On Corporate Borrowing, Credit Spreads and Economic Activity in Emerging Economies: An Empirical Investigation

Caballero, Julián; Fernández, Andrés; Park, Jongho Working Paper On Corporate Borrowing, Credit Spreads and Economic Activity in Emerging Economies: An Empirical Investigation IDB Working Paper Series, No. IDB-WP-719 Provided in Cooperation with: Inter-American Development Bank (IDB), Washington, DC Suggested Citation: Caballero, Julián; Fernández, Andrés; Park, Jongho (2016) : On Corporate Borrowing, Credit Spreads and Economic Activity in Emerging Economies: An Empirical Investigation, IDB Working Paper Series, No. IDB-WP-719, Inter-American Development Bank (IDB), Washington, DC, https://hdl.handle.net/11319/7793 This Version is available at: https://hdl.handle.net/10419/146497 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by-nc-nd/3.0/igo/legalcode On Corporate Borrowing, Credit Spreads and Economic Activity in Emerging Economies: An Empirical Investigation Julián Caballero A ndrés Fernández Jongho Park IDB WORKING PAPER SERIES Nº IDB-WP-719 A ugust 2016 Department of Research and Chief Economist Inter-American Development Bank A ugust 2016 On Corporate Borrowing, Credit Spreads and Economic Activity in Emerging Economies: An Empirical Investigation Julián Caballero* A ndrés Fernández* Jongho Park** * Inter-American Development Bank ** University of Maryland Cataloging-in-Publication data provided by the Inter-American Development Bank Felipe Herrera Library Caballero, Julián. On corporate borrowing, credit spreads and economic activity in emerging economies: an empirical investigation / Julián Caballero, Andrés Fernández, Jongho Park. p. cm. — (IDB Working Paper Series ; 719) Includes bibliographic references. 1. Corporate bonds-Developing countries. 2. Credit-Developing countries. I. Fernández, Andrés. II. Park, Jongho. III. Inter-American Development Bank. Department of Research and Chief Economist. IV. Title. V. Series. IDB-WP-719 Copyright © Inter-American Development Bank. This work is licensed under a Creative Commons IGO 3.0 Attribution- NonCommercial-NoDerivatives (CC-IGO BY-NC-ND 3.0 IGO) license (http://creativecommons.org/licenses/by-nc-nd/3.0/igo/ legalcode) and may be reproduced with attribution to the IDB and for any non-commercial purpose, as provided below. No derivative work is allowed. Any dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IDB's name for any purpose other than for attribution, and the use of IDB's logo shall be subject to a separate written license agreement between the IDB and the user and is not authorized as part of this CC-IGO license. Following a peer review process, and with previous written consent by the Inter-American Development Bank (IDB), a revised version of this work may also be reproduced in any academic journal, including those indexed by the American Economic Association's EconLit, provided that the IDB is credited and that the author(s) receive no income from the publication. Therefore, the restriction to receive income from such publication shall only extend to the publication's author(s). With regard to such restriction, in case of any inconsistency between the Creative Commons IGO 3.0 Attribution-NonCommercial-NoDerivatives license and these statements, the latter shall prevail. Note that link provided above includes additional terms and conditions of the license. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, or the countries they represent. http://www.iadb.org 2016 Abstract1 This paper studies the influence of external financial factors on economic activity in emerging economies (EMEs) motivated by a considerable increase in foreign financing by the corporate sector in EMEs since the early 2000s, mainly in the form of bond issuance. A quarterly external financial indicator for several EMEs is built using bond-level data on spreads of corporate bonds issued in foreign capital markets, and its relationship with economic activity is examined. Results show that the indicator has considerable predictive power on future economic activity. Furthermore, an identified adverse shock to the financial indicator generates a large and protracted fall in real output growth, and about a third of its forecast error variance is associated with this shock. These findings are robust to controlling for possible spillovers from sovereign to corporate risk, among other considerations. JEL classifications: E32, E37, F34, F37, G15 Keywords: Corporate bonds, Options adjusted spread, Economic activity, Emerging economies 1 Caballero (corresponding author): Research Department, Inter-American Development Bank, 1300 New York Avenue, NW, Washington, DC, Tel: +1 (202) 623-3556, email: [email protected]. Fernández: Research Department, Inter-American Development Bank, email: [email protected]. Park: Economics Department, University of Maryland, College Park, email: [email protected]md.edu. We benefited from conversations with Ozge Akinci, Wenxin Du, Jacek Rothert, Stephanie Schmitt-Grohé and Jesse Schreger. We are also grateful for comments received at the workshops held at the University of Maryland, the Central Bank of Peru, and the U.S. Naval Academy. Maria Daniela Sánchez and Santiago Téllez provided excellent research assistance. Any errors are our own. The information and opinions presented are entirely those of the author(s), and no endorsement by the Inter- American Development Bank, its Board of Executive Directors, or the countries they represent is expressed or implied. 