisis are deeper and more violent than those in othercontraction phases.

4-2. Financial cycles
Figure 2 shows the fluctuations of financial series and the phases of expansion andcontraction in financial cycles for each financial market in the seven emerging economies under
examination. In Figure 2, the periods of an expansion phase in the financial cycles are shaded, while
the periods of a contraction phase are not shaded. For housing-price cycles (HPI), many economies are
in an expansion phase in the second half of the 2000s—except for South Korea andIndonesia—although all are in a contraction phase in 2008–2009. In the first half of the 2000s, thefeatures vary across economies. The expansion phases are more dominant in South Korea, Taiwan,
and Indonesia, while the contraction phases are more dominant in Hong Kong and Singapore. In the81990s, while the contraction phases are more dominant in South Korea, Taiwan, and Indonesia, theexpansion phases are more dominant in Hong Kong and Thailand in the first half of the 1990s. In the
1980s, the only economies for which housing-price series are available are South Korea, Hong Kong,and Singapore; in these economies, expansion phases are more dominant in the second half of the
1980s.
For equity-price cycles (SPI), there are many features common to all the economies. Many
economies are in contraction phases in 2008–2009, 2000–2003, 1997–1999, 1990–1991, and
1983–1985; the exception is the Hong Kong economy, which is in an expansion phase in 1990–1993
and 1983–1987. For credit cycles (CREDIT), there are some common features across the examined
economies in the 1980s and 1990s. Most economies—except for Hong Kong and South Korea—are incontraction phases in 1990–1993 and 1982–1987. In the 2000s, features vary across economies.
Table 2 shows the basic features of expansion and contraction phases in financial cycles.
The upper panel of Table 2 contains the features of housing-price cycles, while the middle and lower
panels contain the features of equity-price cycles and credit cycles, respectively. Although the cycles
comprise the 1981Q1–2011Q2 sample period in seven emerging economies in East Asia, some cycles
are over a shorter sample period, depending on data availability.7 The number of expansion andcontraction phases are 48 and 45 in the housing-price cycles, 60 and 58 in the equity-price cycles, and64 and 62 in the credit cycles. The housing-price cycles contain fewer phases than the other cycles,
because for many economies, the sample period of the housing price series is shorter than that of theother series.
The expansion and contraction phases in both the housing-price and credit cycles last about
seven quarters, although the durations of the expansion phases in both housing-price and credit cycles
tend to match those of their contraction phases. The expansion phases in equity-price cycles, on theother hand, tend to be longer than their contraction phases. Over the 1990s and 2000s, the contraction
phases of the financial cycles have become shorter, although the expansion phases of financial cycles
tend to be stable. For only the credit cycle, both phases have become shorter. There are differences
between the features of the two economic crisis periods and those of other, typical periods. The
expansion phases of the financial cycles prior to economic crisis periods tend to be longer than thoseof other expansion phases, for all financial markets. Moreover, there are differences between thefeatures of the Asian crisis periods and the global crisis periods. The durations of the intense
contraction phases of the Asian and global crises are 10.0 and 4.5 quarters for the housing-price cycles,
9.6 and 5.6 for the equity-price cycles, and 11.3 and 5.8 quarters in the credit cycles, respectively. On
the other hand, the durations of intense expansion phases followed by intense contraction phases in the
7 The economies under examination that have later starting dates are South Korea (1986Q1), Taiwan (1991Q3),
Thailand (1991Q1), Malaysia (1998Q4), and Indonesia (1994Q1) for housing-price cycles; Singapore (1985Q1) andIndonesia (1983Q2) for equity-price cycles; and Hong Kong (1994Q1) for credit cycles.

9Asian and global crises are 8.0 and 10.7 quarters for the housing-price cycles, 8.3 and 11.4 for theequity-price cycles, and 11.4 and 8.0 quarters for the credit cycles, respectively. The durations of thecontraction phases of the financial cycle in the Asian crisis periods are longer than those in the global
crisis periods; in contrast, the expansion phases of the two asset-price cycles prior to the global crisis
periods are longer.
Equity-price cycles are more pronounced than housing-price and credit cycles. The phasesof equity cycles are stronger or deeper than those of the other cycles. In expansion phases, housing
price and credit each increase by 15–30 percent, on average; in contrast, equity price increases by
63 percent, on average. The features in terms of amplitude and slope vary between NIE 4 economies
and ASEAN 3 economies. Vis-à-vis both expansion and contraction in cycles: they are not necessarily
longer, but they are sharper in ASEAN economies than in NIE economies, excluding housing-pricecycles.
To summarize, the main features of financial cycles over the last three decades for emerging
economies in East Asia are as follows. 1) The expansion and contraction phases of both housing-priceand credit cycles last about seven quarters, on average. 2) The expansion phases of equity-price cycles
tend to be longer than the contraction phases in their cycles, lasting nine and six quarters, respectively.
3) The contraction phases of financial cycles and the expansion phases of credit cycles have become
shorter during the 1990s and 2000s. Once one excludes expansion phases preceding crisis periods, theexpansion phases become shorter over time.

