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International capital flows, boom-bust cycles, and business cycle synchronization in the asia pacific region.

I. INTRODUCTION

Over the past decade, a number of Asia Pacific countries have liberalized their financial markets to foreign capital by reducing restrictions on inward and outward capital flows. Increased capital flows due to financial integration can generate substantial effects on business cycles. Large capital inflows due to financial market liberalization can generate an initial surge in investment and asset price bubbles followed by capital outflows and recession, the so-called boom-bust cycles. In the worst case, the boom-bust cycles can generate a sudden reversal of capital flows and eventually financial crises. (1) On the other hand, by allowing domestic residents to engage in international financial asset transactions, financial market opening can reduce the volatility of some macroeconomic variables such as consumption through risk-sharing. (2)

What are the macroeconomic effects of capital flows, in particular on business cycle fluctuations? Do business cycles become less volatile and more synchronized across countries as the degree of financial integration increases? Understanding business cycle implications of capital flows is also important for analyzing welfare implications of financial market liberalization policies as well as international monetary arrangements.

This article focuses on the effects of capital flows due to financial market liberalization on business cycles, in particular co-movements across countries. (3) We aim to shed some lights on this issue by providing detailed stylized facts on capital flows and business cycles in the Asia Pacific region and by empirically analyzing the relationship between capital flows and business cycles. For empirical analysis, we adopt the vector auto-regression (VAR) method. First, we identify the capital flow shocks and examine their effects on cyclical movements of key macroeconomic variables in each country. Then, we examine whether these effects are consistent with the boom-bust cycle theory. By further analyzing the cross-country correlation of capital flow shocks, we can analyze the role of capital flows in explaining business cycle synchronization.

Economic theory does not provide a unanimous prediction for the effects of capital flows on co-movements of business cycles. Financial market integration can increase business cycle co-movements as macroeconomic effects of capital flows in different countries follow a similar pattern through various channels of contagion and common shocks. (4) However, co-movements of output can decrease as allocation of capital becomes more efficient, allowing production to become more specialized. (5) Other variables can also affect the relationship between capital flows and business cycles, including monetary and fiscal policies, the nature of underlying shocks in the economy, etc. (6)

Using the data of 12 Asia Pacific countries, we find the following stylized facts of business cycles. First, business cycles in the five Asian Crisis countries are highly synchronized and follow business cycles in Japan, while they differ from business cycles in Australia. On the other hand, Greater China, including Hong Kong and Taiwan, shows similar cyclical movements among themselves. Second, in general, business cycles since the 1990s are more synchronized across countries than those in the 1980s, which support the view that financial and trade integration increases business cycle synchronization in Asia.

We find empirical evidence that positive capital flow shocks (capital inflows) affect output, consumption, and investment positively in most countries, which is consistent with the boom-bust cycle theory. In addition, capital flow shocks are positively correlated across the Asian Crisis countries (except for the Philippines). These two results imply that capital flow shocks can explain business cycle synchronization among the Asian Crisis countries to some extent.

The remaining sections of this article are organized as follows. Section II provides literature review on the relationship between financial integration and business cycles. In Section III, we analyze trends and stylized facts of business cycles in the Asia Pacific region. In particular, we investigate how volatility of business cycles has changed over time and whether we can find any evidence of business cycle synchronization in the region. We analyze 12 sample countries in the Asia Pacific region, including 5 Asian Crisis countries (Indonesia, Korea, Malaysia, the Philippines, and Thailand), China, Singapore, Taiwan, Hong Kong, Japan, Australia, and New Zealand. Section IV provides an empirical analysis of the relationship between capital flows and business cycles. We analyze how capital flow shocks affect various macroeconomic variables and investigate whether capital flow shocks generate boom-bust cycles in the region. We also analyze the properties of capital flow shocks identified in our model. In particular, we investigate whether the estimated capital flow shocks are driven by exogenous economic events and correlated across countries. Section V concludes the paper.

II. LITERATURE REVIEW

This section explains different theories on the effects of economic integration on the symmetry of business cycles and documents empirical studies on this issue. (7) Financial market integration can decrease co-movements of output by increasing industrial specialization (Kalemli-Ozcan, Sorensen, and Yosha 2001). Countries with integrated international financial markets can ensure against country-specific shocks through portfolio diversification; therefore, such countries can afford to have a specialized production structure. That is, financial market integration allows firms to take full advantage of comparative advantage and engage in production specialization, which in turn increases the asymmetry of output as long as industry-specific shocks exist.

Empirical analysis confirms a decrease in cross-country correlation of output in the 1990s. This can be explained by a decrease in cross-country correlation of productivity shocks combined with increased financial market integration (Heathcote and Perri 2002). Degree of financial market integration endogenously and positively responds to the correlation of shocks. That is, as productivity shocks become less correlated, potential welfare gains from portfolio diversification increase, as does the degree of financial market integration.

