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ARTICLE IN PRESS Journal of International Money and Finance ■■ (2016) ■■–■■

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Journal of International Money and Finance j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / j i m f

Capital and credit market integration and real economic contagion during the global ﬁnancial crisis ☆ Ju Hyun Pyun a, Jiyoun An b,* a b

Business School, Korea University, 145 Anam-Ro, Seongbuk-Gu, Seoul 02841, Republic of Korea Kyung Hee University, 1732 Deogyoung-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Republic of Korea

A R T I C L E

I N F O

Article history: Available online JEL Classiﬁcation: E32 F15 F36

Keywords: Capital market integration Credit market integration Global ﬁnancial crisis Real business cycle co-movements

A B S T R A C T

This study investigates the role of ﬁnancial integration in the spread of global ﬁnancial crisis. In particular, this study shows how the effect of the crisis on real business cycle co-movement varied for capital and credit market integration, using a sample of 58 countries in 2001–2013. During the global ﬁnancial crisis, the United States – the epicenter of the crisis – experienced a severe downturn in the real economy, and other countries followed suit. We ﬁnd that during the global ﬁnancial crisis, the business cycle co-movements between the United States and the rest of the world were stronger when the level of capital market integration between them was higher. However, the co-movements were weaker when the level of credit market integration was higher. These ﬁndings are robust even when including investment channels, local fundamental factors, endogenous policy responses across countries, and alternative measures for ﬁnancial integration and business cycle co-movements. © 2016 Elsevier Ltd. All rights reserved.

☆ We are grateful to Jay M. Chung, Andrew Clare, Stefan Eichler, Stanimira Milcheva, Daehwan Kim, Bokyoung Park, Dyani Poedjioetomo, Alberto Pozzolo, Dong-Eun Rhee, and two anonymous referees for their valuable comments and suggestions. We also thank the participants of the 2013 KIC-IIS-KIEP International Conference, 2014 IFABS conference, and 2014 Asian Finance Association Conference for useful discussion. This research has been supported by new faculty research grants from Korea University Business School and the earlier version of the paper was ﬁnancially supported by the Korea Institute for International Economic Policy. All errors and omissions are our own. * Corresponding author: Tel.: +82 31 201 3884. E-mail address: [email protected] (J. An).

http://dx.doi.org/10.1016/j.jimonﬁn.2016.04.004 0261-5606/© 2016 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Ju Hyun Pyun, Jiyoun An, Capital and credit market integration and real economic contagion during the global financial crisis ☆, Journal of International Money and Finance (2016), doi: 10.1016/j.jimonfin.2016.04.004

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1. Introduction The cross-border holdings of international ﬁnancial markets have increased remarkably since the 2000s, as shown in Lane and Milesi-Ferretti (2007) and Lane (2013). Other previous literature has conﬁrmed the ongoing process of ﬁnancial globalization, particularly among advanced economies. However, the global ﬁnancial crisis (GFC) – the most serious ﬁnancial crisis since the Great Depression in 1929 – has raised doubts about the beneﬁts of ﬁnancial globalization.1 Negative shocks originating from the US ﬁnancial market quickly spread globally through various channels during the GFC. Many countries experienced severe recessions following the US economic downturn. Several studies have assessed the existence of contagion during ﬁnancial crises.2 Some have emphasized the role of ﬁnancial linkages in contagion, arguing that ﬁnancial linkages acted as the main transmission channels of cross-country shocks during ﬁnancial crises in the 1990s (e.g., Baig and Goldfajn, 1999; Caramazza et al., 2004; Kaminsky and Reinhart, 2000; Van Rijckeghem and Weder, 2001). However, Lane (2013) has provided market-speciﬁc views on ﬁnancial integration during the GFC. He suggests that emerging economies beneﬁted from their “structure of international investment” as holding both liquid foreign currency and safe foreign debt assets during the GFC provided a substantial buffer. However, the portfolio proﬁles of many advanced economies – such as “long equity, short debt” – proved risky in the face of declining ﬁnancial markets because of negative valuation effects. According to Lane (2013), we conjecture that various ﬁnancial instruments – such as equities, bonds, derivatives, and loans – and their international linkages play different roles in spreading shocks across countries.3 In addition, although extensive studies on ﬁnance have focused on contagion across ﬁnancial markets, relatively few have examined the transmission of negative ﬁnancial shock to the real economy. Frankel and Saravelos (2012) and Rose and Spiegel (2010, 2012) approach the issue of contagion in the real economy during the GFC from a macroeconomic perspective. Kalemli-Ozcan et al. (2013a) investigate the effect of banking integration on real business cycle synchronization during the GFC. However, previous literature has not fully examined the effect of capital (equity) and credit (debt) market integration on the real economy. Our study investigates how the effect of the GFC on real business cycle co-movement varied for capital and credit market integration for 57 countries with the United States, the origin of the GFC, during 2001–2013. When the crisis is contagious, both the origin and other countries experience economic downturn and their business cycles become more synchronized. Thus, we interpret the effect of the crisis on business cycle co-movement between the United States and the rest of the world as the degree of real economic contagion after controlling for other important determinants of business cycle co-movement. Then, we examine business cycle co-movement during the GFC with ﬁnancial integration in two speciﬁc markets: capital (equity) and credit (debt). With a simultaneous equation model that controls for endogeneity, we ﬁnd that during the GFC, the business cycle co-movement between the United States and each of the other countries increased more when the United States and its partner had higher capital market integration, and decreased more when the pair had higher credit market integration. The results are robust even when including investment channels, local fundamental factors, endogenous policy responses across countries, and alternative measures for ﬁnancial integration and business cycle co-movements. Our ﬁndings provide signiﬁcant evidence that correlated equity positions caused by extremely “largescale” negative ﬁnancial shock during the GFC did not buffer the transmission of the shock and rather increased business cycle co-movement. By contrast, undiminished demand for safe US debt assets during the GFC did not set off a chain reaction of debt collateral and did not amplify the negative shock.4

1 Baele et al. (2004) summarize the beneﬁts of ﬁnancial integration shown in previous literature: ﬁnancial integration provides greater risk sharing by diversifying portfolios, improves capital allocation and makes the ﬁnancial system more eﬃcient by reducing transaction barriers, and creates economic growth via ﬁnancial development. 2 Please see Claessens et al. (2001) for an excellent review of the literature on contagion. 3 For instance, Ahrend and Goujard (2014) ﬁnd that different types of ﬁnancial linkages have different inﬂuences on asset price co-movement during the GFC. However, the difference is that our main interest is the international business cycle whereas they focused on asset price co-movement. 4 This result is in line with Prasad’s (2014) argument about the “rush to safety,” which shows investors’ special preference for US dollar-denominated assets.

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An important concern about the contagion study is the wake-up call hypothesis based on previous research, which suggests that a country’s fundamentals far outweigh ﬁnancial linkages in terms of the ability to amplify or buffer against negative shock (e.g., Goldstein, 1998).5 Our work enriches the wake-up call hypothesis literature by focusing on the effects of ﬁnancial linkages in the “real” market contagion. In so doing, we show that our results on ﬁnancial linkages and business cycles are robust when controlling for other important local fundamental factors that affect the transmission-of-the-crisis shock. Our study builds on previous studies examining the effect of ﬁnancial integration on real business cycle co-movement, which were initiated by Kose et al. (2003). Subsequent research, such as Imbs (2004) and Kalemli-Ozcan et al. (2003), has debated the true effects of ﬁnancial integration on the international real business cycle.6 Davis (2014) reconciles the views of Imbs (2004, 2006) and Kalemli-Ozcan et al. (2003) and ﬁnds discrepancies between the effects of capital and credit market integration on the synchronization of the international business cycle. Our study is related to Davis (2014), which distinguishes wealth and balance sheet effects on international business cycle transmission through capital and credit markets. While wealth effects tend to dominate the capital market in international business cycle transmission, balance sheet effects tend to dominate the credit market during normal times. However, distinct from Davis (2014), our results imply that during the crisis, the dominance of the effects is possibly different as the coeﬃcients are reversed. Other works closely related to ours are Kalemli-Ozcan et al. (2013a, 2013b), which investigate the impact of banking integration on the transmission of international business cycles. In particular, Kalemli-Ozcan et al. (2013a) ﬁnd that higher linkages in the banking sector were associated with more divergent output cycles before 2007; however, during the GFC, more integrated banking between countries led to more synchronized output co-movement.7 Our contribution considers aggregate portfolio investment (equity and debt), including stocks, ADRs, bonds, and treasury bills, whereas they focus solely on cross-border bank loans. Our work is also linked to subsequent inﬂuential studies by Rose and Spiegel (2010, 2012). Rose and Spiegel (2010) ask a very similar question to the one presented here: did ﬁnancial integration play a signiﬁcant role in the transmission of the negative shock during the GFC? They do not ﬁnd strong evidence that international ﬁnancial linkages played a signiﬁcant role in the spread of the GFC shock. In this regard, Dées and Zorell (2012), using a simultaneous equation model similar to this study, ﬁnd the absence of direct link between ﬁnancial linkages and business cycle co-movement for crosscountry data before the GFC. However, our work dissects “different” types of ﬁnancial integration and provides signiﬁcant and different roles of capital and credit market linkages in transmitting negative GFC shocks from the United States. Moreover, our work considers the dynamic extent to which ﬁnancial integration conveyed the shocks of the GFC to the real business cycle by examining both crosssectional and time-series variations of a 58-country sample over 2001–2013. The remainder of this paper is organized as follows. In Section 2, we provide a theoretical discussion on the channels by which ﬁnancial integration affects real business cycle co-movement during the GFC. Section 3 describes our empirical model and the data used. Sections 4 and 5 present the empirical results and robustness tests, respectively. Concluding remarks follow in Section 6.