5.

Interactions between business and financial cycles
This section analyzes synchronization between business and financial cycles. Fluctuations infinancial variables influence business cycles through some channels. Asset-price fluctuations may
affect real economies through wealth effects on consumption and the Tobin’s q effects on investment.
Bank credit plays an important role in driving consumption and investment decision-making, byintermediating between the surplus and deficit sectors of an economy. Moreover, the use of housing as
collateral may strengthen the effects of fluctuations in housing prices on consumption through changes
in borrowing capacity, which is referred to as the financial accelerator effect.8
This section addresses the following questions: What is the nature of the links between thefinancial and business cycles in emerging Asian economies over the last three decades? Have the links
between the business and financial cycles changed over time—and if so, why and how?
To answer these questions, we calculate the degree of synchronization between the business8

Table 4 shows the phases of the business cycle and fluctuations in the three financial series for each economy. The
fluctuations in the housing-price series are smaller than those in the other financial series for all economies, excluding
Hong Kong and Singapore. The three financial series seem to correlate in Hong Kong and Singapore. In addition,
during the global crisis period of 2008–2009, most economies are in contraction phases with declines in equity prices;Hong Kong and Singapore has declines in housing prices and credit. We can see the similar features with the Asiancrisis period of 1997–1998.

10

and financial cycles in each economy. Table 3 shows the concordance index, proposed by Harding andPagan (2006), for each economy. The index takes the value of 1 if two cycles are perfectly
synchronized, and takes the value of 0 if two cycles are countercyclical. Moreover, the index takes thevalue of 0.5 if there is no the link at all between the cycles.
The concordance indices over the full-sample period are roughly greater than 0.50 for all
economies, excluding Indonesia. In many economies, cycles in output and equity price have tended tobe highly synchronized during the last three decades. In particular, in NIE 4 economies and Thailand,
cycles in output and equity price tend to be in the same phase about 70 percent of the time. For South
Korea, Hong Kong, and Singapore, business cycles are significantly synchronized with both
equity-price and housing-price cycles. The concordance index for cycles in output and credit is lessthan 0.60 and not significant, with the exception of Hong Kong.
These findings are consistent with those of previous studies. Claessens et al. (2011b) findthat emerging economies feature a degree of synchronization between output and equity-price cycles
that is stronger than that seen in advanced countries; they also find that advanced countries display ahigher degree of synchronization between output cycles and credit or housing-price cycles thanemerging economies. As Claessens et al. (2011b) point out, these features may reflect the impact ofexternal capital flows on these real economies, via the dynamics of equity prices in emerging
economies.
Has the degree of synchronization changed over time? Table 3 also shows the concordance
statistics for each economy examined, over the two subsamples—that is, before and after 2000. For
South Korea, Hong Kong, and Malaysia, the concordance indices between business and financial
cycles have become larger in the 2000s than in the 1980s or 1990s. In particular, the indices between
business and housing-price or equity-price cycles are high and significant in 2000s, exceeding 0.70;
moreover, the indices between business and housing-price cycles exceed 0.75 in the 2000s. In contrast,
for the other four economies, the indices have become smaller in the 2000s. However, the indicesbetween business and each of the two asset-price cycles remain at around 0.6 in the 2000s; this is not
significant—except in Indonesia, where the indices between the business and two asset-price cycles
are much smaller in the 2000s.
Do these features of synchronization between business and financial cycles remain, even if
one were to exclude the most recent crisis periods? Table 4 shows the concordance indices over theperiods under examination, while excluding the Asian crisis and global crisis periods. Although thefeatures in Table 3 do not roughly change, the indices between business and equity-price cycles
become insignificant in the 2000s for South Korea, Singapore, and Malaysia. The contraction phase of
the business cycle may often coincide with that of the equity-price cycle during the global crisis period.
Once the global crisis period of 2008–2009 is excluded, the indices between business and credit or
housing-price cycles are significantly high in 2000s for Thailand.
11