However, countries with liberalized capital accounts can be significantly more synchronized, even though they are more specialized (Imbs 2004). A large body of literature on contagion argues that capital flows in different countries, in particular developing countries in the same region, are synchronized through various channels of financial contagion including herd behavior and information asymmetry among others (Calvo and Mendoza 2000; Mendoza 2001). International investors may classify different countries in a single group and make region-based investment decisions. In addition, capital flows can be highly synchronized if shocks that determine capital flows are positively correlated or spill over across countries, or if developing countries go through a financial liberalization process at the same time. As capital inflows have significant effects on business cycles (so-called "boom-bust" cycles), if capital flows are highly correlated and have similar effects on business cycles, then financial integration can contribute to synchronization of business cycles.

III. TRENDS AND STYLIZED FACTS OF BUSINESS CYCLES

This section documents the main characteristics of business cycles of the selected countries in the Asia Pacific region. (8) We use the annual data from the International Financial Statistics and examine volatility (measured by standard deviation) and co-movements (measured by cross-country correlation) of output, consumption, and investment in these countries. (9) The sample period is from 1980 to 2006 and all the data are real (by gross domestic product [GDP] deflator) and Hodrick-Prescott filtered (with filtering parameter set at 100). As we are interested in changes in business cycle statistics as financial markets are liberalized, we examine business cycles in different sub-sample periods: 1980-1989 and 1990-2006. For the second period, we use the data with and without the Asian Crisis period because the data for that period may distort the statistics.

We focus on two aspects of business cycles related to financial market liberalization and examine whether the stylized facts derived from the data support the theoretical predictions studied in the previous section. First, we investigate how much the volatility of business cycles has changed over time. As financial markets develop over time, volatility of consumption is likely to decrease through consumption smoothing and risk-sharing channels unless output volatility increases substantially. However, the impact on volatility of output is more ambiguous as argued in the previous section. Second, we focus on the degree to which business cycles in the region are synchronized and the changes in the degree of business cycle synchronization over time. We expect that business cycles in this region become more synchronized due to the region's trade integration and high portion of intra-industry trade. However, the effects of financial integration on business cycle co-movements are ambiguous as argued in the previous section.

A. Volatility of Business Cycles

Table 1 presents volatility of output, relative volatility of consumption, and investment in four different periods: the whole period, the 1980s, and the 1990s with and without the Asian Crisis period. The output volatility is low with a standard deviation ranging from 1.73 to 2.70 in more developed countries in the region: Japan, Australia, and New Zealand. On the other hand, less developed countries in the region exhibit higher volatility: 5.93 in Thailand, 5.03 in Indonesia, and 4.34 in Malaysia. Developed countries tend to have more stable industrial structures and output streams. Small countries that depend on natural resources for their main products tend to have volatile output streams due to volatile prices (terms of trade) of primary goods. Moreover, the share of agricultural activity is higher and the shares of the industry and service sectors are lower in the less developed countries. The agricultural sector output is highly variable as it is heavily affected by extremely volatile productivity and price shocks.
TABLE 1

Volatility of Business Cycles

 1980- 1980- 1990- 1990-2006
 2006 1989 2006 (Without
 Crisis)

Standard deviatioin of output

Korea 2.76 1.50 3.17 2.35

Indonesia 5.03 1.28 6.22 5.63

Malaysia 4.34 3.14 4.33 3.73

Philippines 3.90 5.49 2.90 2.95

Thailand 5.93 3.38 6.51 6.43

Japan 2.70 0.98 2.79 2.83

China 3.65 3.24 3.36 3.49

Singapore 3.94 3.61 3.89 4.13

Taiwan 2.43 2.51 2.23 2.24

Hong Kong 3.77 2.87 4.13 4.05

Australia 1.73 1.87 1.50 1.52

New Zealand 2.60 2.23 2.45 2.52

Relative standard deviation of
consumption

Korea 1.27 0.72 1.28 1.23

Indonesia 1.03 2.37 0.88 0.97

Malaysia 1.36 1.38 1.29 1.23

Philippines 0.91 0.69 1.08 1.10

Thailand 0.84 0.64 0.83 0.78

Japan 0.81 0.82 0.91 0.92

China 0.98 0.87 0.62 0.61

Singapore 0.84 1.05 0.76 0.59

Taiwan 1.23 1.51 1.05 1.08

Hong Kong 1.05 0.84 1.16 1.18

Australia 0.52 0.45 0.68 0.60

New Zealand 0.93 0.86 1.10 1.14

Relative standard deviation of
investment

Korea 4.47 3.42 4.34 3.56

Indonesia 4.13 7.44 3.98 3.96

Malaysia 4.23 4.82 4.05 4.09

Philippines 4.33 4.55 2.82 2.34

Thailand 3.47 3.14 3.38 3.16

Japan 2.41 5.60 1.89 1.84

China 2.21 2.32 2.36 2.42

Singapore 3.40 2.44 3.84 3.67

Taiwan 4.90 5.84 4.60 4.59

Hong Kong 3.01 4.71 2.55 2.40

Australia 4.34 3.57 4.03 4.13

New Zealand 3.86 4.07 3.52 3.53


Comparison of output volatility in the two periods shows mixed results. Five countries show significant increases (Korea, Indonesia, Malaysia, Thailand, and Japan), one country shows a significant decrease (the Philippines), and the remaining countries do not experience significant changes over time. Except for the Philippines, the five Asian Crisis countries exhibit higher volatility of output in the 1990s compared to the 1980s. This result is consistent even when the crisis period is excluded.