1 Introduction One of the most important development in the macroeconomics of emerging market economies (EMEs) since the beginning of the twenty-first century is a major increase in their corporate sectors’ reliance on foreign debt. This has made the stock of international debt issued by these economies quadruple in a little over a decade, from an outstanding stock of debt of about 600 billion USD in the early 2000s to 2.4 trillion USD by the end of 2014;2and has created an intense debate about its macro implications and desirability. A benign view posits that for EMEs, often portrayed as credit-constrained small open economies, access to international capital markets by the corporate sector is essential for sustaining long-run economic growth, as it can provide domestic entrepreneurs with needed funds to finance new investment projects that would otherwise not be available from local sources. However, the costly crises of the 1990s and, more recently, the global financial crisis of 2008/2009, have taught us that greater access to capital markets also entails risks for EMEs, particularly stemming from abrupt changes in the amount and the cost of international capital available. This has placed at center stage the role of external financial conditions as important drivers of economic activity in EMEs. This paper seeks to shed new light on the role that external financial factors play when accounting for economic activity in emerging economies. Our particular interest is to quantify the extent to which changes in the lending conditions faced by the corporate sector of EMEs in world capital markets affect economic activity in these economies. For that purpose we build an external financial indicator for several EMEs using individual bond-level data on spreads from corporate bonds issued in foreign capital markets and traded in secondary markets. We then quantify how much information this indicator contains in terms of future fluctuations in economic activity in these economies, and how this activity responds to shocks in the indicator. Our focus is on bond issuance because, as we document in detail for 17 EMEs, it is this form of finance the one that corporates have preferred the most in recent years when increasing their reliance on international sources of funding. We find strong evidence that the external financial indicator that we construct contains information on future economic activity in EMEs, even after controlling for other domestic and external factors that may also drive aggregate fluctuations in these economies. Results from panel forecasting regressions indicate that, on average, an increase of 100 basis points in the external financial indicator is correlated with a decrease in real output growth of 0.22 percentage points in the following quarter, and up to 0.34 points three quarter ahead. Furthermore, using a panel structural vector autoregressive (SVAR) model, we find that an identified (positive) shock to the external financial indicator generates a large and protracted fall in economic activity. A one standard devi- 2This stylized fact is further documented in Section 3 as well as the 17 EMEs that we consider when computing the stock of international debt. ation shock in the external financial indicator, equivalent to an increase of 150 basis points, leads to a fall in real output growth of up to 0.75 percent four quarters ahead, and long-run mean growth is reached again only three years after the shock. Furthermore, 35 percent of the forecast error variance in real output growth is accounted by these shocks. This number is between two and three times those obtained in previous studies that quantified the role of risk premia shocks for emerging economies’ business cycles, but that relied solely on sovereign risk and did not account for the effect of corporate spreads. When looking at other macroeconomic variables, namely aggregate consumption and investment, we find that both react vigorously to shocks to the external financial indicator, but the former appears to be the one that is relatively most affected. These findings are robust to several extensions. First, our benchmark results are virtually unchanged after we control for possible spillovers from sovereign to corporate risk. This is consistent with the fact, also documented, that in recent times new international lending in EMEs has mainly been channeled to the corporate sector, while governments have substituted foreign sources of finance for domestic ones. Second, we show that, while considerably reduced, the information content that our external financial indicator possesses continues to be significant once we control for the VIX, a measure of uncertainty and risk aversion in global capital markets. Third, we find that the predictive power of the external financial indicator considerably increased during the world financial crisis and, more importantly, the post-recovery years. Moreover, its predictive ability continues to hold when we consider bonds issued only by non-financial corporations. Both results are consistent with the fact that, in the post-crisis period, the bulk of the surge in international bond financing by the corporate sector has been done by non-financial corporations. Lastly, we validate our results when using alternative schemes for identification of shocks to our measure of external financial indicator, alternative measures of economic activity, and alternative specifications of the panel SVAR. This paper is related to and contributes to four different literatures. The stylized facts that we document in terms of the patterns in external financing by EMEs contribute to the work by Shin (2014), Turner (2014) and Powell (2014), among others, on how corporations from emerging economies have stepped up their financing through international capital markets (rather than traditional bank lending). Our work complements this literature by providing a systematic analysis of the external financing patterns exhibited by several EMEs, particularly the large increase by non-financial corporations (NFCs) in international bond issuance. Our work, showing that fluctuations in spreads of international bonds issued by NFCs in EMEs contain information on future economic activity, contributes to a long-standing literature that studies the relevance of external financial factors when accounting for aggregate fluctuations 3 in these economies.3External financial factors in this literature are typically proxied by U.S. interest rates or spreads of EMEs’ sovereign debt. The papers in this literature usually estimate VAR models that identify the dynamic effects on EMEs’ business cycle from exogenous shocks to U.S. interest rates or EMEs’ sovereign spreads (see, e.g., the seminal papers by Canova,2005 and Uribe and Yue,2006).4This literature finds that external financial factors explain a sizeable proportion of business cycles in emerging economies. A recent paper by Akinci (2013), however, shows that the effect of international financial conditions on EME economic activity is driven not by fluctuations in U.S. interest rates, but by risk aversion in global financial markets—as proxied by the volatility of U.S. stock prices—and their effect on sovereign spreads.5We contribute to this literature by paying