Financial Integration and International
Transmission of Business Cycles:
Evidence from Dynamic Correlations
Jarko Fidrmuc
Kentaro Iwatsubo
Taro Ikeda
June 2010
Discussion Paper No.1007

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANFinancial Integration and International Transmission ofBusiness Cycles: Evidence from Dynamic Correlations
Jarko Fidrmuc *Kentaro Iwatsubo **Taro Ikeda ***June 2010
Abstract
We estimate determinants of dynamic correlations of output comovement of OECD
countries between 1990 and 2008. We show that trade intensity, degree of financial
integration and specialization pattern have significantly different effects on comovements at
different frequencies. This can bias the results using aggregate data or statistical filters. For
example, financial integration is showed to have the highest positive effect for middle
business frequencies, while it is insignificant for short-term frequencies.

JEL-Numbers: E32, F15, F41.
Key words: Business cycle, Transmission, Financial Integration, Dynamic Correlation.

1

Introduction

The impact of economic integration on the international transmission of business cycles
is not unambiguous. On the one hand, higher trade and financial openness mightstrengthen the comovement of economic fluctuations across countries, implying ahigher degree of business cycle convergence. Positive shocks support also growth in*Austrian Central Bank, CESifo Munich, and Comenius University, Department of Applied Mathematics

andStatistics,

Bratislava,

Slovakia.

e-mail:jarko.fidrmuc@lrz.uni-muenchen.deandjarko.fidrmuc@fmph.uniba.sk.
**

Graduate School of Economics, Kobe University, Japan, 2-1 Rokkodai Nada, Kobe 657-8501, Phone:+81-78-803-6875, e-mail: iwatsubo@econ.kobe-u.ac.jp***

Graduate School of Economics, Kobe University, Japan, 2-1 Rokkodai Nada, Kobe 657-8501, e-mail:young_tubo80@msn.com.

1

other countries, while negative shocks can affect negatively also the partner economies
through contagion effects. On the other hand, however, trade and financial integration
may support the degree of specialization of countries.
Similarly, financial linkages could increase comovement via supply and contagion
effects. The supply of foreign capital causes a positive correlation between source andtarget countries. In turn, positive productivity shocks in target country are likely to
attract investment from other economies (Backus et al., 1995) and to increase sectoral
specialization. Thus the effects of financial integration are also unclear.
Using dynamic correlations for different frequencies, we show that the traditional
determinants of international business cycles have statistically different effects: The
short-run cycles, which are responsible for a large part of aggregate fluctuations, cannot
be explained well by financial integration and trade specialization pattern, while shortrun trade demand effects may play a significant role.
The paper is structure as follows. The next section discusses different measures ofbusiness cycles. Section 3 presents a framework of analysis which follows specially theearlier work by Imbs (2004) and Inklaar et al. (2009). These authors propose to use asystem of equation for output correlation, trade intensity and specialization, andfinancial integration. We extend this framework for analysis of comovements at
different frequencies and receive better results that those reported in the previous
literature, as is shown in Section 4. The last section concludes.
2

Methods for Business Cycles Analysis

The previous literature has concentrated mainly on testing whether various indicators
influence correlation of international business cycles. The majority of the papers
follows Frankel and Rose (1998) and use simple de-trended (differenced) indicators of
economic activity, which are used for computation of standard correlations of businesscycles between the countries. However, this measure of business cycles is likely to be
influenced by random events including regional-specific or global shocks. Fiscal
consolidations and for example the recent financial crisis increase correlation of output
between countries, while business-cycles are likely to be less affected by these shocks.
Therefore, several papers have used various statistical filters which distinct
between business cycles and short-run fluctuations. The most standard measure is the
Hodrick-Prescott filter. However, this filter has been often criticized for generating
spurious cycles when filtering macroeconomic time series (Harvey and Jaeger, 1993,2

Canova, 1998). Baxter and King (1995) suggest that the band-pass filter should exclude
frequencies lower than those corresponding to a periodicity of 8 years and higher thanthose corresponding to a frequency of 1.5 years. However, Benati (2001) shows that
also band-pass filter may markedly distort key business cycle stylized facts, as capturedby the cross-correlations and the cross-spectral statistics between the variables ofinterest.
We apply dynamic correlations proposed by Croux et al. (2001). For two variables
yi and yj with spectral density functions Siλ and S jλ and a co-spectrum Cijλ defined for
frequencies λ over the interval -π ≤ λ≤ π, the dynamic correlation, ρ ijλ , is

ρλ =
ij

Cijλ
Siλ S jλ.