According to the consumption smoothing property in the inter-temporal current account model, consumption should be less volatile than output (Obstfeld and Rogoff 1996). Countries, when facing positive shocks, lend to foreign countries to smooth the consumption stream over time, vice versa. However, in the table, we observe that this is not the case in some of our sample countries. (10) The table shows that consumption volatility is significantly less than output volatility in seven countries, mostly in more developed countries in the region such as Japan and Australia. Developed countries can smooth their consumption by using various risk-sharing instruments. As financial markets develop, developing countries should be able to gain access to the risk-sharing instruments and reduce the volatility of their consumption stream. However, consumption volatility does not change much over time and no explicit pattern is detected in the table.

Investment is three to four times more volatile than output in the table, which is the typical result in other empirical and simulation studies (Baxter and Crucini 1995; Kim, Kose, and Plummer 2001). Investment volatility in China, Hong Kong, and Japan is among the lowest with a relative standard deviation of less than or around three, while investment in the five Asian Crisis countries is quite volatile with a relative standard deviation higher than four. We do not find any significant changes in investment volatility from the 1980s to the 1990s, except for Indonesia and Japan, where investment volatility significantly decreases.

Including the Asian Crisis period in the data for the 1990s does not significantly change the statistics for all three variables. In particular, volatility does not change much by including or excluding the crisis period in the data. In conclusion, we find that output volatility increases in the 1990s in many countries and consumption smoothing is not realized in some countries as consumption volatility is higher than output volatility.

B. Co-Movements of Business Cycles

To illustrate the degree to business cycle synchronization across countries, we calculate cross-country correlation of output in Table 2. The first panel shows the results from the entire sample period. A significant and positive correlation exists across most countries, except for Australia. Business cycles of Australia are negatively correlated with five Asian countries and two countries show near zero correlation. This is not surprising because the industrial structure of Australia is quite different from the typical structure in Asian countries. Greater China--China, Hong Kong and Taiwan--shows high positive correlation among each other. This can be explained by the fact that the three economies are in the same economic zone. (11) A high correlation between Malaysia and Singapore can be explained in the same context.
TABLE 2

Cross-Country Corrclation of Output

 Korea Indonesia Malaysia Philippines Thailand Japan

1980-2006

Indonesia 0.70

Malaysia 0.48 0.83

Philippines 0.39 0.48 0.49

Thailand 0.74 0.89 0.76 0.59

Japan 0.62 0.71 0.42 0.55 0.77

China 0.26 0.43 0.15 -0.19 0.25 0.26

Singapore 0.25 0.53 0.72 0.54 0.59 0.37

Taiwan 0.46 0.31 0.06 0.23 0.3 0.51

Hong Kong 0.74 0.69 0.39 0.46 0.64 0.69

Australia 0.06 -0.21 -0.43 -0.07 -0.16 0.08

New Zealand 0.47 0.43 0.11 0.21 0.32 0.31

1980-1989

Indonesia 0.05

Malaysia -0.13 0.53

Philippines 0.26 0.48 0.63

Thailand 0.35 0.64 0.57 0.77

Japan 0.18 0.49 0.29 0.32 0.82

China 0.30 -0.47 -0.65 -0.78 -0.38 0.06

Singapore -0.06 0.53 0.99 0.69 0.56 0.23

Taiwan 0.80 0.29 -0.30 -0.01 0.33 0.33

Hong Kong 0.78 0.43 -0.01 0.33 0.34 0.14

Australia 0.14 0.45 -0.17 -0.16 0.43 0.77

New Zealand 0.68 0.31 -0.11 -0.03 0.11 0.05

1990-2006

Indonesia 0.82

Malaysia 0.82 0.93

Philippines 0.42 0.59 0.41

Thailand 0.83 0.95 0.88 0.49

Japan 0.60 0.85 0.70 0.76 0.79

China 0.51 0.83 0.75 0.52 0.85 0.81

Singapore 0.43 0.55 0.57 0.37 0.61 0.56

Taiwan 0.38 0.55 0.61 0.46 0.48 0.64

Hong Kong 0.75 0.87 0.78 0.61 0.85 0.87

Australia -0.12 -a27 -0.35 0.43 -a29 -0.12

New Zealand 0.56 0.70 0.53 0.80 0.72 0.65

 China Singapore Taiwan Hong Australia
 Kong

1980-2006

Indonesia

Malaysia

Philippines

Thailand

Japan

China

Singapore 0.13

Taiwan 0.49 0.21

Hong Kong 0.52 0.42 0.69

Australia 0.2 -0.19 0.43 0.22

New Zealand 0.66 0.13 0.51 0.68 0.41

1980-1989

Indonesia

Malaysia

Philippines

Thailand

Japan

China

Singapore -0.71

Taiwan 0.46 -0.28

Hong Kong 0.05 0.09 0.74

Australia 0.41 -0.24 0.59 0.29

New Zealand 0.32 -a04 0.74 0.83 0.30

1990-2006

Indonesia

Malaysia

Philippines

Thailand

Japan

China

Singapore 0.78

Taiwan 0.68 0.77

Hong Kong 0.83 0.73 0.65

Australia -0.24 0.07 -0.09 -0.03

New Zealand 0.69 0.49 0.28 0.65 0.28

Note: Negative coefficients are italicized. Bold numbers in the
bottom panel indicate that correlation coefficients increase in
the second period.