particular attention to the role of corporate external borrowing, instead of sovereign borrowing, motivated by the aforementioned shift in the composition of borrowers in foreign capital markets from sovereigns to corporates in EMEs. The empirical work we undertake in this paper in terms of constructing an external financial indicator directly from bond spreads is mostly inspired by Gilchrist et al. (2009) and Gilchrist and Zakrajsek (2012) on the effects of credit market shocks and economic fluctuations in the United States (subsequently extended to Western Europe by Gilchrist and Mojon,2014 and Bleaney et al., 2016). Our work expands their analysis to the case of EMEs, while simultaneously providing an analysis of the patterns of foreign finance in these economies, which in turn justifies our focus on the international bond issuance by the corporate sector. The paper is also related to a new vintage of dynamic, stochastic equilibrium models motivated by much of the empirical findings just highlighted. These models aim at accounting for business cycles in EMEs through financial shocks and the amplifying effects of financial frictions 3At least since D´ ıaz-Alejandro (1985) the literature has explored how international financial conditions affect EMEs. A strand of the literature focuses on the role of capital flows in driving economic conditions or the incidence of crises, either because of surges in inflows (see, e.g., Calvo et al.,1993;Fern´ andez-Arias,1996;Reinhart and Reinhart,2009, and Caballero,2016) or because of sudden stops in inflows (see, e.g., Calvo,1998 and Calvo et al.,2008). Another strand of the literature studies the effects of international interest rates and global risk aversion on EMEs’ business cycles (see references in main text). Our paper contributes to the latter literature. 4Several subsequent papers have followed the seminal works of Canova and Uribe and Yue, including the papers by Mackowiak (2007), Ag´ enor et al. (2008), and ¨ Osterholm and Zettelmeyer (2008). Izquierdo et al. (2008) take a different modelling approach, estimating a Vector Error Correction Model (VECM). Recently, a new vintage of papers using a GVAR approach have studied the global spillovers from U.S. monetary policy, including Chudik and Fratzscher (2011), Chen et al. (2012), Feldkircher and Huber (2016), and Georgiadis (2015). Despite the use of different samples, identifying assumptions and estimation techniques, they all find that external factors explain a sizeable proportion of business cycles in EMEs, ranging from 20 to 60 percent of the variability of economic activity. Neumeyer and Perri (2005) is an early paper showing that sovereign spreads in EMEs behave in a countercyclical manner, which is what subsequent work shows. 5The effect of global risk aversion on EMEs’ economic fluctuations have also been highlighted by Matsumoto (2011) and Carri` ere-Swallow and C´ espedes (2013); although, these papers are silent on its effect on country spreads. On the effects of global factors on EMEs’ sovereign spreads, Arora and Cerisola (2001), Gonz´ alez-Rozada and Levy-Yeyati (2008), and Ciarlone et al. (2009) show that EMEs’ sovereign spreads depend negatively on global financial conditions, such as U.S. interest rates, U.S. high-yield corporate spreads, and the volatility of U.S. stock prices, respectively. 4 for the decisions of private agents (see, most recently, Fern´ andez and Gulan,2015).6Our work contributes to this literature by providing empirical evidence of the hypotheses derived from these models regarding the links between corporate bond spreads and economic activity. Our results offer strong support to the key hypotheses in this literature insofar as external financial factors are a key determinant for economic activity in EMEs through their effect on the corporate sector. The paper is divided into seven sections, including this introduction. Section 2 summarizes the theoretical framework used to think about the links between international borrowing, credit spreads and economic activity in EMEs. Section 3 presents the stylized facts on international corporate borrowing in these economies. Section 4 describes how we construct the external financial indicator and studies its time series dynamics. Section 5 presents our benchmark results in terms of the forecasting information content of the external financial indicator, and the macro dynamics following a shock to it. Section 6 presents various extensions and robustness checks. Concluding remarks are presented in Section 7. An online Appendix includes further technical material as well as further robustness analysis. 2 External Corporate Borrowing, Credit Spreads and Economic Activity in EMEs: A Theoretical Framework Considerable progress has been made in recent years in building microfounded small open economy models that account for the linkages among external financial factors, foreign corporate debt issuance and economic activity in small EMEs. Two clear hypotheses emerge from these works: 1. Spreads on bonds issued by corporates of EMEs in international capital markets contain information on aggregate economic activity. Thus, proxies of economic activity in these economies ought to be correlated with these spreads over the business cycle. 2. Exogenous perturbations to these spreads will have an impact on future economic activity, mainly via their effect on aggregate investment and consumption. For the remainder of this section we provide a brief summary of the main insights from the theoretical frameworks developed in recent times that have established a link between external financial factors, foreign corporate debt and economic activity in these economies. The goal is not to provide a comprehensive literature review. Instead, we intend to lay out the main insights from these studies that give rise to the type of empirical tests undertaken in the rest of our work. The literature has postulated two reasons why agents in EMEs may borrow funds from world capital markets. One is associated with factors that affect the level of aggregate investment. The other relates to factors affecting aggregate consumption. Each one, in turn, articulates a chan- 6This research agenda was initiated by the contributions of Neumeyer and Perri (2005), and Uribe and Yue (2006). Subsequent works are Garc´ ıa-Cicco et al. (2010), Fern´ andez-Villaverde et al. (2011), Chang and Fern´ andez (2013), Fern´ andez et al. (2015a). In a recent theoretical contribution, Chang et al. (2016) study the business cycle effects of the endogenous choice of finance modes for emerging economies. 