5 Bekaert et al. (2014) conﬁrm the wake-up call hypothesis by showing that the quality of countries’ economic fundamentals and policies play substantial roles in the transmission of negative shocks across ﬁnancial markets, whereas ﬁnancial linkages have economically small roles. 6 Kose et al. (2003) show that increased trade and ﬁnancial linkages have no signiﬁcant effect on output and consumption correlation. However, Kalemli-Ozcan et al. (2003) show that enhanced ﬁnancial integration creates a greater chance of industrial specialization and results in more exposure to industry- or country-speciﬁc shocks and business cycle de-synchronization. Conversely, Imbs (2004) suggests that increased ﬁnancial integration generates correlated capital ﬂows, which contributes to business cycle synchronization, although increased ﬁnancial integration indirectly causes business cycle de-synchronization through industrial specialization. Imbs (2004) ﬁnds that the positive effect of ﬁnancial integration on business cycle synchronization is stronger. Subsequent research by Imbs (2006) sheds light on the link between ﬁnancial integration, output, and consumption correlation. Note that Dées and Zorell (2012) emphasize the direct effect of trade linkages and production structure on business cycle co-movement while they ﬁnd ﬁnancial linkages increase business cycle co-movement only indirectly through industry specialization. 7 Kalemli-Ozcan et al. (2013a) also provide a theoretical model that included two kinds of shocks – productivity and ﬁnancial shock – and documented the propagation mechanism of each shock to the real business cycle according to banking integration.

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2. Theoretical discussion on business cycle co-movement and ﬁnancial integration during the GFC We ﬁrst introduce possible theoretical channels through which negative shocks in the integrated ﬁnancial markets were transmitted to international real business cycle in normal times, as suggested by previous studies (e.g., Baxter and Crucini, 1995; Kollmann et al., 2011). Next, we discuss the difference in the effects of ﬁnancial integration on business cycle co-movement between normal times and during the crisis, in particular, the GFC. Prior studies in the international real business cycle model have suggested that international business cycle transmission via ﬁnancial integration occurs through wealth and balance sheet effects. First, ﬁnancial integration leads to a diverged business cycle when the wealth effect dominates. Suppose there are two countries, home and foreign, in which the two ﬁnancial markets are closely integrated. If there is a negative idiosyncratic shock at home, home consumption and investment decline. In addition, such a shock has a negative effect on foreign investors’ wealth (with a form of home equities). Then, the foreign country decreases consumption and increases savings, which leads to an increase in investment. Thus, the asymmetric patterns in the two countries’ investments through the wealth effect result in business cycle divergence. In particular, Davis (2014) shows that the wealth effect dominates when discussing transmission through capital (equity) markets. In this line, previous studies, such as Kalemli-Ozcan et al. (2003), suggest that in an integrated ﬁnancial market, home and foreign reallocate their capital, which promotes industrial specialization and business cycle divergence. However, we argue that during the GFC, the abovementioned effects of equity market integration on business cycle co-movement can be reversed. If the negative shock at home is no longer a local shock, but a common shock, and spreads through all industries across countries,8 then capital ﬂight (correlated capital movement) rather than capital reallocation in the integrated equity market can occur, which would not cushion the transmission of the negative shock effectively. Thus, during the “global” ﬁnancial crisis, capital investment that decreased simultaneously to and from the United States would lead to business cycle synchronization. Indeed, countries that have high equity market integration with the United States, such as Germany, Japan, the Netherlands, and the United Kingdom, experienced sudden decreases in equity integration during the GFC (see Fig. 2; a more detailed explanation is in Section 3).9 The balance sheet effect introduces a different channel for international business cycle transmission through ﬁnancial integration. Again, suppose the two ﬁnancial markets are closely integrated. If the negative shock occurs at home, this would potentially cause some defaults in home debt assets. Furthermore, the balance sheets of foreign ﬁnancial intermediaries that hold some of the risky home debts become worse off, and the foreign ﬁnancial intermediaries respond by paying off the home debts in order to decrease their debt to asset ratios. Thus, both countries face a lack of credit supply at the same time, and their real business cycles are more synchronized. Davis (2014) shows that the balance sheet effect tends to dominate when discussing transmission through credit (debt) markets. However, we suggest that during the GFC, the balance sheet effect would be mitigated in the integrated debt market with the United States because the negative shock in the United States did not cause foreign investors to pay off US debt assets and did not result in a chain reaction. In particular, the status of US government debt assets as a safe-haven investment attracted investors during the GFC. Certainly, prior studies provide convincing evidence on undiminished demand for US debt, which also hinted at the “ﬂight to safety.”10 Forbes (2010) notes that foreign demand for US Treasury debt,

8 Previous literature has suggested that the different kinds of negative shocks in the integrated ﬁnancial market have different consequences for their spillover effects (Pericoli and Sbracia, 2003). A local or idiosyncratic shock at home can be buffered in the integrated market through risk diversiﬁcation, and thus, it does not lead to signiﬁcant contagion. On the contrary, a common shock in the integrated market cannot be cushioned; it would have negative effects on both goods and ﬁnancial markets across countries. 9 Graphs showing each individual country’s equity and debt market integration over time are available from the authors upon request. 10 Previous studies regarding “ﬂight to safety” have suggested that the increased market uncertainty during crises induced investors to ﬂee stocks in favor of bonds, which are safer assets (e.g, Baele et al., 2013).

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in particular, short-term T-bills, increased sharply during the peak of the crisis.11 Prasad (2014) argues that “the dollar remains the preferred refuge from troubled ﬁnancial markets, even if its home country is the source of those troubles” (Prasad, 2014, p. 19). We also ﬁnd that our debt integration measure shows a relatively constant pattern compared to the equity integration measure during this period (see Fig. 2). Consequently, high debt market integration with the United States during the GFC did not amplify the transmission of the negative shock, unlike the expectations from the previous theory. Therefore, the roles of equity integration and debt integration in delivering the negative shock from the United States to the rest of the world during the GFC can be differentiated from Davis (2014). Countries with high equity market integration with the United States were more vulnerable to the negative shock during the GFC because of capital ﬂight, whereas countries with high debt market integration with the United States were more insulated from the shock because of increased demand for US debt assets, the so-called “rush to safety.”12 3. Empirical analysis 3.1. Data and measurements To gauge real economic spillover during the GFC, we compare the real economic growth of the United States – the epicenter of the crisis – with that of other countries. Although it is obvious that the US real growth rate decreased during the crisis and recovered later, the extent to which the rest of the world followed a similar pattern is ambiguous. Our study considers the dynamics of real economic contagion from the United States to the rest of the world by examining business cycle co-movement between the United States and 57 other countries. The measurement for year-by-year business cycle synchronization (SYNCHi,us,t) is the negative divergence deﬁned as the negative absolute value of real GDP per capita growth rate differences between countries i and j in year t:

SYNCHi ,us,t = − g i ,t − g us,t

(1)

where gi,t and gus,t indicate the log difference of GDP per capita, based on PPP adjusted constant 2005 international dollars of country i and the United States, which are collected from World Development Indicators (WDI) of the World Bank. This simple index is taken directly from Kalemli-Ozcan et al. (2013a), based on Giannone et al. (2010). It does not reﬂect the volatility of the output growth of each country in a pair but captures only the co-variation of output growth. Business cycle synchronization with the United States became higher when a country was inﬂuenced more negatively by the US economy during the GFC. Thus, SYNCH may capture a degree of the real economic contagion during the GFC if we successfully control for other determinants of real business cycle co-movements. A merit of this measure is that we can utilize time-series information for changes in growth rates, as well as intensity of contagion. Fig. 1 reports the real growth rate movements of the United States and the selected partner countries. We conﬁrm that the measure for business cycle co-movement reﬂects well the changes in the growth rates of both the United States and the other countries. For instance, business cycle co-movement between the United States and Canada increased during the GFC, as seen by the decrease in Canada’s growth rate beginning with the decline of US growth in 2008. Moreover, we conduct the same analysis with alternative business cycle comovement measures, SYNCH1, and SYNCH2, which are discussed in Section 5. The deﬁnition of ﬁnancial integration varies among academics and policymakers. Taking a narrow view, fully integrated markets imply that all potential market participants face a single set of rules,

11 However, the focus of Forbes (2010) on foreign holdings of US assets is ﬁnancial underdevelopment for emerging market countries. 12 This is consistent with Naes et al.’s (2011) ﬁnding that US stock market liquidity is countercyclical; they argue that during recessions, including the GFC, there is a “ﬂight to quality” in equities, as investors sell stocks, especially riskier stocks, in favor of safer securities.