The seven Asian Crisis countries (including Singapore and Hong Kong) show positive output correlation among each other and their business cycles are positively correlated with those in Japan. This indicates that Japan has been leading the business cycles in that region. McKinnon and Schnabl (2002) showed that the yen/dollar exchange rate significantly affects business cycles in the East Asian countries through trade and foreign direct investment (FDI) channels. For example, depreciation of the yen in 1995 significantly slowed down the East Asian export expansion, while yen appreciation accelerates Japanese FDI into the East Asian countries. Bayoumi and Eichengreen (1999) find that the correlation of supply shocks in the region is especially high for two groups, with Japan and Korea in one group and Indonesia, Malaysia, and Singapore in the other. Loayza, Lopez, and Ubide. (2001) examine common patterns in aggregate demand and supply shocks with a different methodology. They find strong co-movements for two groups: Japan, Korea, and Singapore make up one group, and Indonesia, Malaysia, and Thailand, in the other group. These results indicate that there are two different business cycles in the region, even though the East Asian countries show strong co-movements as a whole.

Comparison of the data in the 1980s and 1990s proves that business cycles are more synchronized in the 1990s. We examine this property by comparing the number of negative cross-country correlations of output in the two periods. We observe negative correlation in 17 country pairs during the 1980s, while the number decreases to 8 in the 1990s. (12) only Australia displays a negative correlation. From a total of 66 pairs, 47 pairs show that correlation increases from the 1980s to the 1990s.112 In fact, correlation coefficients are significantly positive in most of the 47 cases; only two pairs exhibit a correlation coefficient of less than 0.4.

Empirical results on business cycle co-movements in previous studies were mixed, depending on sample countries and periods. Some document that the correlation of output decreases over time, in particular in the 1990s. Heathcote and Perri (2002) showed that output correlation among the United States, Europe, Canada, and Japan dropped from 0.76 to 0.26. On the other hand, Kose, Prasad, and Terrones (2003a), using the data for 21 industrial and 55 developing countries, showed that output correlation in general increased in the 1990s from the previous periods. This is mostly due to the industrial countries in the sample. The empirical results in this article support the view that business cycles become more synchronized as financial markets are liberalized. (13)

In conclusion, we can summarize the main characteristics of the business cycle co-movements as follows. First, business cycles in Australia are different from those in the East Asian countries. Second, business cycles in the five Asian Crisis countries are highly synchronized and follow business cycles in Japan. Third, the countries in Greater China, which encompasses Hong Kong and Taiwan, show similar cyclical movements. Finally, business cycles in general are more synchronized across countries in the 1990s than in the 1980s, which support the view that financial integration increases business cycle synchronization.

IV. CAPITAL FLOWS AND BUSINESS CYCLES: EMPIRICAL STUDIES

In this section, we investigate how capital flow shocks affect the business cycle dynamics of the Asia Pacific countries: for example, whether capital flows generate boom-bust cycles, and whether capital flows help explain the synchronization of the business cycles in the Asian countries. Capital flows, especially after the financial market liberalization, may increase the volatility of business cycles by creating boom-bust cycles, in particular fluctuations in investment, consumption, exchange rate, and other asset prices. Furthermore, if capital flows are positively correlated across countries, due to simultaneous capital market liberalization in Asian countries or due to the herd behavior of international investors or due to common shocks, the boom-bust cycles in each country may imply the synchronization of the business cycles.

For empirical methodology, we adopt the VAR estimation method to extract the shocks to capital flows, to analyze how shocks to capital flows affect the various macroeconomic variables in each country, and to examine how the shocks to capital flows are correlated across countries. (14)

A. VAR Model

We assume that the economy is described by a structural-form equation

(1) G(L)[y.sub.t] = [e.sub.t]

where G(L) is a matrix polynomial in the lag operator L, [y.sub.t] is an n x 1 data vector, and et is an n x 1 structural disturbance vector.15 We assume that et is serially uncorrelated and var([e.sub.t]) = A, which is a diagonal matrix where the diagonal elements are the variances of structural disturbances. That is, structural disturbances are assumed to be mutually uncorrelated.

We can estimate a reduced-form equation (VAR)

(2) [y.sub.t] = B(L)[y.sub.t - 1]+ [u.sub.t],

where B(L) is a matrix polynomial in lag operator L and var([u.sub.t]) = [SIGMA].