5 Figure 2. Corporate Gross Bond Issuance in EMEs by Country Brazil 0 20 40 60 80 100 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds Chile 0 5 10 15 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds Colombia 0 2 4 6 8 10 12 14 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds Mexico 0 10 20 30 40 50 60 70 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds Peru 0246 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds South Africa 0 5 10 15 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds Czech Republic 0 2 4 6 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds Hungary 0123456 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds Poland 0 1 2 3 4 5 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds Russia 0 20 40 60 80 100 140 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds Turkey 0 5 10 15 20 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds Israel 0 2 4 6 8 10 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds Indonesia 0 5 10 15 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds Korea 0 50 100 150 200 250 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds Malaysia 0 10 20 30 40 50 60 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds Philippines 0 2 4 6 8 10 12 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds Thailand 0 5 10 15 20 25 30 35 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bonds Domestic Bonds 1 Note: This figure shows gross issuance of international and domestic debt securities (bonds) by country on a nationality basis. The data are presented in billions of current U.S. dollars and sourced from Dealogic’s DCM database. See the Appendix for a description of how the country aggregates are obtained from transaction-level data and for a definition of international and domestic debt securities. 12 There are four additional stylized facts related to the issuance of debt securities in EMEs that we summarize here but further document in the Appendix for the sake of space. First, by and large, international bond issuance has been a corporate phenomenon, as sovereigns in EMEs have instead substituted foreign for domestic financing.19 Second, the increase in international bond reliance by the corporate sector has taken place with roughly the same strength in both financial and non-financial corporations.20 Third, the vast majority of international bond issuance is denominated in foreign currency, most of which is denominated in U.S. dollars (more than 60 percent, on average) or other non-local currency (20 percent). Fourth, the increase in foreign bond issuance by corporates in EMEs exceeds the recorded growth in economic activity in the post-financial crisis period observed in these economies. The ratios of gross bond issuance to GDP increased in most countries considered, particularly since the onset of the global financial crisis.21 Summing up, the systematic analysis of a pool of 17 EMEs reveals that bond issuance by corporations in these economies grew at a fast pace after the turn of the century, mostly driven by issuance in foreign capital markets. This trend accelerated after the Global Financial Crisis, and it was concentrated in bonds denominated in foreign currency, mainly USD, and outperformed other external sources of finance such as direct bank loans. This has led to the stock of debt by corporates in these EMEs to quadruple in little over a decade. Sovereigns, unlike corporates, have moved away from issuing bonds in international markets and have relied more on domestic markets. 4 An External Financial Indicator of Credit Spreads on Corporate Bonds in Emerging Economies 4.1 Constructing an External Financial Indicator We now describe the methodology and data sources that we use to construct an external financial indicator for emerging economies based on the bonds issued by their corporate sectors in international markets. We focus on these bonds since our goal is to capture international financial forces that affect economic activity in these economies. We construct the external financial indicator for the emerging economy kat quarter t (EF Ik t) by taking a weighted average of option-adjusted spreads (OAS) across a sample of bonds 19 A proper analysis of sovereign debt is beyond the scope of this paper. However, it ought to be noted that this trend in sovereign bond issuance should not be taken as evidence that sovereigns are more insulated from external financial shocks since, in some countries, international investors are the majority holders of this locally-issued, locallydenominated debt. 20 This is in line with the recent work of Cortina-Lorente et al. (2016), who document that 50 percent of bond issuance in developing countries is done by financial firms. 21 Latin America, for example, had similar shares of gross bond issuance in both domestic and international securities by 2008, in the order of 1 percentage point of GDP. By the end of our sample, in 2014, this had tripled for the case of international bonds, while it remained constant for domestic ones. 13 issued by the corporate sector of economy k. The concept of OAS is suitable for our purpose because it provides a way to homogenize spreads across a variety of bonds of different characteristics.22 Formally: EF Ik t=X i wk itsk it (1) where sk it is the OAS for bond iat time tand wk it its relative weight. The latter is computed as wk it =Bond Sizek i PNBk t j=1 Bond Sizek j (2) where NBk tdenotes the number of bonds issued by the corporate sector in economy kwhose OAS is available at time t, and Bond Sizek irefers to the size of bond imeasured in constant USD. Because Dealogic, our data source for bond issuance presented in the previous section, lacks information on OAS, we switch to Bloomberg when computing the external financial indicator. This data provider contains OAS for a large pool of bonds issued by corporates in emerging market economies since the late 1990s. When choosing the sample of bonds to compute the external financial indicator we follow a set of criteria. Among the universe of corporate bonds available in Bloomberg, we choose only those with at least one corresponding OAS value at a quarterly frequency