To summarize, the main features of synchronization between business and financial cycles
over the last three decades among emerging economies in East Asia are as follows. 1) The degree ofsynchronization between output and equity-price cycles is both high and significant for many
economies. 2) Patterns of change in the degree of synchronization vary across economies; for South
Korea, Hong Kong, and Malaysia, the degree of synchronization between output cycles and credit,housing-price, or equity-price cycles has become higher in the 2000s than in the 1980s or 1990s.
These main findings are consistent with those of previous studies (Claessens et al., 2011b;
Peltonen, Sousa, and Vansteenkiste, 2009). Peltonen, Sousa, and Vansteenkiste (2009) suggest that
housing wealth effects on consumption have substantially increased in recent years among emerging
Asian economies, while equity wealth effects on consumption have tended to be large over the last 20
years.
Why have patterns of synchronization between business and financial cycles changed overtime? It is because financial intermediation in emerging Asian economies has changed since 2000.
Following the Asian crisis of 1997–1998, there have been prolonged declines in investment by the
corporate sector. The corporate sector has decreased its bank borrowing and, in many cases, has
shifted from external finance to internal finance. Under such circumstances, the banking sector has
shifted from corporate-focused lending to household-focused lending. Indeed, in South Korea,
Malaysia, and Indonesia, the share of credit going to the household sector has increased from about 25
percent of all credit in the 1990s to about 50 percent in 2005 (Mohanty and Turner, 2010). A similarchange has also been seen in other emerging Asian economies. The effects of asset-price or credit
fluctuations on output can strengthen if members of the household sector purchase their homes or
consume through bank borrowing, as seen in many developed economies. The results of recent studies
of OECD countries suggest there are strong housing wealth effects or collateral enhancement effects
on a real economy; the disintermediation of the corporate sector and household credit may haveplayed important roles in changes in synchronization between business and financial cycles since 2000
in emerging Asian economies.

6.

Conclusion
The purposes of this study are to analyze financial cycles in seven East Asian emerging

economies over the last three decades and to examine the synchronizations between the business andfinancial cycles therein. This study focuses on cycles in housing, equity, and credit markets—namely,
housing-price, equity-price, and credit cycles. We detect cycles using quarterly series over the
1981Q1–2011Q2 period. The current study addresses the following questions: What have been thebasic features of financial cycles in emerging Asian economies over the last three decades? Have thosefinancial cycles changed over time? What is the nature of the links between financial and businesscycles among emerging Asian economies? Have the links between business and financial cycles
12changed over time—and if so, why and how?
The results in terms of the basic features of the cycles are as follows. 1) The expansion andcontraction phases of both housing-price and credit cycles last about seven quarters, on average. 2)
The expansion phases of equity-price cycles tend to be longer than the contraction phases in theircycles, lasting nine and six quarters, respectively. 3) The contraction phases of financial cycles and the
expansion phases of credit cycles have become shorter during the 1990s and 2000s. Once one
excludes expansion phases preceding crisis periods, the expansion phases become shorter over time.
Further, the current study’s findings in terms of synchronization between business andfinancial cycles are as follows. First, the degree of synchronization between output and equity-pricecycles is both high and significant for many economies. Second, the patterns of change in the degreeof synchronization vary across economies. For South Korea, Hong Kong, and Malaysia, the degree ofsynchronization between output cycles and credit, housing-price, or equity-price cycles has become
higher in the 2000s than in the 1980s and 1990s.

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Harding, Don, and Adrian Pagan (2006), “Synchronization of Cycle,” Journal of Econometrics,
13132(1), pp.59-79.
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June 2010.
Peltonen, Tuomas A., Ricardo M. Sousa, and Isabel S. Vansteenkiste (2009), “Wealth Effects in
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Association Joint International Conference, Seoul, 4-6 July 2002.

14

Table 1. Basic Features of Business Cycles
Expansions (T-P)

Contractions (P-T)

Number Duration Amplitude Slope

Number Duration Amplitude SlopeBusiness Cycles
Full 7

Full

81

6.7

12.5%

1.8%

80

5.3

1.3%

0.2%

1980s

23

6.3

13.9%

2.1%

24

5.6

3.7%

0.6%

1990s

28

7.1

12.9%

1.9%

24

5.6

0.9%

0.2%

without crisis

21

6.0

11.2%

1.9%

17

5.3

5.0%

0.8%

asian crisis

7

10.1

18.2%

1.7%

7

6.3

-9.2%

-1.5%

30

6.6

10.8%

1.4%

32

4.8

-0.2%

0.0%

ongoing

5

-

-

-

2

-

-

-

without crisis

18

5.9

10.6%

1.8%

23

4.7

1.3%

0.3%

2000s

global crisis
NIEs 4

7

8.1

11.3%

1.4%

7

5.1

-5.2%

-1.0%

1980s

11

6.9

17.8%

2.5%

13

5.6

3.4%

0.6%

1990s

16

7.1

12.8%

1.8%

13

5.8

2.4%

0.3%

2000s

17

6.1

11.3%

1.6%

18

5.0

-1.3%

-0.2%

ASEAN 3 1980s

12

5.7

10.4%

1.8%

11

5.6

4.1%

0.6%

1990s

12

7.1

12.9%

1.9%

11

5.3

-0.9%

-0.1%

2000s
13
7.1 10.2% 1.2%
14
4.6
1.2% 0.2%
Notes: The statistics for Duration, Amplitude, and Slope refer to sample means. The number is the number of phasesthat started during the given sample periods. The duration for expansions is the number of the qurters from trough topeak, while the duration for contractions is the number of quarters from peak to trough. The amlitude for expansions is
the change rate of real GDP from trough to peak, the amlitude for contractions is the change rate of real GDP from peak
to trough. The slope is the amplitude divided by the duration. The full sample covers 1981Q1 to 2011Q2.