There are several ways of recovering the parameters in the structural-form equation from the estimated parameters in the reduced-form equation. The identification schemes under consideration impose restrictions on contemporaneous structural parameters only. Let [G.sub.0] be the contemporaneous coefficient matrix in the structural form, and let [G.sub.0](L) be the coefficient matrix in G(L) without the contemporaneous coefficient [G.sub.0]. That is,

(3) G(L) = [G.sub.0]+ [G.sup.0] (L).

Then, the parameters in the structural-form equation and those in the reduced-form equation are related by

(4) B(L) =-[G.sub.0.sup.-1][G.sup.0](L).

In addition, the structural disturbances and the reduced-form residuals are related by

(5) [e.sub.t] = [G.sub.0][u.sub.t],

which implies

(6) [sigma] = [G.sub.0.sup.-1][LAMBDA][G.sub.0.sup.-1].

In the method proposed by Sims (1980), identification is achieved by Cholesky decomposition of the reduced-form residuals, A. In this case, [G.sub.0] becomes triangular so that a recursive structure, that is, the Wold-causal chain, is assumed. In a general non-recursive modeling strategy suggested by Blanchard and Watson (1986) and Sims (1986), maximum likelihood estimates of [LAMBDA] and Go can be obtained only through the sample estimate of [SIGMA]. The right-hand side of Equation (6) has n x (n + 1) free parameters to be estimated. As [SIGMA] contains n x (n + 1)/2 parameters, by normalizing n diagonal elements of [G.sub.0] to Is, we need at least n x (n - 1)/2 restrictions on [G.sub.0] to achieve identification. In this generalized structural VAR approach, [G.sub.0] can be any structure (non-recursive). In this article, recursive modeling is used.

B. Basic Model and Effects on Output

We construct a basic model to examine the effects of capital flow shocks on output. The basic model includes three variables, (CUR, RGDP, CAP}, where CUR is the current account (as the ratio to the trend GDP), RGDP is the log of real GDP, and CAP is the capital account (as the ratio to the trend GDP). (16) A constant term and four lags are assumed. CAP and RGDP are included in the model as they are primary variables of interest; we examine the effects of capital flows or capital account on the real GDP. CUR is included to control the capital account movements that depend on current account movements as some capital account movements are often related to the financing of current account imbalances and we are interested in extracting autonomous capital flows.

The basic model uses a recursive structure, in which the ordering of the variables is {CUR, RGDP, CAP}, where the contemporaneously exogenous variables are ordered first. With this ordering, the shocks to capital flows are extracted by conditioning on the current and lagged CUR and RGDP, in addition to their own lagged variables. We condition on the current (and lagged) CUR as current account imbalances are often financed by capital account. We exclude such endogenous movements of capital flows from the shocks to capital flows. In addition, we condition on the current (and lagged) real GDP as changes in the real GDP may affect the capital account. For example, an increase in the real GDP may attract more capital, and improve the capital account. We exclude the endogenous movements of capital flows due to the real GDP changes from the shocks to capital flows as we would like to infer the effects of capital flow shocks to real GDP. (17)

The sample period is 1990-2006, during which capital account was liberalized in these Asian-Pacific countries (de Brouwer 1999, 2001; Grenville 1998). We consider two samples, one with the Asian Crisis period and the other without it (dropping 1997:3-1998:2). We relate the capital flow shocks identified in the model to the financial market liberalization and the global common shocks under a more liberalized financial market. If the capital account had been tightly controlled (i.e., China), the shocks to capital flows in our model or autonomous capital flows would have been very small as the capital account should have been directed to finance the current account imbalances (note that our model identifies capital flow shocks, by controlling for the current account movement). Therefore, by examining the effects of autonomous capital account shocks during the sample period, we can infer the consequences of capital account liberalization.

We use quarterly data for the estimation as monthly data is not available for most countries. We consider eight countries for which quarterly data series are available for most of the sample period. They are Korea, Japan, Indonesia, Thailand, the Philippines, Taiwan, Australia, and New Zealand. (18) Data sources are International Financial Statistics, Bank of China, and BIS Database.

The impulse responses to CAP shocks over 3 years are reported in Figure 1 for the sample including the crisis period and Figure 2 for the sample dropping the crisis period. Dotted lines are one standard error bands. The scale represents percentage changes. At the top of each column, the country names are denoted. At the far left of each row, the name of each responding variable is reported.

First, we explain the results for the sample including the crisis period. In response to positive CAP shocks, the real GDP tends to increase in all countries. In New Zealand, the real GDP decreases in the very short-run but increases over time. In Taiwan, the increase is not clear, considering the wide error band. In all other countries, the increase in real GDP is clear. The positive effect of capital inflows is significant in most countries, including all crisis countries under consideration, and quite persistent in many countries. Positive effects are especially strong in the four Asian Crisis countries (Korea, Indonesia, Thailand, and the Philippines). In Indonesia, Thailand, and the Philippines, positive effects are different from 0 with more than 84% probability for more than 4 years. In Korea, positive effects are different from 0 with more than 84% probability for more than one and a half years. In other countries such as Australia and Japan, positive effects are different from 0 with more than 84% probability for more than 1 year. The results for the sample excluding the crisis period, reported in Figure 2, are not much different, although the persistence of real GDP responses tends to be smaller.