for their lifetime. We also drop bonds from the sample if information is not available on either date of issuance, bond size, industry that issuer belongs to, maturity date, or currency of denomination. Among this pool of bonds, we focus only on USD-denominated corporate bonds that have been issued in foreign capital markets.23,24 22 The terminology “option” originally refers to the callability or puttability of the bond. The concept of OAS is introduced to account for a potential stop of cash flow as a result of call and put options being exercised. It also takes into account default risk since all possible future states of cash flow are considered in calculating OAS. Formally, let rtand ri,k tdenote, respectively, the (time varying) yield curves of the safe asset and the bond iin economy k, so that sk it =ri,k t−rt. An OAS, sk it, is computed after deriving ri,k tas a solution to the following equation (omitting the k index for simplicity) pi t= N X n=1 Y(n) M X τ=t Ci τ(n) 1 + rτ+ri t where pi tis the bid price of the risky bond i;Q(n)denotes the probability of nth path of the economy being realized; Mstands for maturity; and Ci τ(n)denotes the cash flow in the path n. See O’Kane and Sen (2005) and Gabaix et al. (2007) for further explanations on the OAS. 23 Due to lack of information on governing law and listing place for each bond in our Bloomberg terminal, we used information on ISIN and issuer’s country of incorporation to make sure that we kept only international debt securities in our sample. See the technical appendix for details on the definition of international debt securities we use. 24 Even though Bloomberg does not allow us to download data on the specific treasury used for the OAS computation, we manually checked the Bloomberg screen for a selected number of bonds and found that in all cases with available data it is a U.S. Treasury. We manually checked bonds for all possible combinations of issuer’s country of incorporation, country where the bond ISIN was assigned, and type of exchange where the bond is listed (unlisted or international, the latter defined as an exchange different from the issuer’s country of incorporation). We checked a 14 After dropping outliers (top and bottom 0.5 percentiles of OAS for the entire sample of bond-quarter observations by country), we were left with a total of 2,339 bonds and 23,791 (unbalanced) bond-quarter observations for the period 1999.Q2-2013.Q2 and across seven emerging economies: Brazil, Chile, Korea, Mexico, Malaysia, Peru and Philippines. Among the countries considered in the previous section, these were the ones for which at least one bond per quarter was observed for every quarter in the sample (we assigned countries based on issuer’s country of incorporation). The summary statistics of the dataset used to construct the external financial indicators are presented in Table 1. The average number of bonds per quarter is just under 400, and differs between countries. Brazil, Mexico, Korea and Chile exhibit the largest shares of the bonds considered, ranging between 185 to 1,061 bonds. In contrast, Malaysia, Peru and Philippines, exhibit less than a hundred different bonds. In all countries, the number of bond-quarter observations remains stable until 2009 and then increases until the end of the sample (not reported).25 The size of bond in Table 1refers to total proceeds (i.e., the dollar amount raised by the firm by issuing the bond). The average size is 329 million but the size distribution is highly (positively) skewed, akin to that documented in Gilchrist and Zakrajsek (2012) for U.S. corporate bonds. Maturity at issue and terms to maturity respectively represent years left to the maturity at issue date and at observation date. The mean is close to seven years for both variables. On average, they are two to three years shorter than the case for the U.S. reported in Gilchrist and Zakrajsek (2012). Arguably, this reflects the ability of U.S. firms to issue bonds at longer maturities than firms in EMEs, and it also echoes the findings of Broner et al. (2013) who document that EMEs tend to borrow short term. The mean OAS spread is 370 basis points (bp) for the sample period, and it is positively skewed, with a large standard deviation of 420 bp. The same pattern is observed across all seven countries in the sample, although considerable differences in the average OAS can be seen. Mexico and Brazil are the countries with the highest average OAS, 508 bp and 471 bp, respectively, while Chile (249 bp) and Korea (210 bp) exhibit the lowest levels, nearly half of those in Brazil and Mexico. total of 54 different bonds, which is the total number of combinations in our data. In all cases with available data the specific treasury used for the OAS calculation was a U.S. Treasury (seven bonds did not have information available). 25 The online appendix presents evidence that the subsample of bonds with OAS data is representative of the universe of bonds studied in Section 3. 15 Table 1. Dataset on Corporate Bond Spreads from Emerging Economics: Summary Statistics Country N. Bonds N. Obs. Statistics Mean SD Min Median Max All Countries 2339 23791 Number of bonds per quarter 394.96 201.92 176.00 324.00 1153.00 Size of bond ($ mil) 329.30 375.58 0.10 236.12 3037.77 Maturity at issue (years) 6.94 7.54 0.08 5.50 100.08 Term to maturity (years) 6.48 8.19 0.00 4.50 96.25 OAS spread (basis point) 370.52 420.93 27.24 254.94 5685.23 Brazil 1061 6666 Number of bonds per quarter 116.95 72.11 57.00 92.00 509.00 Size of bond ($ mil) 183.18 291.15 0.10 68.71 1814.23 Maturity at issue (years) 3.93 4.07 0.08 2.00 30.00 Term to maturity (years) 3.84 3.54 0.00 2.75 30.25 OAS spread (basis point) 471.29 510.36 33.50 342.90 5685.23 Chile 185 3186 Number of bonds per quarter 55.89 30.84 14.00 51.00 132.00 Size of bond ($ mil) 416.61 218.31 4.71 406.82 1185.77 Maturity at issue (years) 11.68 11.13 1.00 10.00 100.08 Term to maturity (years) 8.65 10.35 0.00 6.50 96.25 OAS spread (basis point) 249.33 143.52 27.24 229.28 1497.09 Korea 390 5170 Number of bonds per quarter 90.70 52.60 35.00 78.00 221.00 Size of bond ($ mil) 395.44 309.93 4.20 347.65 2053.47 Maturity at issue (years) 6.74 6.93 0.50 5.00 100.00 Term to maturity (years) 5.57 8.08 0.00 3.75 93.25 OAS spread (basis point) 209.78 130.03 36.18 186.43 1017.10 Malaysia 79 1704 Number of bonds per quarter 29.89 7.16 9.00 31.00 