Table 2. Basic Features of Financial Cycles
Expansions (T-P)
Number Duration Amplitude Slope

Contractions (P-T)
Number Duration Amplitude Slope

Housing Price Cycles
Full 7Full
48
6.9 16.5%
2.0%
45
7.2 -13.2% -1.6%
1980s
7
7.7 21.7%
2.8%
7
5.0
-8.9% -1.2%
1990s
14
6.8 22.9%
2.9%
13
10.1 -29.1% -3.2%
without crisis
9
6.1 18.1%
2.9%
8
10.1 -20.4% -2.1%
asian crisis
5
8.0 31.5%
2.9%
5
10.0 -43.1% -4.8%
2000s
27
6.6 10.1%
1.1%
25
6.2
-6.0% -0.9%
ongoing
70
without crisis
14
4.9
6.7%
1.0%
19
6.8
-4.8% -0.5%
global crisis
6
10.7 17.9%
1.5%
6
4.5
-9.9% -2.2%
NIEs 41980s
7
7.7 21.7%
2.8%
7
5.0
-8.9% -1.2%
1990s
9
7.6 28.1%
3.0%
10
11.4 -30.3% -3.0%
2000s
1210.0 20.8%
1.9%
10
5.9
-8.1% -1.4%
ASEAN 3 1980s
1990s
510.0 20.8%
1.9%
3
5.7 -25.3% -3.7%
2000s
15
4.3
2.9%
0.6%
15
6.5
-4.7% -0.6%
Equity Price Cycles
Full 7Full
60
9.5 63.9%
6.7%
58
6.0 -46.7% -7.3%
1980s
14
12.9 96.9%
6.7%
13
5.7 -41.9% -7.5%
1990s
22
8.0 51.8%
7.2%
24
7.2 -56.5% -7.2%
without crisis
15
7.8 60.2%
9.0%
17
6.2 -41.0% -6.3%
asian crisis
7
8.3 34.0%
3.2%
7
9.6 -94.3% -9.4%
2000s
24
8.8 53.0%
6.3%
21
4.8 -37.9% -7.4%
ongoing61
without crisis
11
7.2 46.4%
6.8%
13
4.4 -20.7% -4.7%
global crisis
7
11.4 63.3%
5.5%
7
5.6 -69.9% -12.6%
NIEs 41980s
8
13.5 103.2%
6.7%
9
6.0 -46.0% -7.7%
1990s
13
8.2 49.7%
6.9%
13
6.7 -38.0% -5.2%
2000s
13
9.2 56.0%
6.9%
10
6.9
-5.4% -0.6%
ASEAN 3 1980s
6
12.2 88.4%
6.7%
4
5.0 -32.7% -7.2%
1990s
9
7.6 55.0%
7.6%
11
7.7 -78.5% -9.5%
2000s
11
8.4 49.2%
5.5%
9
4.6 -34.5% -7.2%
Credit Cycles
Full 7Full
64
7.1 23.9%
3.1%
62
7.2
2.5%
0.3%
1980s
21
6.3 27.6%
3.9%
20
6.4
9.3%
1.1%
1990s
18
8.2 28.6%
3.3%
19
8.8
-1.9% -0.2%
without crisis
11
6.1 19.5%
3.1%
12
7.3 12.4%
1.6%
asian crisis
7
11.4 42.8%
3.6%
7
11.3 -26.3% -3.3%
2000s
25
7.0 15.3%
1.9%
23
6.6
0.1% -0.1%
ongoing61
without crisis
13
6.5 13.7%
1.7%
16
6.9
-0.4% -0.2%
global crisis
6
8.0 18.8%
2.4%
6
5.8
1.6%
0.0%
NIEs 41980s
12
5.4 19.3%
3.3%
11
6.6 11.7%
1.7%
1990s
11
6.7 18.5%
2.7%
12
8.2
6.5%
1.0%
2000s
14
7.5 14.0%
1.6%
3
6.7
-0.5% -0.2%
ASEAN 3 1980s
9
7.4 38.6%
4.7%
9
6.1
6.4%
0.4%
1990s
7
10.4 44.4%
4.2%
7
9.9 -16.3% -2.3%
2000s
11
6.4 17.1%
2.3%
10
6.5
0.8%
0.0%
Notes: The statistics for Duration, Amplitude, and Slope refer to sample means. The number is the number of phases thatstarted during the given sample periods. The duration for expansions is the number of the qurters from trough to peak,
while the duration for contractions is the number of quarters from peak to trough. The amlitude for expansions is thechange rate of each respective financial variable from trough to peak, while the amlitude for contractions is that from peak
to trough. The slope is the amplitude divided by the duration. The full sample covers 1981Q1 to 2011Q2.