C. Effects on Other Macro Variables

We modify the basic model to examine the effects of capital flow shocks on other macroeconomic variables. The modified model uses a recursive structure, in which the ordering of the variables is {CUR, X, CAP}, where X denotes the variable of interest. With this ordering, the shocks to capital flows are extracted by conditioning on the current and lagged CUR and X, in addition to their own lagged variables. We condition on the current (and lagged) CUR and X as before. First, the current account imbalances are often financed by capital account, and we would like to exclude such endogenous movements of capital flows from the shocks to capital flows. Second, we condition on the current (and lagged) X as changes in X may affect the capital account. (19)

We include (real) consumption, (real) investment, and the real effective exchange rate as X. Each variable is used as a log form. To construct real consumption and real investment, nominal data are deflated by using a GDP deflator. Note that an increase in the real exchange rate is a real exchange rate appreciation. (20)

Figures 3 and 4 report the results for the period including and excluding crisis period, respectively. The first two rows report the responses of consumption ("CONS") and investment ("INV"). In all countries, at least one of the two variables (consumption and investment) increases. For the full sample period (Figure 3), both consumption and investment increase in all countries but in Taiwan, Indonesia, and the Philippines only the investment increases. For the sample period excluding the crisis period (Figure 4), one or two variables increase in all countries. From this analysis, we can infer that the increase in output following capital flow shocks is mostly due to the increase in consumption and investment because the current account negatively responds to capital flow shocks (Figures 1 and 2).

The third row reports the real effective exchange rate ("RER"). As we expected, real exchange rate appreciation is observed in most countries. In particular, a clear exchange rate appreciation is found in the four Asian Crisis countries, even in the sample excluding the Asian Crisis period. For the other four countries, real exchange rate appreciation is found in New Zealand and Australia, but not in Japan and Taiwan.

D. Properties of Estimated Capital Flow Shocks

The validity of the VAR results in the previous section depends on the identification of shocks, whether capital account shocks represent exogenous changes in capital flows, for example, due to capital account liberalization or due to abrupt changes in the behavior of international investors as in the financial crisis or due to global common shocks. In this part, we examine whether the estimated capital flow shocks actually represent such shocks by plotting cumulative capital flow shocks for each country and relating them to economic events occurred.

Figure 5 plots identified cumulative capital account shocks in each country. (21) For Asian Crisis countries, we observe positive capital flow shocks during the period 1994-1996 when these countries actively embarked on financial market deregulation and opening (de Brouwer 1999; Furman and Stiglitz 1998; Kim, Kose, and Plummer 2001, 2003). For example, Korea allowed non-residents to directly purchase stocks of Korean companies up to 3% per individual in 1992 and this share increased to 23% in May 1997. As a result, the external debt in these crisis-hit countries increased dramatically for 3 years from 1994 to 1996.

This period also coincides with low world interest rate and the appreciation of yen. Yen appreciation increased Japanese overseas direct investment in East Asia. Low interest rates in the industrial countries including Japan produced the portfolio flows to the East Asian economies. On the other hand, the graphs show negative capital flow shocks during the crisis period 1997-1998 as large current account deficits turned into surpluses.

Australia and New Zealand recorded persistent current account deficits throughout the 1990s. For Australia, we observe positive capital flow shocks from the mid-1990s when the country persistently marked current account deficits. For New Zealand, the capital inflows continued until 1997 and the capital account reversed into deficits during 1998-2000. In contrast, Taiwan experienced current account surpluses and net capital outflows before the Asian Crisis. Thus, for Taiwan, we observe negative capital flow shocks in 1995-1996.

Finally, we observe that capital flows into most of these countries in the mid-2000s. We also observe that capital flows out of some countries at the end of the sample period, in which the global financial crisis started.

E. Synchronization of Capital Flows and Business Cycles

In the previous parts, we have shown that a positive shock to capital flows increases output in most countries, and the increase in output is mostly due to a boom in consumption and investment. These findings, especially for the case of the full sample including the crisis period, are consistent with the "boom-bust" cycle theory following the financial market liberalization. In our model, a big surge in capital inflows after the financial market liberalization can be captured as a positive shock to capital flows, and such a positive shock leads to a boom. Later, when capital flows are reversed, capital outflows can be captured as negative shocks to capital flows in our model, and such a negative shock leads to a bust stage.

However, the evidence alone is not enough to support the hypothesis that capital flow shocks or the financial market liberalization process increases business cycle synchronization in the Asia Pacific region. Only when capital flow shocks are highly correlated across countries in the region, can they increase co-movements of business cycles. Otherwise, capital flow shocks may not contribute to business cycle synchronization.