41.00 Size of bond ($ mil) 704.04 580.43 59.76 524.15 3037.77 Maturity at issue (years) 13.45 15.64 2.00 10.00 100.00 Term to maturity (years) 10.75 14.98 0.00 6.50 95.25 OAS spread (basis point) 211.67 203.68 37.64 177.09 2495.75 Mexico 485 5477 Number of bonds per quarter 96.09 43.67 51.00 77.00 211.00 Size of bond ($ mil) 493.93 477.60 0.19 333.77 2963.59 Maturity at issue (years) 10.00 6.56 0.75 9.50 32.00 Term to maturity (years) 7.71 6.59 0.00 6.25 32.00 OAS spread (basis point) 507.95 569.53 27.90 333.59 5415.02 Peru 60 310 Number of bonds per quarter 5.44 10.10 1.00 1.00 52.00 Size of bond ($ mil) 380.66 198.62 70.44 307.34 800.44 Maturity at issue (years) 9.12 3.35 3.50 10.00 25.00 Term to maturity (years) 7.36 3.42 0.75 7.38 25.00 OAS spread (basis point) 430.75 256.92 84.28 347.20 1496.38 Philippines 79 1278 Number of bonds per quarter 22.42 4.57 14.00 22.00 31.00 Size of bond ($ mil) 336.25 243.70 113.35 274.86 1184.59 Maturity at issue (years) 10.38 11.58 2.00 8.50 100.00 Term to maturity (years) 7.32 8.95 0.00 5.00 88.50 OAS spread (basis point) 405.47 219.87 55.16 356.61 1998.70 Note: This table reports summary statistics of the bonds in our dataset. The columns N. of Bonds and N. of Obs. report the number of bonds and the number of OAS-quarter observations in the sample for each country for the entire sample period of 1999.Q2-2013.Q2, respectively. Number of bonds per quarter refers to the number of bonds with an OAS observation at a given quarter. Size of bond is measured in real U.S. dollars (2010.Q3 = 100). OAS spread is the option-adjusted spread of a bond in basis points at a given quarter. Maturity at issue refers to the remaining years of a bond from its issuance date to its maturity date. Terms to maturity refers to the remaining years of the bond from a given quarter to its maturity date. We exclude from the sample OAS observations that are below (above) the country-specific 0.5th (99.5th) percentiles of OAS-quarter observations of all USD denominated bonds available in Bloomberg for the country (including sovereign bonds). 16 4.2 Dynamics of the External Financial Indicator We now document the time series dynamics of the external financial indicators constructed, paying close attention to its comovement with real economic activity. The left column in Figure 3plots, for each of the seven countries considered, the time series of EFI together with real annual GDP growth, during the sample period 1999.Q2-2013.Q2. The two variables exhibit negative comovement. This pattern is most evident during the fall in economic activity around the global financial crisis of 2008/9 and the subsequent recovery. The crisis period was characterized by spikes in all the EF Is. It is also noteworthy that our series of EFI fell to near pre-crisis levels as the EMEs in the sample recovered from the crisis. The negative comovement is also observed before the crisis in most countries, when these economies experienced sustained economic growth for several years while simultaneously our measure of EF I displayed long and protracted reductions.26 The degree of cyclicality of our measures of EF I is further assessed by computing their unconditional serial correlation with real GDP growth: corr ∆GDP k t,∆EF Ik t+jfor j=−4,−3, ..., 4; where ∆GDP k tis real annual GDP growth in economy kand ∆EF Ik t+jis the (annual) first difference in EF Ik. The results of this exercise are reported in the right column of Figure 3. They indicate that EF I is a leading indicator of economic activity, as the correlation exhibits its trough when j < 0,, i.e., economic activity today co-moves the most, and in opposite direction, with lagged changes in EF I. Lastly, it is worth noting that the EFIs constructed exhibit a strong comovement between them. In fact, the first principal component of the seven indicators accounts for 73 percent of the sample variance. Likewise, growth in the emerging markets considered also exhibits strong comovement: 65 percent of the sample variance is associated with the first principal component. We interpret this as evidence that EFI captures global financial forces that affect real economic activity in emerging economies. Later we will formally test this interpretation. 26 Peru is a notable exception, though. This country’s EFI remained flat for most of the episode prior to the crisis of 2008/9. This could partly reflect the lack of a well-established market of foreign bonds in this country during this period, as was documented in the previous section. 17 Figure 3. Real GDP and the External Financial Indicator Brazil 2000 2002 2004 2006 2008 2010 2012 −0.02 0.02 0.06 500 1000 1500 GDP growth (left axis) External Financial Indicator − basis point (right axis) −1.0 −0.5 0.0 0.5 1.0 j = −4 j = −3 j = −2 j = −1 j = 0 j = 1 j = 2 j = 3 j = 4 corr(Yt, EFIt+j) 95% C.I Chile 2000 2002 2004 2006 2008 2010 2012 −0.04 0.00 0.04 0.08 100 200 300 400 500 −1.0 −0.5 0.0 0.5 1.0 j = −4 j = −3 j = −2 j = −1 j = 0 j = 1 j = 2 j = 3 j = 4 Korea 2000 2002 2004 2006 2008 2010 2012 0.00 0.05 0.10 100 300 500 700 −1.0 −0.5 0.0 0.5 1.0 j = −4 j = −3 j = −2 j = −1 j = 0 j = 1 j = 2 j = 3 j = 4 Mexico 2000 2002 2004 2006 2008 2010 2012 −0.06 −0.02 0.02 0.06 200 400 600 800 −1.0 −0.5 0.0 0.5 1.0 j = −4 j = −3 j = −2 j = −1 j = 0 j = 1 j = 2 j = 3 j = 4 Malaysia 2000 2002 2004 2006 2008 2010 2012 −0.05 0.00 0.05 0.10 100 200 300 400 −1.0 −0.5 0.0 0.5 1.0 j = −4 j = −3 j = −2 j = −1 j = 0 j = 1 j = 2 j = 3 j = 4 Peru 2000 2002 2004 2006 2008 2010 2012 0.00 0.05 0.10 200 600 1000 1400 −1.0 −0.5 0.0 0.5 1.0 j = −4 j = −3 j = −2 j = −1 j = 0 j = 1 j = 2 j = 3 j = 4 Philippines 2000 2002 2004 2006 2008 2010 2012 0.02 0.04 0.06 0.08 200 400 600 800 −1.0 −0.5 0.0 0.5 1.0 j = −4 j = −3 j = −2 j = −1 j = 0 j = 1 j = 2 j = 3 j = 4 1 Note: These figures show the time series dynamics of the external financial indicators (EFI) we constructed for each country in our sample with corporate spreads data and their comovement with real economic activity. The left column presents the times series of EFI in levels (diamond/red) and of annual real GDP growth rate (solid/black), both at a quarterly frequency. The right column presents the correlation between real GDP growth and changes in EFI at different lags (corr (∆GDPt,∆EFIt+j)) for j=−4,· · · ,4. Red dotted lines represent a 95% confidence interval. The sample period is 1999.Q2-2013.Q2. 