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Panel A. Major countries

Panel B. Asian countries

Note: GDPG and US GDPG indicate real GDP growth rates of each country and the United States, respectively. We select the major (G6) and several Asian countries for space constraints; trends for other countries are available upon request.

Fig. 1. Trend of real GDP growth rates and business cycle synchronization.

Please cite this article in press as: Ju Hyun Pyun, Jiyoun An, Capital and credit market integration and real economic contagion during the global financial crisis ☆, Journal of International Money and Finance (2016), doi: 10.1016/j.jimonfin.2016.04.004

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have equal access, and are treated equally (Baele et al., 2004). Broadly, however, ﬁnancial integration is a state of close relationships between countries or regions created by lowering ﬁnancial barriers, cutting transaction costs, or reducing regulations on international capital ﬂows. To measure the degree of bilateral ﬁnancial integration (bilateral ﬁnancial linkage), we rely on a quantity-based measure, as in Ahrend and Goujard (2014), Kalemli-Ozcan et al. (2013a, 2013b), and Rose and Spiegel (2010, 2012), following the broad view of ﬁnancial integration. Formally, the bilateral capital and credit market integration between countries i and the United States (FIEQi,us,t;FIDBi,us,t) is deﬁned as follows:

FIEQ i ,us,t =

AssEQ i ,us,t + LibEQ i ,us,t AssDBi ,us,t + LibDBi ,us,t ; FIDBi ,us,t = GDPi ,t + GDPus,t GDPi ,t + GDPus,t

(2)

where AssEQi,us,t (AssDBi,us,t) are assets of equity (debt) securities; a country i (resident)’s cross-border holdings of equity (debt) securities issued by the United States (non-resident) at time t, and LibEQi,us,t (LibDBi,us,t) are liabilities of equity (debt) securities; the US cross-border holdings of securities issued by country i at time t.13 These cross-border holding variables are based on market values of positions held and expressed as current US dollars. GDPi,t and GDPus,t are the current gross domestic product of countries i and the United States, respectively. The sum of GDPi,t and GDPus,t standardizes the sum of assets and liabilities and also relieves valuation effects in the numerator. In addition, we introduce an alternative measure of ﬁnancial integration using the sum of market capitalization instead of GDP to check the robustness of our results. The alternative capital (credit) market integration measure, FIEQ1i,us,t (FIDB1i,us,t), employs capital (credit) market capitalization of country i at year t from the Financial Development and Structure Dataset (see Beck et al., 2010). Higher values of FIEQi,US,t (FIDBi,US,t) indicate higher capital (credit) market integration between country i and the United States. Bilateral equity and debt security holdings data are collected from the Consolidated Portfolio Investment Survey (CPIS) of the International Monetary Fund (IMF). The CPIS reports bilateral portfolio equity and debt holdings for 74 reporting countries and 231 partner countries. The CPIS data include private portfolio holdings and the holdings of quasi-oﬃcial entities, such as sovereign funds, but exclude oﬃcial holdings, such as the foreign reserve holdings of central banks. Note that there is a large body of literature that examines the role of reserve holdings in the international transmission of the crisis (e.g., Aizenman and Hutchison, 2012). Panel A of Fig. 2 shows the pattern of average level of equity and debt integration with the United States for both advanced and emerging economies before, during, and after the GFC. Panel B of Fig. 2 displays aggregate portfolio investment holdings: US equity/debt investment held by foreign residents (equity/debt assets) and foreign equity/debt investment held by US residents (equity/debt liabilities). As Fig. 2 highlights, starkly different patterns emerge between equity integration and debt integration during the GFC. In general, equity market integration with the United States plummeted during the GFC for most countries. However, debt market integration with the United States was relatively constant, or even increased, during the GFC. TIi,us,t is the measure for bilateral trade integration, calculated as (EXi,us,t + IMi,us,t)/(GDPi,t + GDPus,t). EXi,us,t represents exports from country i to the United States at year t, and IMi,US,t represents imports from the United States to country i at year t. Bilateral export–import data are obtained from Direction of Trade Statistics (DOT). SIMi,us,t is the measure for similarity in the production structure or industry specialization, constructed as SIMi ,us,t = ∑ nN sn,i ,t − sn,us,t . sn,i ,t is sector n’s share in total value added in country i. sn,us,t is sector n’s share in total value added in the United States. The sectoral value-added shares are computed using agriculture, manufacturing, and service industry value-added data from WDI.14

13 It is acknowledged that assets data collected from creditor surveys are more reliable detailed data than liabilities data because the holder (creditor) usually knows what securities it holds while the issuer of a security (debtor) may not know the residency of the holder. In fact, the IMF derives its liabilities data, called “derived liabilities,” using the assets data. 14 Previous studies such as Imbs (2004, 2006) and Dées and Zorell (2012) compute the production similarity using the United Nations Industrial Development Organization (UNIDO) or UN National Accounts country data (UNNA). However, the coverage of these data are limited to manufacturing industry (UNIDO) or to smaller number of countries (UNIDO and UNNA), and when using production similarity from UNNA, the number of our sample countries shrinks from 58 to 40 countries. Nevertheless, our main results using this production similarity measure from UNNA do not change. Please see column (5) of Table 6.

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Panel A. Levels of financial integration with the United States Advanced economies 1.8 1.5 1.2 0.9 0.6 0.3 0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 FIEQ

FIDB

Emerging and developing economies 0.5

0.4

0.3

0.2

0.1

0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 FIEQ

FIDB

Note: FIEQ and FIDB are the equal-weighted average of the equity and debt market integration measures across countries. Advanced economies include 28 countries while emerging and developing economies include 29 countries from our sample. Fig. 2. Trend of bilateral ﬁnancial integration with the United States.

Exogenous variables are used to identify the system of equations. Geographical variables such as log distance from the US, and border with the US, and common language (English language) are obtained from Centre d’Études Prospectives et d’Informations Internationales (CEPII). The common legal origin variable with the US (common law) is coded as one if both country i and the US share their legal system origin: English (common), as discussed in La Porta et al. (2008). The US dollar peg variable is based on the classiﬁcation used in Shambaugh (2004). The time-series variables are the US dollar peg, Please cite this article in press as: Ju Hyun Pyun, Jiyoun An, Capital and credit market integration and real economic contagion during the global financial crisis ☆, Journal of International Money and Finance (2016), doi: 10.1016/j.jimonfin.2016.04.004

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Panel B. Aggregate portfolio investment holdings with the United States (current USD millions)

Partner: Advanced economies

12,000,000 10,000,000 8,000,000 6,000,000 4,000,000 2,000,000 0 2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

US equity investment held by foreign residents

Foreign equity investment held by US residents

US debt investment held by foreign residents

Foreign debt investment held by US residents

(current USD millions)

2013

Partner: Emerging economies

1,400,000 1,200,000 1,000,000 800,000 600,000 400,000 200,000 0 2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

US equity investment held by foreign residents

Foreign equity investment held by US residents

US debt investment held by foreign residents

Foreign debt investment held by US residents

2013

Note: Portfolio investment (equity and debt) holdings are aggregated across countries. Advanced economies include 28 countries and emerging and developing economies include 29 countries from our sample. Fig. 2. (continued)

capital market restriction indices, regional trade agreement, and bilateral GDP per capita. The US dollar peg is coded as one when a currency either stays within 2% bands against the US dollar or has zero volatility in all months except for a one-off devaluation. Bilateral capital control restrictions (equity and debt) are obtained from Fernández et al. (2015) which introduce capital control restrictions on both inﬂows and outﬂows of ten categories of assets for 100 countries. The bilateral regional trade agreement variable is coded as one when both countries engage in regional trade agreement, otherwise zero. The data are taken from de Sousa (2012). Please cite this article in press as: Ju Hyun Pyun, Jiyoun An, Capital and credit market integration and real economic contagion during the global financial crisis ☆, Journal of International Money and Finance (2016), doi: 10.1016/j.jimonfin.2016.04.004

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Table 1 Descriptive statistics.

SYNCH FIEQ FIDB TI SIM

N

Mean

S.D.