Table 3. The Concordance Indices
Full: 1981-2011

1980s and 1990s: 1981-1999

2000s: 2000-2011

0.525

Housing
PiriceGDP
0.618 *

EquityPiriceGDP
0.656 **

0.864

0.038

0.016

HKG Hong Kong

0.714 ***

0.730 ***

0.762 ***

0.002

0.000

0.000

0.010

0.022

0.000

0.046

0.000

0.000

SGP Singapore

0.525

0.648 ***

0.726 ***

0.645 ***

0.684 *

0.767 ***

0.326

0.587

0.674 +

0.639

0.008

0.000

0.008

0.038

0.001

0.103

0.257

0.052

TWN Taiwan

0.557

0.623

0.664 ***

0.539

0.676

0.671 ***

0.587

0.571

0.652 ***

0.111

0.001

0.640

0.391

0.003

0.114

0.602

0.008

CreditGDP
KOR Korea

0.290

0.487

Housing
PiriceGDP
0.500

EquityPiriceGDP
0.632

0.834

0.426

0.116

0.604

0.003

0.045

0.750 ***

0.671 **

0.711 ***

0.696 *

0.826 ***

0.848 ***

CreditGDP

0.667 *

0.587

0.750

0.041

THA Thailand

CreditGDP

Housing
PiriceGDP
0.761 ***

EquityPiriceGDP
0.696 *

0.652

0.190

0.184

0.598

0.634 +

0.648 **

0.618

0.667 +

0.645 *

0.565

0.609

0.113

0.100

0.011

0.128

0.092

0.033

0.351

0.326

0.253

MYS Malaysia

0.582

0.745 ***

0.590

0.526

0.600

0.526

0.674

0.761 ***

0.696 **

0.152

0.004

0.202

0.670

0.293

0.855

0.067

0.005

0.026

IDN

0.590

0.443

0.549

0.539

0.458 **

0.597

0.674

0.435

0.478

0.184

0.384

0.523

0.657

0.027

0.248

0.105

0.716

0.410

Indonesia

0.652

Notes: The statistics is the concordance index proposed by Harding and Pagan (2002). The figure in Italic type shows p-value. ***, **, *, + reports significance at 1%, 3%, 5%, 10%.
The full sample for each economy covers the following periods. The full sample for credit cycle covers the period 1994Q1-2011Q2 in Hong Kong. The full sample for housing cycle
covers the period 1986Q1-2011Q2 in Korea, 1991Q3-2011Q2 in Taiwan, 1991Q1-2011Q2 in Thailand, 1998Q4-2011Q2 in Malaysia, and 1994Q1-2011Q2 in Indonesia. The fullsample for equity cycle covers the period 1985Q1-2011Q2 in Singapore, and 1983Q2-2011Q2 in Indonesia.

Table 4. The Concordance Indices without crisis periods
Before Asian Crisis: 1981-1996

2000s before global crisis: 2000-2007

2000s: 2000-2011

0.438

Housing
PiriceGDP
0.386

EquityPiriceGDP
0.609

0.459

0.771

0.308

0.750 *

0.656 +0.059

SGP Singapore

0.703 ***
0.003

0.064

0.000

0.484

0.299

0.145

0.103

0.257

0.052

TWN Taiwan

0.547

0.591

0.672 ***

0.500

0.594

0.688 ***

0.587

0.571

0.652 ***

0.610

0.942

0.006

0.718

0.479

0.016

0.114

0.602

0.008

CreditGDP
KOR Korea
HKG Hong Kong

Housing
PiriceGDP
0.719 *

EquityPiriceGDP
0.656

0.039

0.047

0.214

0.604

0.003

0.045

0.688 ***

0.594

0.750 ***

0.875 ***

0.696 *

0.826 ***

0.848 ***

0.068

0.001

0.241

0.005

0.000

0.046

0.000

0.000

0.688 +

0.771 ***

0.406

0.594

0.656

0.326

0.587

0.674

CreditGDP
0.750 *

CreditGDP
0.587

0.594

0.652

0.518

THA Thailand

EquityHousing
PiricePiriceGDP
GDP
0.761 ***
0.696 *

0.184

0.688 **

0.708 *

0.609

0.656 **

0.719 *

0.594

0.565

0.609

0.014

0.033

0.130

0.029

0.036

0.870

0.351

0.326

0.652
0.253

0.516

0.625

0.688 +

0.625

0.674

0.761 ***

0.696 **MYS Malaysia

0.516
0.855

0.981

0.273

0.078

0.250

0.067

0.005

0.026

IDN Indonesia

0.547

0.582

0.688

0.500

0.469

0.674

0.435

0.478

0.596

0.400

0.148

0.193

0.586

0.105

0.716

0.410

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

expansion

0.10

expansion

0.10

expansion

0.00

‐0.10
0

TWN
1

0.00

‐0.10
0

HPCYCLE_TWN02

IDN
1

0.00

‐0.10
0

HPCYCLE_IDN02

0.10

HPCYCLE_HKG02

KOR

expansion

0.10

expansion

1

0.00

‐0.10
0

THA
1

0.00

‐0.10
0

HPCYCLE_THA02

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

1

HPCYCLE_KOR02

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1HKG

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

0.10

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1Figure 1: Business Cycles for Asian emerging Economies
0.10