18 5 The External Financial Indicator and Economic Activity The evidence presented so far is consistent with the hypothesis that spreads on bonds issued by corporates of EMEs in international capital markets have information on aggregate economic activity which would explain the negative comovement observed between the two variables over the business cycle. We turn now to a more formal analysis of this hypothesis by quantifying the information content and predictive ability of credit spreads of these bonds on economic activity. We will also evaluate the hypothesis that exogenous perturbations to these spreads have an impact on future economic activity. 5.1 Forecasting Information Content of the External Financial Indicator When assessing the information content and predictive ability from credit spreads of corporate bonds issued in international markets on economic activity in emerging economies, we extend Gilchrist and Zakrajsek’s (2012) forecasting specification to a multi-country panel setting. Formally, we estimate a dynamic balanced panel regression of real GDP growth against changes in EF I: ∆GDP k t+h=αk+ p X j=0 βj∆GDP k t−j+γ∆EF Ik t+ ΓΩt+e Γe Ωk t+k,t+h,for h≥1(3) where kdenotes each of the seven emerging economies considered, k= 1, ..., 7; and h≥1 is the forecast horizon. In our benchmark specification we fix h= 1, but later consider alternative values. Variables ∆EF I and ∆GDP are annual changes in EF I and the (log of) real GDP, respectively.27 We set the lag length equal to one (p= 0), although we later explore a richer lag specification structure as robustness. The estimated period starts in 1999.Q2, when our EFI series begin, and covers until 2013.Q2. We estimate the dynamic panel regression with country fixed effects (αk) and use a set of controls that are country-specific (e Ωk) and of global nature (Ω). The choice of controls is motivated by the literature on drivers of economic activity in emerging economies (see the Introduction). In particular, guided by previous studies on the role of global factors when accounting for economic activity in EMEs, we include in Ωtwo variables that are common across the seven countries and that aim at capturing the role of foreign factors beyond those already captured in changes of EF I: (annual) changes in term spreads of 3-month and 10-year U.S. Treasury yields, ∆USY ield Curve, and (annual) changes in the real U.S. Federal Funds Rate, ∆RF Ft. These two were included in Gilchrist and Zakrajsek’s (2012) original specification for the U.S. economy as domestic controls. In terms of the country-specific controls, we include in e Ωka measure of the domestic monetary 27 We will refer to annual real GDP growth and ∆GDP interchangeably form now on. 19 policy stance by including the (annual) change in the policy rate in real terms, ∆RLocalRate, and (annual) changes to a country-specific commodity price index that uses as weights the share of the commodities exported by each emerging economy relative to total exports, deflated by the U.S. CPI, ∆RP com.28 Thus, our framework examines the marginal information content of credit spreads, as proxied by EF I, conditional on the stance of external and domestic monetary policies as well as real shocks coming from commodity exports.29 We estimate the model using the Least Square Dummy Variable estimator (LSDV). The estimated coefficients are reported in Table 2. Numbers in parenthesis are t-statistics adjusted for standard errors clustered by country. The columns report results according to 5 alternative specifications that vary according to the set of controls used. In the first column, no controls are used. The second column reports results where we add the two global controls, Ωt= [∆USY ield Curvet; ∆RFFt]. The third and fourth specifications are reported in the next two columns where we sequentially include the two domestic controls without global controls, e Ωk t= [∆RLocalRatek,t] and e Ωk t= [∆RP comk,t], respectively. The final specification reported in the last column reports results when all controls are included. Our proxy for global financial conditions for emerging economies, ∆EF I, is a statistically significant predictor of economic activity in these countries. The coefficient associated to this variable is estimated to be negative and statistically significant at 5percent significance level in all five specifications considered. Moreover, the magnitude of the estimated coefficient implies a strong and negative relationship between contemporaneous values of changes in EFI and future real output growth. According to the estimated coefficient from the last specification considered, bγ=−0.000022, an increase in EFI of 100 basis points in the current quarter is correlated with a reduction of 0.22 percentage points in the output growth rate in the next quarter. This is a considerable reduction considering that such an increase is common in the data (e.g., a one standard deviation in ∆EF I is 195 basis points). The two external controls, ∆USY ield Curvetand ∆RFFt, are significant when added alone in Spec. 2 and when added jointly with the two country-specific controls in Spec. 5. Moreover, both have positive coefficients, which we interpret as coming from the fact that monetary policy in the U.S. is countercyclical (i.e., interest rates increase to smooth stronger economic activity), which has positive spillovers for emerging economies. Neither of the country-specific con- 28 ∆RPcom is computed by weighting the international prices of 44 distinct commodities goods in international markets by their country-specific (constant) weights computed as their share in total commodity exports. The source (and motivation) for using ∆RPcom comes from Fern´ andez et al. (2015a) who, among others, argue that exogenous fluctuations in the price of commodities that emerging economies export are an important driver of their business cycles. See this work for further details on the construction of this variable. 29 It may be argued that more variables could be added to model 3in order to enhance the forecasting ability of our specification (e.g., industrial production). Such task, however, is not the aim of this investigation. Instead, our goal is to assess the information that the external financial indicator contains over and above a set of standard macro variables. 