In this regard, Table 3 reports the cross-country correlations of the capital flow shocks identified in our model, for the periods with and without the crisis. For the sample period with the crisis, the correlation is positive in most cases. Negative correlations are found only in 6 out of 28 cases. Therefore, as shown in the previous section, as capital flow shocks have similar effects on business cycles across countries, we can conclude that capital flow shocks contribute to business cycle synchronization among the crisis countries. For the sample period without the crisis, there are more cases of negative correlations, but same conclusion holds. In addition, the average of positive correlations is 0.17 but the average of negative correlations is -0.09. That is, the case of positive correlation is stronger than the case of negative correlation.
TABLE 3

Cross-Country Correlation of Cumulative Capital Flow Shocks
 Korea Indonesia Philippines Thailand Japan Taiwan

Iiichilling
The crisis
period

Indonesia 0.41

Philippines 0.13 0.14

Thailand 0.00 0.16 0.06

Japan 0.29 -0.07 -0.13 0.04

Taiwan 0.02 0.13 -0.02 0.05 0.04

Australia 0.27 0.02 -0.24 0.02 0.19 0.00

New Zealand 0.17 0.18 -0.12 0.20 0.27 -0.21

Excluding
the crisis
period

Indonesia 0.09

Philippines 0.04 -0.12

Thailand -0.04 0.10 0.00

Japan 0.23 -0.02 -0.14 -0.02

Taiwan -0.10 0.13 -0.06 -0.05 0.03

Australia 0.39 0.18 -0.24 -0.02 0.19 0.01

New Zealand 0.23 0.29 -0.10 0.17 0.27 -0.21

 Australia

Iiichilling
The crisis
period

Indonesia

Philippines

Thailand

Japan

Taiwan

Australia

New Zealand 0.20

Excluding
the crisis
period

Indonesia

Philippines

Thailand

Japan

Taiwan

Australia

New Zealand 0.20


We suggest two possible reasons to explain why capital flow shocks are positively correlated. First, the timing of financial market liberalization in those countries was similar, and each country experienced a boom-bust cycle after the liberalization. Thus, the financial market liberalization process itself contributes to the synchronization of the business cycles. Second, given some extent of openness in the financial markets, contagion through financial channels contributed to similar capital flows in these countries. Due to information cascade, international investors classify these countries in the same group and apply a single investment decision for the whole group. Combined with herd behavior, financial contagion contributed to the synchronization of capital flows and eventually of business cycles.

V. CONCLUSION

The relationship between financial integration and co-movements of business cycles is not unambiguous, both theoretically and empirically. In this article, we first document business cycle synchronization in a number of the Asia Pacific countries and explain the phenomenon by examining financial market liberalization and capital flows. We find that business cycle synchronization among the Asian Crisis countries in the 1990s can be at least partially explained by synchronization of capital flows and the ensuing boom-bust cycles after the financial market liberalization. That is, business-cycle synchronization increased mostly because of common capital inflow shocks, rather than via direct contagion through commercial capital flows. Therefore, the results imply that financial market liberalization is likely to synchronize business cycles across a group of countries. This is an interesting finding as recent studies using data from developed countries often conclude the opposite.

Understanding the effects of capital flows on business cycle co-movements has important implications on various issues. First, potential welfare gains from international risk-sharing highly depend on the degree of business cycle synchronization across countries. When countries follow similar business cycles, it is less efficient to share risks across countries. If financial market liberalization and capital flows increase business cycle co-movements, then potential welfare gains from financial market liberalization would be lower than the level calculated from the existing level of business cycle co-movements. Therefore, potential welfare gains from financial market liberalization might be over-estimated.

Second, the findings of this article can provide implications on financial market liberalization policies. In implementing financial market liberalization policies, policymakers should consider the effects of the speed and sequencing of such policies on business cycles and eventually on welfare. Finally, our results have implications on regional monetary and financial integration in terms of optimum currency area criteria. For example, one of the conditions for an optimum currency area is the presence of similar business cycle movements in the potential candidate countries.

When most emerging East Asian countries started to liberalize their financial markets in the early 1990s, only a few regional risk-sharing channels such as trade channels existed. Although Japan still remained an important source country for external financing before the crisis, Western investors outside the region also played an important role. Since the crisis, however, most East Asian countries have become net providers of international capital due to their current account surpluses. While receiving inflows of foreign direct and portfolio investment on a net basis, these countries have repaid large sums of bank loans for the past several years. Looking to the future, whether countries in the Asia Pacific region have similar patterns of capital flows will be an empirical question. However, until a regional risk-sharing mechanism for integrating the financial markets in the region is fully developed, most East Asian countries are likely to become more integrated into the global financial markets.

ABBREVIATIONS

FDI: Foreign Direct Investment

GDP: Gross Domestic Product

VAR: Vector Auto-Regression

doi:10.1111/j.I465-7287.2011.00285.x

[c] 2011 Western Economic Association International

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(1.) Although other fundamental domestic problems contribute to financial crises, capital account liberalization and the resulting lending booms sometimes end in twin currency and banking crises.

(2.) Domestic residents can reduce fluctuations in income stream and consumption by borrowing from abroad during recessions or lending to foreign countries during booms. International portfolio diversification enables consumers and firms to achieve risk-sharing gains by diversifying risks associated with country-specific shocks.