Min

Max

p1

p5

p25

p50

p75

p95

p99

646 646 646 646 646

−0.024 0.557 0.425 0.255 0.270

0.021 0.996 0.895 0.473 0.188

−0.123 0.000 0.000 0.001 0.008

−0.00002 7.310 5.514 3.629 0.970

−0.091 0.000 0.001 0.001 0.014

−0.067 0.001 0.002 0.003 0.044

−0.036 0.020 0.020 0.025 0.127

−0.018 0.141 0.074 0.094 0.227

−0.008 0.596 0.342 0.279 0.358

−0.001 2.519 2.664 1.114 0.675

−0.0003 5.166 4.600 2.646 0.741

Note: SYNCH is a proxy for real business cycle co-movement, deﬁned as absolute value of real GDP growth rate differences between country i and the United States in year t. FIEQ and FIDB are the capital and credit market integration measures, respectively, calculated as the sum of assets and liabilities divided by the sum of the current GDP of the United States and partner country. TI is the trade integration measure, the sum of exports and imports divided by the sum of the current GDP of the United States and partner country. These are quantity-based measures. SIM is a measure for the similarity in the production structure or industry specialization. Mean, S.D., Min, and Max are the summary statistics of country-year observations. FIEQ, FIDB, and TI are multiplied by 100 and represented as a percentage term for simple presentation.

Before we present our benchmark estimates, we report descriptive statistics in Table 1 for the main endogenous variables employed in the empirical analysis. SYNCH ranges from −12% (most divergent) to −0.002% (most synchronized) and its reported mean is −2.4%. The means of equity integration and debt integration over the sum of GDP are 0.56 and 0.43, respectively, but vary across countries (see Appendix Table A1). 3.2. Empirical model and identiﬁcation issues We investigate the impact of negative shock from the United States on the international business cycle and the extent to which it varied according to ﬁnancial integration. We use country-pair panel data of annual observations from 2001 to 2013. Following previous studies on business cycle comovement and international linkages such as Imbs (2004, 2006), Dées and Zorell (2012) and Davis (2014), we introduce simultaneous equations model as a main speciﬁcation. This model not only considers direct and indirect channels of ﬁnancial integration on business cycle co-movement, but also disentangles the interactions between business cycle co-movement, ﬁnancial integration, trade integration, and other variables. Our simultaneous equation model consists of ﬁve equations as follows:

SYNCHi ,us,t = α 0 + β1C us,t + β 2FIEQ i ,us,t + β 3C us,t FIEQ i ,us,t + β 4 FIDBi ,us,t + β5C us,t FIDBi ,us,t + β6TIi ,us,t + β7 SIMi ,us,t + ε i ,us,t

(3)

FIEQ i ,us,t = λ0 + λ1C us,t + λ 2FIDBi ,us,t + λ 3C us,t FIDBi ,us,t + λ 4TIi ,us,t + λ5 SIMi ,us,t + X iE,us,t λ6 + ε i ,us,t FIDBi ,us,t = φ0 + φ1C us,t + φ2FIEQ i ,us,t + φ3C us,t FIEQ i ,us,t + φ4TIi ,us,t + φ5 SIMi ,us,t + X iD,us,t φ6 + wi ,us,t TIi ,us,t = θ 0 + θ1C us,t + θ 2FIEQ i ,us,t + θ 3FIDBi ,us,t + θ 4 SIMi ,us,t + X iT,us,tθ5 + ui ,us,t SIMi ,us,t = γ 0 + γ 1C us,t + γ 2FIEQ i ,us,t + γ 3FIDBi ,us,t + γ 4TIi ,us,t + X iS,us,t γ 5 + ei ,us,t where SYNCHi,us,t is a measurement for business cycle co-movement between country i and the United States at year t. Cus,t is an indicator for the GFC – a dummy variable coded as one for the GFC period of 2008–2009.15 In our robustness tests, we adopt an additional continuous variable as a proxy for the GFC, using the Chicago Board Options Exchange Market Volatility Index (VIX). FIEQi,us,t (FIDBi,us,t) is the

15 Broadly, the GFC occurred between 2007 and 2009. In November 2007, Lehman Brothers Inc. collapsed, and then, the crisis quickly spread across the world. Within 2009, the US GDP growth rates began to recover, growing close to pre-crisis levels. However, our analysis excludes 2007 and considers only the period from 2008 to 2009 as the GFC period because annual observations may not reﬂect time-to-time changes quickly.

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ﬁnancial integration variable for the capital (credit) market between country i and the United States at year t. Financial integration variables are adjusted by subtracting mean values for ease of interpretation on the interaction terms.16 TIi,us,t is the measure for bilateral trade integration. SIMi,us,t is the measure for similarities in the production structure or industry specialization. Our ﬁve endogenous variables are SYNCHi,us,t, FIEQi,us,t, FIDBi,us,t, TIi,us,t, and SIMi,us,t. We include the interaction terms of the crisis and each ﬁnancial integration measure in the ﬁrst equation to examine the role of ﬁnancial integration in determining business cycle co-movement especially during the crisis. The interaction of each ﬁnancial integration and the crisis variable gives two more nonlinear endogenous variables, which makes the number of endogenous variables exceed the number of equations. Wooldridge (2010) states that in the simultaneous equation model nonlinear in endogenous variables, it is not a requirement to include additional equations for the nonlinear endogenous interaction terms. A simple approach of the identiﬁcation is to add an appropriate set of exogenous variables such as the interaction terms between the crisis variable and other instrumented exogenous variables once the rank condition holds.17 XEi,us,t, XDi,us,t, XTi,us,t, and XSi,us,t are vectors of exogenous variables for each equation.18 As shown in the previous studies such as Davis (2014), Dées and Zorell (2012) and Imbs (2004, 2006), the included exogenous variables help to describe bilateral ﬁnancial and trade integration and production similarity between country i and the United States. First, a common set of exogenous variables in the equations (FIEQi,us,t, FIDBi,us,t, and TIi,us,t) are physical distance (log distance from the United States) and border with the United States which inﬂuence transactions of real goods and ﬁnancial assets, and English language which captures linguistic and cultural proximity between countries. We also include the timevarying exchange rate peg (US dollar peg) to control for the impact of exchange rate volatility on ﬁnancial and trade integration with the United States and, furthermore, the transmission of negative shock through the peg during the GFC. To identify the whole system, we include different sets of exogenous variables in each equation. For two ﬁnancial integration equations, XEi,us,t and XDi,us,t contain common legal origin (common law) because it is likely to lead to similar institutions, regulations, and customs for ﬁnancial transactions between countries. We add additional time-varying exogenous variables into XEi,us,t and XDi,us,t separately. Fernández et al. (2015) construct capital control measures for various categories of assets. Given their contribution, XEi,us,t contains both the sum and the absolute difference of aggregate capital control restriction indices in equities transaction for countries i and the United States, whereas XDi,us,t includes both the sum and absolute difference of capital control restriction indices in debts (with all maturities) transaction for both countries. For the identiﬁcation of trade equation, XTi,us,t contains additional time-varying institutional variable, regional trade agreement dummy, which is widely used in gravity literature in trade. Lastly, in the production similarity equation, we add the (log) product of and the absolute value of difference between GDPs per capita of countries into XSi,us,t. Additional exogenous variables for all equations are year dummies. Hence, if the system is well identiﬁed by the simultaneous equations model, then we successfully determine the effects of the crisis and its interaction with equity and debt market integration on business cycle correlation through the coeﬃcients βi, for i = 1,…,7. Note that Fries and Kappler (2015) discuss the identiﬁcation issue of the panel structure simultaneous equation models for the business cycle co-movement thoroughly.19

16 The coeﬃcients in the interaction terms of ﬁnancial integration and the crisis need to be interpreted by comparing the levels of ﬁnancial integration and their mean values. 17 The rank condition can safely be assumed to hold in a model of this size. Please see the chapter 9 of Wooldridge (2010) for detailed discussion of the identiﬁcation of simultaneous equation models such as order and rank conditions. Wooldridge (2010) also mentions that technically, the rank condition in nonlinear interaction term models is not necessary for identiﬁcation if the rank condition of the original system without the interaction term holds. 18 The formal conditions for identiﬁcation include order and rank conditions. Basically, we need to exclude as many exogenous variables from each equation as we include endogenous variables (Wooldridge, 2010). 19 Fries and Kappler (2015) focus on the ﬁrst business cycle equation in their system using two stage least squares, rather than reporting the result of the whole system because it is certainly hard to ﬁnd appropriate exogenous instruments that identify the relationship between trade and FDI.