SGP

0.05

expansion

0.10

expansion

1

0.00

‐0.05
‐0.10
0

HPCYCLE_SGP02

MYS

0.05
1

‐0.05
0.00

‐0.10
0

HPCYCLE_MYS02

4.00

4.00
expansion

6.00

SGP_HPI

expansion

6.00

TWN_HPI_1

expansion

6.00

TWN_HPI_2

expansion

Notes:
Sources:
RPPI_SR2_2005_TWN_SAL
1

5.00

0

1

4.00

0

1

5.00

0

RPPI_SR1_2005_TWN_SAL

1

5.00

0

2.00

RPPI_DP_2005_HKG_Sal

RHPI_2005_KOR_SAL

2.00

RPPI_2005_SGP_SAL

2.00

expansion

6.00

KOR_SPI

2.00

expansion

6.00

SGP_SPI

expansion

6.00

TWN_SPI

expansion
RSP_2005_TWN_SAL

1

4.00
0

1

4.00
0

1

4.00

0

1

0

‐0.20

RSP_2006_HKG_Sal

RSP_2005_KOR_SAL‐0.10

RSP_2005_SGP_SAL

0.10

‐0.20

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

HKG_SPI

1981M01
1982M01
1983M01
1984M01
1985M01
1986M01
1987M01
1988M01
1989M01
1990M01
1991M01
1992M01
1993M01
1994M01
1995M01
1996M01
1997M01
1998M01
1999M01
2000M01
2001M01
2002M01
2003M01
2004M01
2005M01
2006M01
2007M01
2008M01
2009M01
2010M01
2011M01

KOR_HPI

0
1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

4.00
6.00

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q16.00
1

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

expansion

1981M01
1982M01
1983M01
1984M01
1985M01
1986M01
1987M01
1988M01
1989M01
1990M01
1991M01
1992M01
1993M01
1994M01
1995M01
1996M01
1997M01
1998M01
1999M01
2000M01
2001M01
2002M01
2003M01
2004M01
2005M01
2006M01
2007M01
2008M01
2009M01
2010M01
2011M01

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

HKG_HPI

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

1981M01
1982M01
1983M01
1984M01
1985M01
1986M01
1987M01
1988M01
1989M01
1990M01
1991M01
1992M01
1993M01
1994M01
1995M01
1996M01
1997M01
1998M01
1999M01
2000M01
2001M01
2002M01
2003M01
2004M01
2005M01
2006M01
2007M01
2008M01
2009M01
2010M01
2011M01

6.00

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

2.00
1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

4.00

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

2.00

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1Figure 2: Financial Cycles for Asian NIEs Economies
0.20

0.10

HKG_CREDIT

expansion

0.10

KOR_CREDIT‐0.10

expansion

0.10

SGP_CREDIT

expansion

TWN_CREDIT

expansion
HPCYCLE_TWN_CRE01

1

‐0.10

0.00
0

HPCYCLE_HKG_CRE01

0.00

1

0

HPCYCLE_KOR_CR01

1

0.00
0

HPCYCLE_SGP_CRE01

0.00

1

4.00
‐0.10

0

4.00

5.00

MYS_HPI

expansion

6.00

IDN_HPI

expansion

Notes:
Sources:
RPPI_R14_2005_IDN_SAL

0

1

4.50

0

1

5.00

0

2.00

RHPI_SDL_2005_THA_SAL

2.00

RHPI_2005_MYS_Sal

2.00

expansion

6.00

MYS_SPI

expansion

6.00

IDN_SPI

expansion
RSP_2005_IDN_SAL

1

4.00
0

1

4.00
0

1

0

0.20

‐0.20

RSP_2005_THA_SAL

0.40

‐0.40

RSP_2005_MYS_Sal

1.00

‐0.50

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

THA_SPI

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

4.50
6.00

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

expansion

1

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

THA_HPI

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

5.00

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

4.00
1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

4.00

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1Figure 3: Financial Cycles for ASEAN Economies
THA_CREDIT

expansion

MYS_CREDIT

expansion

IDN_CREDIT

expansion
HPCYCLE_IDN_CRE01

1

0.00
0

HPCYCLE_THA_CRE01

0.20

1

‐0.20

0.00
0

HPCYCLE_MYS_CRE01

0.50

1

4.00
0.00

0

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

expansion
RPPI_SR2_2005_TWN_SAL

Notes:
Sources:

100

80
0

TWN
1

80
0

RPPI_SR1_2005_TWN_SAL
RSP_2005_TWN_SAL

IDN

60

expansion
RPPI_R14_2005_IDN_SAL
1

110

0

120

expansion
RPPI_DP_2005_HKG_Sal

120

KOR

expansion
RSP_2005_KOR_SAL

1

100
80
0

THA

80

expansion
RHPI_SDL_2005_THA_SAL

1

100
0

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

1
120

RHPI_2005_KOR_SAL
HPCYCLE_KOR_CR01

120

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1HKG

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

120

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1

1981Q1
1982Q1
1983Q1
1984Q1
1985Q1
1986Q1
1987Q1
1988Q1
1989Q1
1990Q1
1991Q1
1992Q1
1993Q1
1994Q1
1995Q1
1996Q1
1997Q1
1998Q1
1999Q1
2000Q1
2001Q1
2002Q1
2003Q1
2004Q1
2005Q1
2006Q1
2007Q1
2008Q1
2009Q1
2010Q1
2011Q1Figure 4: Financial Market Fluctuations under Business Cycles for Asian Economies
SGP

expansion
RSP_2005_SGP_SAL

expansion
RSP_2005_MYS_Sal

1

100
80
0

RPPI_2005_SGP_SAL
HPCYCLE_SGP_CRE01

MYS
1

100
80
0

RHPI_2005_MYS_Sal
HPCYCLE_MYS_CRE01

Appendix 1 The periods of Turning Points: Troughs and Peaks
1-A Business Cycles
HKG
KOR
SGP
Troughs
Peaks
Troughs
Peaks
Troughs
Peaks
1983Q1
1984Q2
1981Q4
1984Q1
1985Q4
1987Q3
1982Q3
1984Q1
1986Q2
1990Q1
1988Q1
1988Q3
1985Q3
1987Q2
1992Q2
1994Q1
1990Q4
1994Q1
1987Q4
1988Q4
1995Q1
1996Q1
1995Q3
1997Q2
1989Q2
1989Q4
1996Q3
1997Q3
1998Q4
2000Q4
1990Q2
1991Q4
1998Q3
2000Q3
2002Q1
2002Q3
1993Q1
1997Q2
2001Q4
2002Q2
2003Q2
2008Q1
1998Q3
2000Q3
2003Q2
2004Q1
2009Q1
2001Q4
2002Q2
2005Q1
2006Q1
2003Q2
2003Q4
2006Q3
2007Q3
2005Q1
2008Q1
2009Q1
2010Q2
2009Q1

THA
Troughs
1981Q4
1982Q4
1984Q2
1986Q4
1989Q3
1991Q4
1993Q2
1994Q3
1998Q3
2001Q3
2006Q1
2009Q1
2010Q3MYS
Peaks
1982Q2
1983Q4
1984Q4
1988Q3
1990Q3
1992Q4
1993Q4
1996Q2
2000Q1
2004Q4
2008Q1
2010Q1

1-C Equity Price Cycles
HKG
Troughs
Peaks
1981Q3
1982Q4
1987Q3
1989Q3
1993Q4
1995Q1
1997Q3
1998Q3
2000Q3
2003Q1
2007Q4
2009Q1
2009Q4
2010Q2Troughs
1981Q4
1986Q2
1991Q2
1992Q3
1994Q1
1998Q4
2001Q4
2003Q1
2005Q2
2006Q3
2009Q1

1982Q4
1985Q3
1988Q4
1991Q1
1993Q4
1995Q4
1997Q1
1998Q4
1999Q4
2001Q3
2003Q2
2004Q4
2007Q1
2009Q1

1982Q2
1983Q3
1985Q3
1992Q4
1995Q2
1998Q2
2001Q2
2003Q1
2008Q4

Troughs
1986Q1
1990Q4
1992Q3
1994Q2
1998Q3
2003Q1
2006Q3
2009Q1MYS
Peaks
1983Q2
1985Q2
1990Q2
1993Q4
1999Q2
2003Q4
2006Q1
2007Q4

Troughs
1982Q3
1988Q1
1990Q4
1991Q4
1995Q1
1998Q3
2001Q2
2003Q1
2006Q1
2008Q4

Peaks
1981Q2
1987Q3
1990Q1
1991Q2
1994Q3
1996Q4
2000Q1
2002Q2
2004Q4
2007Q4

Peaks
1990Q1
1991Q4
1993Q4
1996Q1
1999Q4
2006Q1
2007Q4

Troughs

TWN
Troughs
Peaks
1981Q4
1982Q4
1984Q3
1985Q2
1989Q3
1990Q3
1991Q2
1992Q4
1994Q3
1996Q1
1997Q3
1999Q1
2000Q1
2003Q1
2004Q1
2005Q1
2007Q3
2008Q4
2009Q4
2010Q2
2010Q4

1987Q2
1989Q3
1996Q4
1998Q4
2001Q1
2003Q1
2005Q1
2009Q2

1-C Credit Cycles
HKG
Troughs
Peaks
1994Q3
1995Q4
1997Q3
1999Q2
2000Q3
2001Q3
2002Q1
2003Q2
2003Q4
2004Q2
2005Q2
2006Q4
2008Q2
2009Q1

Troughs
1999Q2
2001Q4
2003Q2
2004Q4
2006Q2
2008Q4

Troughs
1982Q4
1987Q2
1993Q3
2001Q2
2004Q1
2008Q2
2010Q1

Notes: The Shaded-dates are the peaks arround the regional and global crises, such as 1997 and 2008, and the previous troughs.