(3.) We do not focus on the effects of capital flows on business cycle volatility in this article. See, for example, Buch, Dopke, and Pierdzioch (2005) and Kose, Prasad, and Terrones (2003a, 2003b) on this issue.

(4.) See Kim, Kose, and Plummer (2001) for a detailed explanation on financial contagion.

(5.) See, for example, Heathcote and Perri (2004), Imbs (2004), and Kalemli-Ozcan, Sorensen, and Yosha (2001).

(6.) Another important issue in the literature is trade integration and its impact on business cycles. Trade integration can generate synchronized business cycles if countries mostly engage in intra-industry trade (production fragmentation), while trade integration can decrease the degree of co-movements if trade promotes inter-industry specialization and countries are subject to industry-specific shocks. See, for example. Frankel and Rose (1998) and Shin and Wang (2004).

(7.) Note that we focus on the effects of financial market integration on output co-movements, not cross-country consumption correlation which is expected to increase as consumers in different countries receive a similar income stream through portfolio diversification and consumption smoothing.

(8.) See Kim, Kose, and Plummer (2003) for a detailed analysis of stylized facts of business cycles in Asia and the G-7 countries.

(9.) Quarterly data are not available for some countries. Later for the VAR estimation, we use quarterly data.

(10.) We should note that the volatility of consumption changes depending on the specific consumption data. It is known that the volatility of durable goods consumption is two to four times higher than that of nondurables consumption (see Backus, Kehoe, and Kydland 1995).

(11.) Since its recent economic reform, China has embarked upon a process of financial and real integration with Hong Kong and Taiwan. Even before Hong Kong's return to China's sovereignty in 1997, it had achieved a high degree of integration with the mainland. With respect to trade, for instance, Hong Kong intermediates a lion's share of China's external trade via re-exports and offshore trade. Regarding financial activity, a substantial amount of the international capital (in the forms of foreign direct investment, equity and bond financing, and syndicated loans) financing China's economic expansion is raised via Hong Kong. Economic links between China and Taiwan have also proliferated since the 1990s. According to official statistics (although the official statistics under-represent the overall economic interest of Taiwan in China), China is the largest recipient of Taiwan's overseas investment and Taiwan is China's third-largest source of foreign direct investment (Cheung, Chinn, and Fujii 2003).

(12.) This case is indicated by bold and italic numbers in the table. We do not report the case excluding the crisis period, but the results are similar.

(13.) Another channel that can increase output correlation is through increased intra-industry trade (production fragmentation).

(14.) A similar empirical methodology was used in Kim, Kim, and Wang (2003) to analyze the boom-bust cycles in Korea. Torne11 and Westermann (2002) also examined the boom-bust cycles by using a sample of 39 countries.

(15.) For simplicity, we present the model without the vector of constants. Alternatively, we can regard each variable as a deviation from its steady state.

(16.) We use an exponential trend on the GDP level (or a linear trend on the log level of GDP). When constructing the ratio, we use all variables in terms of U.S. dollars.

(17.) Note that the effects of CAP shocks on CUR and RGDP are invariant to the ordering between CUR and RGDP. On the other hand, capital flows might affect CUR and RGDP within a quarter, and the CUR and RGDP shocks may reflect some part of (exogenous) CAP shocks. However, even in such cases, CAP shocks still represent the shocks to CAP that are not endogenous to CUR and RGDP changes as they do not result from endogenous responses to CUR and RGDP, although CUR and RGDP shocks may include (exogenous) shocks to CAP in addition to shocks to CUR and RGDP.

(18.) The estimation period for Thailand is from 1993 as the data series are available only from 1993. The estimation period for the regression including real exchange rate of Thailand and Indonesia is from 1994 as the data series are available only from 1994.

(19.) As in the basic model, we order X before CAP. By doing so, CAP shocks represent the shocks to CAP that are not endogenous to CUR and RGDP changes since they do not result from endogenous responses to CUR and X, although CUR and X shocks may include (exogenous) shocks to CAP, in addition to shocks to CUR and RGDP.

(20.) For Taiwan, consumer price index is used, as a GDP deflator is not available.

(21.) We plot cumulative capital flow shocks because capital account shocks themselves are very volatile.

* We thank an editor and two anonymous referees for their helpful comments. S.K. acknowledges the support by Research Settlement Fund for the new faculty of Seoul National University.

SOYOUNG KIM and SUNGHYUN H. KIM *

S. Kim: Department of Economics, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul, Korea. Phone (02) 880-2689, Fax (02) 886-4231, E-mail soyoungkim@snu.ac.kr

S. H. Kim: Department of Economics, Sungkyunkwan University, Seoul, Korea, and Department of Economics, Suffolk University, Boston MA, USA. Phone 617-9944232, Fax 617-994-4216, E-mail sunghyun.kim@suffolk. edu
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Author:Kim, Soyoung; Kim, Sunghyun H.
Publication:Contemporary Economic Policy
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Date:Jan 1, 2013
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