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We mainly focus on country pairs with the United States. Of course, the negative shock from the United States can be transmitted to France via Germany as well as directly to France, and there might be inter-linkage transmission channels, such as United States–Germany–France. However, we clarify further the direct impact of the negative shock from the United States on the real economies of other countries in order to address investors’ special preference for US assets. In Subsection 4.5, we discuss whether our main ﬁndings on business cycle and ﬁnancial integration between the United States and the rest of the world are generalized. Our main analysis focuses on the effect of the crisis on the international business cycle (i.e., contagion to the real economy), which is the partial derivative with respect to the crisis variable, ∂SYNCHi,us,t/ ∂Cus,t. This partial derivative represents how strongly the crisis shock affected business cycle comovement. Thus, the effect of the crisis on the business cycle is computed as follows, ∂SYNCHi,us,t/ ∂Cus,t = β1 + β3FIEQi,us,t + β5FIDBi,us,t. However, our main interest is to examine not only the real economic contagion effect but also the extent to which the real contagion depends separately on equity and debt market integration, as indicated by the coeﬃcients of β 3 = ∂(∂SYNCH i,us,t /∂C us,t )/∂FIEQ i,us,t , and β5 = ∂(∂SYNCHi,us,t/∂Cus,t)/∂FIDBi,us,t. If β3 is positive (negative), then real economic contagion marginally increases (decreases) with equity market integration. If β5 is positive (negative), then real economic contagion marginally increases (decreases) with debt market integration. For instance, Kalemli-Ozcan et al. (2013a) present a positive coeﬃcient on the interaction term between banking integration and the GFC variable, showing that higher banking integration is associated with more synchronized business cycle during the GFC period. 4. Empirical results 4.1. Main model with simultaneous equations Table 2 presents the results from the simultaneous equations model in (3) for business cycle comovement, each proxy of ﬁnancial integration, trade integration and production similarity, estimated using three-stage least squares (3SLS) analysis. Column (1) reports the results for the ﬁrst equation in the model, in which bilateral business cycle correlation is the dependent variable. First, the estimated coeﬃcient of equity integration is signiﬁcantly negative, whereas the coeﬃcient of debt market integration is signiﬁcantly positive. These two coeﬃcients of ﬁnancial integration on the international business cycle conﬁrm that different types of bilateral ﬁnancial integration have the opposite effects on business cycle co-movement, as suggested in Davis (2014). In addition, we show that trade integration has a positive effect on business cycle co-movement and countries with similar production structure (low values in SIMi,us,t) have more synchronized business cycle. The ﬁndings in Table 2 are consistent with those of Davis (2014), validating our system of equations indirectly. Our analytical model of real economic contagion includes the crisis variable and the interaction terms of the crisis with each form of ﬁnancial integration. The interaction terms with equity and debt market integration are shown to be statistically signiﬁcant. Speciﬁcally, the coeﬃcient of the interaction term with equity market integration is signiﬁcantly positive (0.04, s.e. = 0.02), corresponding to ∂(∂SYNCHi,us,t/∂Cus,t)/∂FIEQi,us,t, and that of the interaction with debt market integration is signiﬁcantly negative (−0.04, s.e. = 0.02), corresponding to ∂(∂SYNCHi,us,t/∂Cus,t)/∂FIDBi,us,t. Hence, the positive interaction term with equity integration indicates that a higher level of equity market integration with the United States ampliﬁed the effect of the GFC shock originating from the United States on business cycle co-movement. Conversely, the negative coeﬃcient of the interaction term with debt integration means that higher debt market integration with the United States buffered the effect of the GFC on business cycle co-movement. Again, during the GFC, the wealth effect through equity market integration and the balance sheet effect through debt market integration were mitigated, as discussed in Section 2. However, our empirical ﬁndings require additional explanation compared to Davis (2014). Although our results for equity and debt market integration on the international business cycle “during the GFC” are opposite to the ﬁndings in Davis (2014), the estimated coeﬃcients of both ﬁnancial integration types on business cycle co-movement over the whole sample period are consistent with those in Davis (2014). Please cite this article in press as: Ju Hyun Pyun, Jiyoun An, Capital and credit market integration and real economic contagion during the global financial crisis ☆, Journal of International Money and Finance (2016), doi: 10.1016/j.jimonfin.2016.04.004

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Table 2 Main results.

Dependent variables FIEQ × CRISIS FIDB × CRISIS FIEQ FIDB TI SIM CRISIS

(1)

(2)

(3)

(4)

(5)

SYNCH

FIEQ

FIDB

TI

SIM

0.042** (0.020) −0.041** (0.020) −0.048*** (0.010) 0.053*** (0.012) 0.009*** (0.003) −0.072*** (0.010) −0.010*** (0.003)

Log distance from the US Border with the US English language Common law US dollar peg Sum of bilateral capital control restrictions (equity) Abs. diff. of bilateral capital control restrictions (equity) Sum of bilateral capital control restrictions (debt)

0.101** (0.046) −0.113** (0.053) 0.780*** (0.041) 1.114*** (0.056) 0.284* (0.169) −0.216 (0.185) −0.105** (0.045) −0.002 (0.053) −0.426 (0.449) −0.047 (0.053) −0.02 (0.052) 0.049 (0.054) 0.019 (0.021) −0.014 (0.027)

0.153 (0.143) −0.051 (0.171) 0.088** (0.040) −0.068 (0.046) −0.629* (0.376) 0.114** (0.046) 0.011 (0.045) −0.090* (0.048)

0.155* (0.083) 0.156 (0.096)

0.631*** (0.116) 0.018 (0.028) 0.154*** (0.029) 2.469*** (0.084) −0.171*** (0.030)

0.013 (0.017)

0.110*** (0.031)

−0.027 (0.020) 0.010 (0.020)

Abs. diff. of bilateral capital control restrictions (debt) Regional trade agreement

0.006 (0.023)

Log product of bilateral GDPs per capita Abs. diff. of bilateral GDPs per capita Observations

0.028 (0.054) −0.121** (0.062) 0.049*** (0.017)

646

646

646

646

−0.106*** (0.013) −0.000002* (0.000001) 646

Note: Standard errors are reported in parentheses. *, **, and *** indicate signiﬁcance at the 10%, 5%, and 1% levels, respectively. Financial integration variables are adjusted by subtracting mean values for the interaction terms. Additional exogenous variables are the year dummy variables and the interaction terms between global ﬁnancial crisis dummy and time-varying exogenous variables (US dollar peg, sum of equity/debt market restrictions, absolute difference in equity/debt market restriction, log product of bilateral GDPs per capita, and absolute difference in bilateral GDPs per capita).

4.2. Including macroeconomic policy responses Macroeconomic policy responds to the internal and external shocks directly and inﬂuences the transmission of the shock. For instance, many advanced countries implemented expansionary ﬁscal and monetary policies in light of the GFC. To control for these policy responses “endogenously” and the effect of policy lags, we include the correlation of macroeconomic policy variables and their lagged terms (predetermined instrument variables) in the system of equations. In this analysis, business cycle co-movement is a function of policy correlation between countries and that the policy correlation is a function of business cycle co-movement, the crisis dummy, peg dummy, and its lagged term. The year-by year ﬁscal and monetary policy co-movement variables are computed similar to output correlation measure. The ﬁscal policy variable is calculated as the absolute value of differences in changes Please cite this article in press as: Ju Hyun Pyun, Jiyoun An, Capital and credit market integration and real economic contagion during the global financial crisis ☆, Journal of International Money and Finance (2016), doi: 10.1016/j.jimonfin.2016.04.004

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Table 3 Macroeconomic policy responses.

Dependent variables FIEQ × CRISIS FIDB × CRISIS FIEQ FIDB TI SIM CRISIS Monetary policy Fiscal policy

(1)

(2)

(3)

(4)

(5)

(6)

(7)

SYNCH

FIEQ

FIDB

TI

SIM

Monetary

Fiscal

0.052** (0.021) −0.041** (0.020) −0.029*** (0.008) 0.032*** (0.009) 0.002 (0.003) −0.039*** (0.010) −0.011*** (0.003) 0.124*** (0.025) 0.289*** (0.065)

0.149** (0.059) −0.140** (0.059) 0.849*** (0.040) 1.062*** (0.049) 0.197 (0.172) −0.556*** (0.171) −0.103** (0.045)

0.154 (0.154) 0.335* (0.175) 0.094** (0.042)

0.192** (0.083) 0.081 (0.089)

0.569*** (0.116) 0.031 (0.029)

−0.034 (0.044) −0.046 (0.045) 0.067*** (0.016)

−0.002 (0.016)

Lag of monetary policy

0.005 (0.007)

0.322*** (0.030)

Lag of ﬁscal policy Observations

0.004 (0.003)

569

569

569

569

569

569

0.317*** (0.039) 569

Note: Standard errors are reported in parentheses. *, **, and *** indicate signiﬁcance at the 10%, 5%, and 1% levels, respectively. Table 4 introduces the ﬁscal and monetary policy variables. The ﬁscal policy variable is calculated as the negative absolute value of government ﬁnal consumption expenditure growth rate differences between countries i and the United States in year t. The monetary policy variable is calculated in a similar way except that it uses average annual M2 growth rates. For simple presentation, this table does not report the coeﬃcients of the constant and exogenous variables in the main model, which are available from the authors upon request.