(1)

Dynamic correlations do not cover only contemporaneous comovements but also
possible time phase shifts in business cycles because they use the information fromspectral density functions.

3

Empirical Framework

We estimate a system of equations showing the importance of trade, specialization
pattern and financial integration (Imbs, 2004, Inklaar et al., 2008) for dynamic
correlations at different frequencies,ρ λ = α λ + α λT + α λ S + α λ F + ε λ
ij

1

2

ij

3

ij

4

ij

ij

(2)

where ρij stands for the bilateral dynamic correlations of quarterly seasonally adjusted
output in countries i and j, and T, S, and F are indicators of trade intensity,
specialization pattern and financial integration.
However, the explanatory variables may be endogenous. Therefore we estimate asystem of equations. Trade intensity, denoted by T, is defined as the average ratio ofexports, X, and imports, M, related to output, Y,

Tij =1 T X ij + M ij.
∑
T t =1 Yi + Y j

This indicator is explained by the specialization pattern, S, distance between the capitalcities, D, a dummy for geographic contingency, B, a dummy for common languages, L,
and GDP per capita, y,
Tij = β 1λ + β 2λ S ij + β 3λ Dij + β 4λ Bij + β 5λ Lij + β 6λ ( y i + y j ) + η ijλ .

3(3)

Similarly to Imbs (2004) and Inklaar et al. (2008), we use the index of industrial
similarity,

1 T N
Sij = ∑∑ sin − s jn
T t =1 n =1
This index computes the average deviation of industrial shares for countries i and j
using the GGDC total economy database. The trade pattern is explained as

Sij = β1λ + β 2λYiYj + β 3λ yi − y j + β 4λTij + β 5λ Fij + ϑijλ ,(4)where |yi – yj | is the difference of GDP per capita, which is taken as a proxy for
different factor equipment between the countries. Furthermore, the explanatory
variables include also aggregate output, trade intensity, and the intensity of financial
integration.
Finally, we use asset holdings according to IMF to measure the financial
integration,
1 T F + Fij,
Fij = ∑ ij
T t =1 Yi + Y j
ALwhere FA and FL are bilateral financial assets and liabilities. This indicator is explainedby institutional indices of legal origin, LP, according to La Porta et al. (1998),
7

Fij = β1λ + ∑ β kλ ( LPi k + LPjk ) + ς ijλ .

(4)

k =2

We estimate the system of equations by three-stage least squares which reflects
the endogeneity of analyzed variables between 1990 and 2008. All variables are taken
preferably from the IMF and World Bank until other source was declared above.

4

Estimations

Tables 1 to 3 report the results of three-stage least squares estimation of system of
equations defined by (2) to (4) for different frequency intervals. The output equation,(Table 1) confirms that there are important differences between determinants ofcomovements at various frequencies. The best results are found for the business cycle
frequencies, while short-run frequencies cannot be explained well by standard
determinants of comovements.
All standard determinants of comovements are correctly signed and highly
significant for the business cycle frequencies. Trade integration is found to have apositive sign, which is consistent with previous findings. Specialization index is4

negative and significant which also follows earlier literature. This implies that intraindustry trade is the forcing drive of synchronization of business cycles while trade
specialization leads to more divergent output movements. Financial integration is foundto be positive and highly significant in our analysis.
We find slightly different determinants for comovements at the long-run
frequencies. Trade and trade specialization have similar effects on business-cycle andlong-run frequencies. In turn, the coefficient for financial integration becomesinsignificant for the long-run and short-run frequencies. Furthermore, only tradeintegration is significant for short-run frequencies. The picture confirms that trade links
work also through the immediate demand channel, while trade pattern and financial
integration need more time for coming into force.
Our sensitivity analyses for country groups confirm these results. For theEuropean countries, specialization and financial integration are highly significant forbusiness cycles. Trade integration is only marginally significant, but it remains highly
significant for selected frequencies. All these variables are important also for the longrun comovements. Neither determinant, including trade integration, is significant for the
short-run comovements.
The detailed estimations for selected business cycle frequencies show that tradeintegration and specialization are significant for all frequencies. By contrast, financial
integration seems to have hump-shaped effects. The largest effects of the financial
integration are found for medial-range frequencies.
The remaining equations of the system are the same for all frequencies. Table 2
estimates the trade equation. The distance and the geographical adjacency are highly
significant, while language variable is insignificant. Moreover, trade depends positively
on specialization. In turn, trade is insignificant in the specialization equation defined by(4), which depends especially on size of trading economies (Table 4). Finally, theresults for the financial integration equation (5) confirm that the rule of law andaccounting standards are significantly supporting the degree of financial integration(Table 5).