Troughs

Troughs
1982Q3
1985Q3
1987Q3
1990Q2
1994Q2
1999Q4
2002Q4
2005Q2
2007Q1
2008Q3

TWN1
Troughs
Peaks
1992Q4
1997Q1
1997Q3
2001Q2
2002Q1
2005Q3
TWN2
Troughs
Peaks
1999Q1
1999Q3
2001Q2
2003Q3
2004Q3
2008Q1
2008Q4IDN

Peaks
2000Q2
2002Q4
2004Q1
2005Q2
2008Q1

Troughs
1999Q1
2000Q4
2001Q4
2003Q1
2010Q3

Peaks
1982Q4
1983Q4
1985Q3
1987Q2
1990Q4
1992Q3
1995Q1
1997Q4
2000Q3
2003Q2
2009Q1
2010Q3Troughs

Peaks
2000Q2
2001Q2
2002Q2
2003Q3

SGP

Peaks
1981Q1
1983Q3
1986Q1
1989Q4
1992Q1
1997Q3
2001Q3
2004Q2
2005Q4
2007Q3

TWN
Peaks
1981Q3
1983Q4
1987Q3
1990Q2
1993Q4
1996Q2
2001Q4
2004Q1
2008Q3

1982Q2
1987Q1
1988Q1
1993Q1
1994Q2
2000Q3
2002Q4
2006Q1
2010Q2MYS
Peaks
1984Q4
1990Q3
1997Q4
2003Q3
2004Q4
2008Q4

Peaks
1981Q2
1983Q3
1987Q3
1990Q2
1996Q2
2000Q1
2008Q1

1982Q1
1986Q2
1988Q1
1990Q4
1998Q4
2004Q2
2009Q2

KOR
Troughs
1981Q3
1983Q2
1984Q4
1986Q1
1989Q4
1991Q3
1994Q1
1996Q1
1998Q4
2001Q2
2005Q1
2010Q1

THA
Peaks
1990Q2
1993Q4
1997Q1
1999Q4
2005Q2
2007Q4

SGP
Peaks
1986Q4
1988Q3
1990Q4
1997Q2
2000Q2
2002Q3
2003Q4
2007Q1MYS
Peaks
1993Q3
1996Q2
1997Q2
2000Q2
2002Q3
2003Q3
2005Q1
2007Q3
2009Q1

1994Q3
1996Q4
1999Q2
2001Q3
2003Q1
2004Q3
2006Q1
2008Q1
2010Q3

IDN
Troughs
1986Q3
1991Q4
1995Q1
1998Q3
2003Q1
2005Q4
2008Q4

KOR
Troughs

THA
Peaks
1982Q1
1984Q3
1987Q3
1989Q2
1991Q4
1994Q4
1996Q2
1997Q3
1999Q2
2000Q3
2002Q2
2004Q1
2006Q3
2008Q1

SGP
Peaks
1981Q3
1982Q4
1985Q1
1989Q3
1994Q4
1996Q2
1999Q4
2002Q2
2007Q41-B Housing Price Cycles
HKG
Troughs
Peaks
1984Q3
1985Q4
1986Q2
1989Q2
1990Q2
1992Q3
1993Q1
1994Q1
1995Q4
1997Q3
1999Q1
1999Q3
2003Q2
2005Q3
2006Q1
2008Q1
2009Q1IDN
Troughs

KOR
Troughs

THA
Troughs
1982Q1
1984Q3
1986Q2
1991Q3
1998Q3
2000Q4
2004Q2
2006Q3
2009Q1

Peaks
1984Q4
1988Q2
1991Q4
1993Q2
1997Q4
2000Q3
2002Q3
2004Q2
2006Q1
2008Q1

TWN
Troughs
Peaks
1982Q1
1982Q4
1984Q3
1985Q3
1987Q3
1988Q4
1989Q2
1991Q1
1991Q4
1993Q4
1994Q4
1995Q4
1996Q2
1997Q1
1997Q3
1998Q4
1999Q2
1999Q4
2000Q3
2001Q3
2002Q2
2003Q2
2004Q1
2004Q4
2006Q3
2007Q1
2008Q1
2009Q1IDN
Troughs
1982Q1
1986Q3
1987Q4
1989Q2
1993Q1
1994Q2
1999Q4
2002Q2
2007Q1
2010Q1

Peaks
1981Q3
1984Q4
1987Q2
1988Q3
1990Q3
1993Q4
1998Q2
2001Q2
2005Q3
2008Q3Troughs
1981Q3
1982Q4
1984Q2
1987Q1
1991Q4
1993Q2
1997Q2
2003Q1
2009Q3

Peaks
1982Q2
1983Q4
1984Q4
1989Q1
1992Q4
1994Q4
1999Q2
2005Q4