in government spending between the United States and individual countries. The monetary policy variable is calculated in a similar way except that it uses changes in M2. In addition, we use real interest rates collected from the WDI as a proxy for monetary policy. The results with real interest rates are also consistent with those with changes in M2. The results with the policy correlations in Table 3 support the robustness of our main results. Two monetary and ﬁscal policy variables show statistically positive signs on business cycle co-movement. Moreover, the results including policy co-movement variables are robust with alternative identiﬁcation: We consider policy variables as exogenous ones, but the main results are preserved, which are available from the authors upon request. 4.3. Tracing the theoretical channels: investment correlation Our theory emphasizes the investment channels by which ﬁnancial integration transmits shock across real economies because equity and debt market integration directly inﬂuences changes in investment. Thus, we provide a natural experiment for tracing the theoretical channels from ﬁnancial markets to real investment, and from the investment to real output. In Table 4, we add investment correlation measure which is constructed similar to output correlation measure into the system. Here, we allow ﬁnancial integration and the similarity in production structure to affect investment directly while investment and output co-movement are determined endogenously. The results using the measurement for investment co-movement are consistent with the main results in Table 2. In particular, the coeﬃcients on each type of ﬁnancial integration and the interaction terms with the crisis echo the main results in Table 2 although the interaction term for debt market integration turns out to be slightly insigniﬁcant. Note that the results with two alternative SYNCH measures show consistent and signiﬁcant effects of ﬁnancial integration on investment correlation as in Table 4. Please cite this article in press as: Ju Hyun Pyun, Jiyoun An, Capital and credit market integration and real economic contagion during the global financial crisis ☆, Journal of International Money and Finance (2016), doi: 10.1016/j.jimonfin.2016.04.004

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Table 4 Investment correlations.

Dependent variables

(1)

(2)

(3)

(4)

(5)

(6)

SYNCH

INV.

FIEQ

FIDB

TI

SIM

FIEQ × CRISIS

0.090* (0.052) −0.074 (0.054) −0.080*** (0.026) 0.107*** (0.031)

FIDB × CRISIS FIEQ FIDB TI

−0.0003 (0.002) −0.078*** (0.025)

SIM INVESTMENT CRISIS Observations

0.392*** (0.024) 0.008*** (0.002) 636

−0.046*** (0.008) 636

0.756*** (0.045) 1.121*** (0.063) 0.210 (0.184) −0.123 (0.196)

−0.114** (0.046) 636

0.320** (0.150) −0.147 (0.177)

0.087** (0.041) 636

0.150** (0.076) 0.131 (0.088)

0.054 (0.050) −0.129** (0.056) 0.037** (0.016)

0.475*** (0.113)

0.015 (0.028) 636

0.016 (0.016) 636

Note: Standard errors are reported in parentheses. *, **, and *** indicate signiﬁcance at the 10%, 5%, and 1% levels, respectively. Investment is the negative absolute value of real investment growth rate differences between countries i and the United States in year t. For simple presentation, this table only reports the coeﬃcients of endogenous variables and excludes those of the constant and exogenous variables in the main model, which are available from the authors upon request.

4.4. Testing the wake-up call hypothesis We test the wake-up call hypothesis that country’s fundamentals have a stronger inﬂuence than ﬁnancial linkages in amplifying or buffering negative shock – that is, macroeconomic conditions of the country play a more important role in contagion than does ﬁnancial integration (Bekaert et al., 2014). Moreover, it may be questioned whether our results can be driven by confounding factors that cause the synchronization of output. To address these issues, we include country fundamental factors that may inﬂuence contagion in the main simultaneous equation model. Domestic macroeconomic fundamental variables are foreign exchange reserves, current account balance, government budget balance, and unemployment rate which are similar to those used by Bekaert et al. (2014). All domestic variables are ratios to GDP except for unemployment rate. In addition to them, we include domestic ﬁnancial condition variable such as liquid liabilities (currency plus demand and interest-bearing liabilities of banks and non-bank ﬁnancial intermediaries) to GDP, indicating ﬁnancial depth. Table 5 shows the results with local fundamental factors of individual countries. The interaction terms with the equity and debt integration variables show signiﬁcant coeﬃcients, consistent with those in the results in Table 2, even as more local fundamental factors are included from columns (1) to (4). Thus, we conﬁrm the signiﬁcant and different roles of capital and credit market integration in the transmission of the negative shock during the GFC. Furthermore, we ﬁnd some evidence that high levels of reserves (in column 2) buffered against the spillover of the negative shock from the United States and induced business cycle divergence during the GFC. In column (4), which includes all domestic controls, the results on local fundamental variables are statistically insigniﬁcant while the coeﬃcients on the interaction term of the crisis with ﬁnancial integration remain signiﬁcant. 4.5. Generalizing the results The main results in Tables 2 to 5 shed light on the spillover of negative shock from the origin of the GFC, the United States, but it may be questioned whether the main results are speciﬁc to ﬁnancial integration deﬁned vis-à-vis the United States. In other words, is it a general observation that higher capital market integration leads to more synchronized business cycle and higher credit market integration Please cite this article in press as: Ju Hyun Pyun, Jiyoun An, Capital and credit market integration and real economic contagion during the global financial crisis ☆, Journal of International Money and Finance (2016), doi: 10.1016/j.jimonfin.2016.04.004

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Table 5 Wake-up call hypothesis.

Dependent variables FIEQ × CRISIS FIDB × CRISIS FIEQ FIDB TI SIM CRISIS Current account Current account × CRISIS Unemployment Unemployment × CRISIS

(1)

(2)

(3)

(4)

SYNCH

SYNCH

SYNCH

SYNCH

0.043** (0.019) −0.037** (0.019) −0.032*** (0.010) 0.037*** (0.011) 0.003 (0.003) −0.055*** (0.010) −0.008*** (0.003) 0.066*** (0.022) −0.038 (0.032) −0.046* (0.023) 0.020 (0.070)

Reserves Reserves × CRISIS

0.038** (0.018) −0.036** (0.018) −0.033*** (0.010) 0.039*** (0.010) 0.003 (0.003) −0.051*** (0.009) −0.009*** (0.003) 0.070*** (0.021) −0.015 (0.033) −0.054** (0.024) −0.017 (0.071) −0.008 (0.006) −0.028* (0.015)

Liquid liabilities Liquid liabilities × CRISIS

0.035** (0.017) −0.031* (0.017) −0.051*** (0.011) 0.056*** (0.011) 0.005 (0.003) −0.048*** (0.010) −0.010*** (0.003) 0.093*** (0.026) −0.067 (0.041) −0.042 (0.026) 0.014 (0.074) −0.015 (0.009) 0.001 (0.026) 0.000 (0.003) 0.001 (0.009)

Fiscal balance Fiscal balance × CRISIS Observations

566

566

503

0.032** (0.015) −0.027** (0.013) −0.018* (0.010) 0.020** (0.009) −0.001 (0.007) −0.036*** (0.009) −0.006** (0.003) 0.064*** (0.023) −0.054 (0.038) −0.056** (0.024) −0.020 (0.068) −0.028*** (0.008) 0.008 (0.021) 0.001 (0.003) 0.006 (0.008) 0.015 (0.027) −0.068 (0.066) 412

Note: Standard errors are reported in parentheses. *, **, and *** indicate signiﬁcance at the 10%, 5%, and 1% levels, respectively. Current account, unemployment, reserves, liquidity liabilities, and ﬁscal balance variables are country i’s fundamental variables. In the analysis, these variables are adjusted by subtracting mean values for the interaction terms. For simple presentation, this table only reports the ﬁrst equation and excludes the coeﬃcients on the constant and exogenous variables in the main model, which are available from the authors upon request.

results in more divergent business cycle during crises? Generalizing our results requires a more thorough investigation, and so, we save this question for future research for the following reasons. It may be suggested that we employ the same empirical framework to investigate the world– country pair sample. However, such a sample has a more heterogeneous property than our US–the rest of the world pair sample in various aspects. Thus, the world–country pair sample would require careful control for appropriate heterogeneity in the sample. Previous studies on ﬁnancial integration and business cycles address country heterogeneity. For instance, Kose et al. (2003) notice different risksharing patterns between advanced and developing economies. Kalemli-Ozcan et al. (2013a) focus on only developed countries’ banking integration and business-cycle co-movement before and after the GFC. In fact, we have implemented the same empirical analysis to investigate the world–country pair sample that excludes any country pairs with the United States as a reference. However, the results are quite sensitive to the choice of sample country (i.e., world vs. advanced country sample/world vs. world without tax-haven countries). In addition, given that the negative shock is transmitted from the United States to other countries, our system of equations captures the effects of the crisis emanating from the Unites States on business cycle Please cite this article in press as: Ju Hyun Pyun, Jiyoun An, Capital and credit market integration and real economic contagion during the global financial crisis ☆, Journal of International Money and Finance (2016), doi: 10.1016/j.jimonfin.2016.04.004