5Conclusions

The transmission of business cycles represents an important field of international
economic and economic policy. The previous literature documented that trade flows and5specialization pattern play an important role in this process. However, less evidence has
been so far found for financial integration.
A possible reason for this, as shown in this paper, may be that financial
integration may have different effects for comovements at different frequencies. In
particular, financial integration is insignificant for the short-run dynamic correlations,while it reaches a peak at the medium business cycle frequencies. In turn, trade is foundto be an important determinant for the whole range of frequencies.
Acknowledgement

We are grateful to the financial support by the Japan Securities ScholarshipFoundation.

6

Table 1: Output Correlation Equation
Variable
A. All Countries
Intercept
TradeSpecialization
Financial Integration

Short-Run
Frequencies
0.208(0.148)0.059 **
(0.025)-0.003(0.003)-0.756(1.334)

Business Cycle
Frequencies
0.460(0.247)0.124(0.041)-0.010(0.005)4.857(2.225)Long-Run
Frequencies
*
***
**
**

1.152 ***
(0.354)0.140 **
(0.059)-0.027 ***(0.007)3.756(3.189)B. European Countries
Intercept

π/3

π/5

π/16

0.010
(0.215)0.049(0.036)0.000(0.004)4.118 **
(1.936)0.402(0.265)0.148 ***
(0.044)-0.009 *(0.005)5.461 **
(2.392)1.067(0.343)0.142(0.057)-0.025(0.007)
4.022(3.093)0.101
0.549 ***
0.851 ***
0.081
0.637 ***
0.841 ***
(0.118)(0.200)(0.316)(0.173)(0.205)(0.305)
**
Trade
0.020
0.074 *
0.123 **
0.018
0.080
0.119 **
(0.022)(0.038)(0.059)(0.033)(0.039)(0.058)***Specialization-0.001-0.013 ***-0.023 ***-0.002-0.015-0.022 ***(0.002)(0.004)(0.007)(0.004)(0.004)(0.006)******
Financial Integration
0.999
6.714 ***
8.125 ***
4.773
6.933
8.042 ***
(1.037)(1.814)(2.852)(1.547)(1.868)(2.759)Notes: Long-run frequencies correspond to the low frequency below π/16. Business cycle frequencies correspond to the traditional business cycle frequency between π/16 andπ/3 (a period between 1.5 and 8 years). Short-run frequencies represent the frequencies over π/3.

7Table 2: Trade Equation
Variables
Intercept
Distance
BorderCommon Language
GDP per capita ProductSpecialization

OECD
0.327
(2.053)-0.518 ***(0.200)5.252 ***
(0.838)-0.586(0.943)0.033(0.074)0.087 **
(0.035)

Europe8.026 ***
(1.332)-1.306 ***(0.196)1.604 ***
(0.387)0.156(0.552)-0.346 *(0.199)0.033 **
(0.013)OECD
44.815 ***(1.308)0.113 **
(0.046)0.024(0.031)0.250(0.904)-0.003(0.002)Europe51.229 **(22.224)
0.093(0.114)0.607(0.801)2.049(16.764)-0.005(0.010)

Table 2: Specialization Equation
Variables
Intercept
GDP ProductGDP per capita Difference
Trade
Financial Integration

Table 3: Financial Integration Equation
Variables
Intercept
Rule of law
Rating on accounting standards

OECD-0.038 **(0.017)
0.195 **
(0.082)0.181(0.132)8

Europe0.037(0.035)-0.095(0.183)0.059(0.169)References

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