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co-movement through different forms of ﬁnancial integration. However, when expanding our international real business cycle framework to a world–country pair sample, the identiﬁcation of the transmission of the shock would be more complex, in particular, during the period of the crisis. Speciﬁcally, we would need to identify which country is the source of the negative shock and where the shock comes from. 5. Robustness tests 5.1. Alternative measures and other tests To check the robustness of the results, we ﬁrst repeat the main regressions in Table 2 by replacing our crisis dummy for the GFC with the alternative crisis index, the VIX. The VIX is a popular measure of the implied volatility of the S&P 500 index options; high VIX values indicate a high level of expected market volatility. The VIX was recorded at 11.56 on the last day of the year in 2006, 22.50 in 2007, and 40.00 in 2008. Thus, the VIX as a continuous variable shows the progress of the crisis. In the analysis, we use the standardized VIX measures because of different scales compared to other variables. Column (1) of Table 6 shows the results of the main business cycle equation using the VIX and supports our main message in Table 2. We consider alternative measures of output co-movement: First, the alternative measure for business cycle co-movement (SYNCH1) is taken from Morgan et al. (2004). We regress GDP per capita growth on country ﬁxed effect and year ﬁxed effect as follows: gi,t = αi + αt + νi,t for all countries i. Then the residuals (νi,t and νus,t) represent how much output growth of country i and the United States deviates from average growth over the estimation. We then construct the business cycle synchronization proxy as the negative absolute value of difference of residuals as follows: SYNCH1i,us,t = −|νi,t − νus,t|. This index measures how similarly output growth moves between two countries in any given year. Another output correlation measure, SYNCH2i,us,t, is constructed by computing output correlation coeﬃcients between countries with a ﬁve-year rolling window for comparative purpose with previous cross-section studies.20 SYNCH2i,us,t is based on the growth rate of real GDP per capita, adjusted for purchasing power parity. But notice that SYNCH2i,us,t computed for the years after the GFC inevitably includes the effect of the GFC because it is constructed as a ﬁve-year backward looking variable. So the co-movement effect of the ﬁnancial crisis is included in all of the second half of the sample for the relevant endogenous variable, which could bias the results. Columns (2) and (3) of Table 6 report the results with the alternative measures (SYNCH1i,us,t and SYNCH2i,us,t). The results are consistent with the results using SYNCHi,us,t. Since the dependent variable in column (3) is an estimate, we report bootstrapped standard errors to improve the accuracy of statistical inference. Column (4) of Table 6 introduces the alternative ﬁnancial integration measure using the market capitalization variable. Column (5) reports the result with the alternative production similarity measure (SIM1).21 We conﬁrm that the effect of the crisis on business cycle co-movement between the United States and other countries increased with higher levels of equity market integration between them, whereas the effect of the crisis on business cycle co-movement decreased with higher debt market integration. In addition, our main variables of ﬁnancial integration are bounded at zero and skewed to the right. To check whether extreme values of ﬁnancial integration on a long right tail drive the results, we winsorize ﬁnancial integration measures at the 1% and 99% level and exclude outlier information in column (6). The results on ﬁnancial integration are preserved and conﬁrm that the main results are not driven by the extreme values. Previous studies of Dées and Zorell (2012) and Imbs (2004) consider that ﬁnancial integration is exogenous to trade integration and production similarity in their system. We follow their speciﬁcations and examine whether different identiﬁcation strategy inﬂuences our results in column (7). The results

20 Previous literature employs correlation coeﬃcients to measure bilateral business cycle co-movements (Baxter and Kouparitsas, 2005; Davis, 2014; Dées and Zorell, 2012; Imbs, 2004, 2006). 21 For the alternative SIM (SIM1), gross value added for six broad sectors is obtained from UNNA data. The six sectors based on ISIC revision 4 are in the followings: agriculture, forestry, and ﬁshing (ISIC A); mining, manufacturing, utilities (ISIC B-E); construction (ISIC F); wholesale, retail trade, transportation, and accommodation (ISIC G-I); information and communication (ISIC J); other activities (ISIC K-R).

Please cite this article in press as: Ju Hyun Pyun, Jiyoun An, Capital and credit market integration and real economic contagion during the global financial crisis ☆, Journal of International Money and Finance (2016), doi: 10.1016/j.jimonfin.2016.04.004

18

(2)

(3)

(4)

(5)

(6)

(7)

Alternative crisis variable (VIX)

Alternative SYNCH (SYNCH1)

Alternative SYNCH (SYNCH2)

Alternative FIEQ/FIDB (FIEQ1/FIDB1)

Alternative SIM (SIM1)

Winsorizing FIEQ/FIDB at 1% and 99% levels

FIEQ/FIDB depend on only exogenous variables

Dependent variables

SYNCH

SYNCH1

SYNCH2

SYNCH

SYNCH

SYNCH

SYNCH

FIEQ × CRISIS

0.023** (0.011) −0.036*** (0.013) −0.085*** (0.013) 0.096*** (0.015) 0.021*** (0.005) −0.085*** (0.012) −0.008*** (0.002) 646

0.043** (0.019) −0.039** (0.019) −0.027*** (0.010) 0.036*** (0.011) 0.003 (0.003) −0.016* (0.009) −0.007*** (0.002) 646

1.973*** (0.734) −1.976*** (0.762) −0.894 (0.774) 1.068 (0.883) 0.146 (0.130) −0.658 (0.417) 0.190* (0.103) 644

0.046*** (0.017) −0.034** (0.016) −0.092*** (0.013) 0.067*** (0.011) 0.017*** (0.003) −0.115*** (0.011) −0.009** (0.004) 403

0.023* (0.014) −0.020 (0.014) −0.015* (0.008) 0.010 (0.008) 0.010*** (0.002) −0.107*** (0.015) −0.006** (0.003) 421

0.042** (0.020) −0.040* (0.021) −0.052*** (0.011) 0.057*** (0.012) 0.010*** (0.003) −0.073*** (0.010) −0.010*** (0.003) 646

0.076*** (0.019) −0.072*** (0.019) −0.050*** (0.010) 0.058*** (0.012) 0.008*** (0.003) −0.065*** (0.010) −0.009*** (0.003) 646

FIDB × CRISIS FIEQ FIDB TI SIM CRISIS Observations

Note: Standard errors are reported in parentheses. *, **, and *** indicate signiﬁcance at the 10%, 5%, and 1% levels, respectively. Column (4) reports bootstrapped standard errors because alternative SYNCH2 is an estimated measure. Column (1) uses the standardized VIX to indicate the GFC. Column (2) uses the synchronization measure with SYNCH1, which is the negative absolute value of the residual GDP growth rate differences and column (3) employs other alternative measure, SYNCH2, which is the correlation coeﬃcients between country i and the United States with a ﬁve-year rolling window. Column (4) employs alternative FIEQ/FIDB that are divided by market capitalization instead of GDP. Column (5) employs alternative SIM (the similarity in production structures) using more detailed industry categories from UN data. Column (6) winsorizes FIEQ/FIDB at the 1% and 99% levels. Column (7) introduces a different system that FIEQ/FIDB only depend on exogenous variables following Dées and Zorell (2012). For simple presentation, this table only reports the ﬁrst equation and excludes the coeﬃcients of the constant and exogenous variables in the main model, which are available from the authors upon request.

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(1) Robustness tests

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Table 6 Alternative measures and other tests.

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support our explanation of the role of each type of ﬁnancial integration in the transmission of negative shock to the real economy.

5.2. Dissecting ﬁnancial integration It may be argued that the measurements of each type of ﬁnancial market integration may not be detailed enough to capture the heterogeneous roles of ﬁnancial instruments in the transmission of the shock. For instance, holding government bonds is different from holding corporate bonds in terms of the transmission of the shock. However, because of the limitations of the data, we are unable to separate different categories of debts. Another concern is that our ﬁnancial integration measures are also biased because assets and liabilities of equity and debt securities could be asymmetric (i.e., country i’s capital market is highly integrated with the US market because US investors own too much of country i’s equities (liabilities) and country i’s investors own relatively few US equities (assets)). To deal with this issue of asymmetry, we split the ﬁnancial integration measures into assets and liabilities. Table 7 shows the results with separate assets and liabilities integration. We show that the separate integration measures (the shares of domestic assets owned by US investors and of US assets owned by domestic investors) do not display asymmetric effects but work in the same direction for business cycle synchronization during the crisis: both assets and liabilities integration in the capital market increased business cycle co-movement during the crisis, and both types of integration in the credit market decreased it during the GFC.

Table 7 Financial integration. (1)

(2)

(3)

(4)

(5)

(6)

(7)

Dependent variables

SYNCH

FIEQASS

FIEQLIB

FIDBASS

FIDBLIB

TI

SIM

FIEQASS × CRISIS

0.130** (0.061) 0.02 (0.032) −0.023 (0.021) −0.145*** (0.051) −0.02 (0.026) −0.064*** (0.013) 0.043*** (0.014) 0.069*** (0.022) 0.010*** (0.003) −0.071*** (0.011) −0.007** (0.003) 645

FIEQLIB × CRISIS FIDBASS × CRISIS FIDBLIB × CRISIS FIEQASS FIEQLIB FIDBASS FIDBLIB TI SIM CRISIS Observations

−0.037 (0.051) −0.067 (0.099)

0.115*** (0.038) 0.981*** (0.078) 0.311*** (0.070) −0.371*** (0.088) −0.03 (0.019) 645

0.217 (0.365) 0.035 (0.268)

0.052 (0.188) 0.047 (0.140)

0.087 (0.189) 0.913*** (0.108)

0.927*** (0.088) −0.076 (0.053)

−0.059 (0.110) −0.214 (0.219)

0.957*** (0.098) 0.063 (0.180) 0.086 (0.163) −0.036 (0.170) −0.074* (0.039) 645

−0.02 (0.173) −0.085 (0.201) 0.085** (0.043) 645

−0.242*** (0.082) 0.381*** (0.101) 0.023 (0.020) 645

0.815*** (0.281) 0.197 (0.122) 0.016 (0.132) −0.648*** (0.221)

0.734*** (0.137) 0.041 (0.029) 645

0.076 (0.149) −0.159** (0.072) 0.129* (0.068) −0.214** (0.107) 0.089*** (0.019)

−0.002 (0.017) 645

Note: Standard errors are reported in parentheses. *, **, and *** indicate signiﬁcance at the 10%, 5%, and 1% levels, respectively. FIEQ assets (FIEQASS), FIEQ liabilities (FIEQLIB), FIDB assets (FIDBASS), and FIDB liabilities (FIDBLIB) are the ﬁnancial integration measures for equity assets, equity liabilities, debt assets, and debt liabilities from/to the United States. In the analysis, these variables are adjusted by subtracting mean values for the interaction terms. For simple presentation, this table does not report the coeﬃcients of the constant and exogenous variables in the main model, which are available from the authors upon request.

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6. Conclusions We study the role of capital (equity) and credit (debt) integration in the transmission of the negative shock of the GFC from the US ﬁnancial markets – the epicenter of the GFC – to the rest of the world. Our empirical analysis reveals that a higher level of equity market integration during the GFC ampliﬁed the transmission of the US negative shock to many other counties and led to higher business cycle synchronization between the United States and the rest of the world. However, higher debt market integration with the United States during the GFC cushioned the transmission of the negative shock and led to lower business cycle co-movement. We ﬁnd that the effects of equity and debt market integration on business cycle co-movement during the GFC were opposite to the results of seminal research by Davis (2014). However, when computing the total effects of ﬁnancial integration in the whole sample period, which includes analysis before and after the GFC, we conﬁrm Davis’s (2014) ﬁndings. Our study contributes to the literature by linking the ﬁnancial market with the real market and examining the spillover from ﬁnancial market to real market during the crisis period. Future work will extend our analysis to a world–country pair sample and other crisis events, as well as identify ﬁnancial linkages that are more valid as buffers against real economic contagion, thereby providing important guidance. Appendix

Table A1 Country average of ﬁnancial integration and synchronization measures. Panel A. Advanced economies Capital markets (FIEQ)

Major currency areas (6) Canada France Germany Italy Japan United Kingdom Euro area (11) Austria Belgium Cyprus Finland Greece Ireland Malta Netherlands Portugal Slovenia Spain Newly industrialized Asian economies (3) Hong Kong SAR, China Korea, Rep. Singapore Other advanced economies (8) Australia Czech Republic Denmark Iceland New Zealand Norway Sweden Switzerland Avg. of advanced economies (28)

Credit markets (FIDB)

SYNCH

Total

ASS.

LIB.

Total

ASS.

LIB.

3.484 1.727 1.438 0.542 3.172 5.093

1.529 0.392 0.414 0.182 1.112 1.643

1.955 1.336 1.024 0.360 2.060 3.450

2.045 1.302 1.147 0.578 3.614 4.435

0.595 0.819 0.663 0.430 3.353 2.499

1.451 0.483 0.485 0.148 0.261 1.936

−0.008 −0.017 −0.016 −0.008 −0.009 −0.008

0.118 0.263 0.006 0.367 0.067 1.273 0.000 2.188 0.053 0.006 0.494

0.055 0.101 0.001 0.071 0.013 0.808 0.000 1.282 0.018 0.005 0.067

0.063 0.162 0.005 0.295 0.054 0.465 0.000 0.905 0.035 0.001 0.427

0.156 0.241 0.012 0.075 0.035 2.268 0.006 1.315 0.048 0.009 0.314

0.114 0.192 0.010 0.034 0.024 2.010 0.005 0.711 0.040 0.003 0.181

0.042 0.049 0.002 0.041 0.011 0.258 0.001 0.605 0.008 0.005 0.134

−0.010 −0.008 −0.026 −0.019 −0.035 −0.019 −0.022 −0.015 −0.017 −0.029 −0.012

0.648 0.687 0.622

0.102 0.088 0.354

0.545 0.600 0.268

0.387 0.192 0.399

0.368 0.093 0.342

0.018 0.099 0.057

−0.026 −0.025 −0.035

1.241 0.023 0.377 0.010 0.078 0.551 0.739 1.996 0.974

0.608 0.004 0.210 0.009 0.053 0.405 0.421 0.443 0.371

0.634 0.019 0.167 0.000 0.025 0.147 0.319 1.552 0.603

0.933 0.009 0.215 0.019 0.082 0.465 0.445 0.494 0.759

0.284 0.007 0.145 0.004 0.022 0.319 0.175 0.448 0.496

0.649 0.002 0.070 0.015 0.060 0.147 0.269 0.046 0.263

−0.011 −0.025 −0.010 −0.021 −0.016 −0.008 −0.015 −0.011 −0.017

(continued on next page)

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Table A1 (Continued) Panel B. Emerging and developing economies Capital markets (FIEQ)

Commonwealth of independent states (2) Kazakhstan Russian Federation Developing Asia (7) China India Indonesia Malaysia Pakistan Philippines Thailand Latin America and the Caribbean (10) Argentina Bolivia Brazil Chile Colombia Costa Rica Mexico Panama Uruguay Venezuela, RB Middle East and North Africa (3) Egypt, Arab Rep. Kuwait Lebanon Sub-Saharan Africa (2) Mauritius South Africa Central and Eastern Europe (5) Bulgaria Hungary Latvia Poland Turkey Avg. of emerging economies (29)

Credit markets (FIDB)

SYNCH

Total

ASS.

LIB.

Total

ASS.

LIB.

0.015 0.214

0.012 0.001

0.003 0.213

0.096 0.075

0.088 0.019

0.008 0.056

−0.051 −0.044

0.320 0.359 0.103 0.109 0.005 0.043 0.096

0.012 0.000 0.001 0.025 0.000 0.001 0.005

0.308 0.359 0.101 0.085 0.005 0.042 0.090

0.031 0.015 0.042 0.057 0.001 0.051 0.019

0.023 0.000 0.003 0.006 0.000 0.016 0.007

0.008 0.015 0.040 0.051 0.001 0.035 0.011

−0.081 −0.049 −0.034 −0.023 −0.011 −0.023 −0.028

0.062 0.000 0.640 0.158 0.029 0.001 0.414 0.125 0.001 0.004

0.053 0.000 0.013 0.115 0.016 0.001 0.010 0.001 0.001 0.001

0.009 0.000 0.628 0.043 0.013 0.000 0.404 0.124 0.000 0.003

0.086 0.002 0.196 0.118 0.074 0.004 0.357 0.048 0.017 0.076

0.046 0.001 0.018 0.056 0.030 0.002 0.117 0.018 0.006 0.036

0.041 0.000 0.178 0.062 0.044 0.002 0.240 0.030 0.011 0.040

−0.056 −0.021 −0.019 −0.023 −0.020 −0.021 −0.015 −0.046 −0.047 −0.042

0.019 0.010 0.005

0.000 0.010 0.004

0.019 0.000 0.001

0.019 0.005 0.006

0.009 0.005 0.004

0.010 0.000 0.002

−0.021 −0.015 −0.028

0.021 0.342

0.009 0.073

0.012 0.268

0.003 0.051

0.003 0.014

0.001 0.037

−0.027 −0.014

0.001 0.032 0.000 0.039 0.094 0.112

0.000 0.005 0.000 0.002 0.000 0.013

0.001 0.027 0.000 0.036 0.093 0.100

0.006 0.023 0.004 0.053 0.046 0.055

0.002 0.002 0.002 0.007 0.002 0.019

0.005 0.021 0.002 0.046 0.044 0.036

−0.038 −0.020 −0.042 −0.030 −0.035 −0.032

Note: Country classiﬁcation follows the IMF World Economic Outlook. Equity-market and debt-market columns report the average of equity and debt market integration measures, respectively, from 2001 to 2013. Total, ASS., and LIB. indicate the sum of assets and liabilities, of assets, and of liabilities, respectively. These measures (Total, ASS., and LIB.) are (total, assets, and liabilities) holdings between the United States and individual country divided by the sum of the current GDPs of both countries (percentage term).

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