20 Table 2. Panel Forecasting Regression Spec 1 Spec 2 Spec 3 Spec 4 Spec 5 ∆GDPt0.75*** 0.82*** 0.74*** 0.69*** 0.75*** (28.32) (22.59) (28.70) (14.34) (21.42) ∆EFIt-0.000029** -0.000027** -0.000028** -0.000025** -0.000022** (-3.30) (-3.35) (-3.66) (-2.72) (-2.97) ∆US Y ield Curvet0.0025* 0.0032** (2.27) (2.66) ∆RFFt0.0024** 0.0026** (2.70) (2.67) ∆R Local Ratet0.00078 0.00046 (1.43) (0.86) ∆R Pcomt0.012 0.015* (1.38) (2.44) Adjusted R20.681 0.704 0.686 0.688 0.718 Observations 371 371 371 371 371 Note: This table presents the benchmark results of country fixed-effect panel regressions. The dependent variable is the one-quarter ahead annual real GDP growth rate at a quarterly frequency (∆GDPt+1). ∆EF I (measured in basis points) refers to annual changes in the external financial indicator. ∆US Y ield (measured in percentage points) represents annual changes in the term spreads of 3-month and 10-year US treasuries. ∆RFF (measured in percentage points) is the annual changes in the real Federal Funds rate, which is the effective nominal Federal Funds Rate minus U.S. CPI inflation. ∆R Local Rate (measured in percentage points) is the annual changes in the domestic real monetary policy rate (which is computed as the domestic nominal policy rate minus the domestic inflation rate). We use as a proxy for the policy rate the money market rate or the monetary-policy-related interest rate. ∆R Pcom refers to annual changes in the composite commodity index of Fern´ andez et al. (2015a) (see Footnote 28 in the text for details on the construction of this index). The sample includes 7 emerging economies (Brazil, Chile, Korea, Malaysia, Mexico, Peru, Philippines) and the period of analysis is 1999.Q2-2013.Q2. Numbers in parentheses are t-statistics adjusted for standard errors clustered by country. * indicates significance at 10 percent level, ** indicates significance at 5 percent level, and *** indicates significance at 1 percent level. trols is significant when added separately in Specs. 3 and 4. Only ∆RP comk,t is significant when estimated with all other controls in Spec. 5, in which case it has a positive coefficient indicating that periods of high commodity prices are correlated with increases in macroeconomic activity. 5.2 Macroeconomic Effects of Shocks to the External Financial Indicator We turn now to examining the dynamic macroeconomic consequences of shocks to EF I in the EMEs considered. We do so by running a simple bivariate panel structural vector autoregressive model (see Love and Zicchino,2006 and Abrigo and Love,2016) of output growth and changes in EF I. Formally, the SVAR model is AYt=C+B1Yt−1+... +BpYt−p+ Φt(4) where Ytis a 2Kx1vector that collects all pairs of real output growth (∆GDP ) and changes in EF I (∆EF I) across the Kemerging economies; Cis a vector of constants with 21 Table 3. Panel Results Controlling for Sovereign Risk and Foreign Investors’ Risk Aversion Spec 1 Spec 2 Spec 3 Spec 4 Spec 5 w.o EFI Panel A. Controlling for sovereign risk ∆GDPt0.76*** 0.83*** 0.75*** 0.66*** 0.73*** 0.72*** (27.71) (21.82) (29.48) (11.61) (16.93) (18.17) ∆EF It-0.000035** -0.000035*** -0.000034** -0.000028** -0.000027** (-3.29) (-4.36) (-3.06) (-3.08) (-4.01) ∆EMBIt0.000010 0.000012 0.0000094 0.0000097 0.000012 -0.000012 (0.56) (0.82) (0.51) (0.61) (0.88) (-1.31) ∆US Y ield Curvet0.0023 0.0028* 0.0027* (1.78) (2.17) (2.04) ∆RF Ft0.0023* 0.0022* 0.0023* (2.20) (2.34) (2.18) ∆R Local Ratet0.00072 0.00040 0.00043 (1.18) (0.66) (0.67) ∆R P comt0.019** 0.020*** 0.024*** (3.33) (4.05) (4.24) Adjusted R20.685 0.704 0.690 0.704 0.726 0.707 Observations 317 317 317 317 317 317 Panel B. Controlling for foreign investors’ risk aversion ∆GDPt0.72*** 0.79*** 0.72*** 0.66*** 0.73*** 0.72*** (15.40) (17.44) (16.27) (10.09) (13.69) (13.93) ∆EF It-0.000019*** -0.000020*** -0.000019*** -0.000017*** -0.000018*** (-5.98) (-8.94) (-5.64) (-5.29) (-7.07) ∆EMBIt0.0000099 0.000012* 0.0000094 0.0000096 0.000011 -0.0000014 (1.22) (2.07) (1.03) (1.11) (1.67) (-0.30) ∆V IXt-0.00053** -0.00050*** -0.00050** -0.00042** -0.00036** -0.00049** (-3.83) (-4.37) (-3.66) (-2.73) (-2.70) (-3.76) ∆US Y ield Curvet0.0023 0.0027* 0.0026* (1.90) (2.11) (2.06) ∆RF Ft0.0021* 0.0021* 0.0021* (2.39) (2.43) (2.38) ∆R Local Ratet0.00048 0.00027 0.00024 (0.84) (0.45) (0.38) ∆R P comt0.013** 0.015** 0.016** (2.75) (3.50) (3.92) Adjusted R20.709 0.725 0.710 0.717 0.735 0.729 Observations 317 317 317 317 317 317 Note: This table shows results of country-fixed effect panel regression controlling for sovereign risk and foreign investors’ risk aversion. Panel A reproduces the results of the five specifications in Table 2 adding ∆EMBItas an additional control variable for sovereign risk. Panel B reproduces the results in Table 2adding both ∆EMBItand ∆V IXt, the latter as a control for foreign investors’ risk appetite. In both panels the last column drops the covariate for ∆EFI. In both panels the dependent variable is the annual real GDP growth rate at a quarterly frequency (∆GDPt+1). ∆EFI refers to annual changes in the external financial indicator. ∆US Y ield represents annual changes in the term spreads of 3- month and 10-year US treasuries. ∆RFF is the annual changes in the real Federal Funds rate, which is the effective nominal Federal Funds Rate minus U.S. CPI inflation. ∆R Local Rate is the annual changes in the domestic real monetary policy rate. ∆R Pcom refers to annual changes in the composite commodity index of Fern´ andez et al. (2015a). The baseline sample includes 7 emerging economies (Brazil, Chile, Korea, Malaysia, Mexico, Peru, Philippines) and the period 1999.Q2-2013.Q2, but in these tables the 1999.Q2 observation for Chile and all observations for Korea are dropped because of lack of EMBI data. Numbers in parentheses are t-statistics adjusted for standard errors clustered by country. * indicates significance at 10 percent level, ** indicates significance at 5 percent level, and *** indicates significance at 1 percent level. 28 Figure 6. Impulse Response Controlling for Sovereign Risk and Foreign Investors’ Risk Aversion 5 10 15 20 25 −0.010 −0.005 0.000 Response of GDP to a shock in EFI quarter GDP in response to EFI 95% CI IRF of GDP (benchmark) 5 10 15 20 25 −0.010 −0.005 0.000 Response of GDP to a shock in EFI quarter GDP in response to EFI 95% CI IRF of GDP (benchmark) 5 10 15 20 25 0.0 0.1 0.2 0.3 0.4 0.5 Variance Decomposition of RGDP quarter FEVD of RGDP FEVD of RGDP (benchmark) 5 10 15 20 25 0.0 0.1 0.2 0.3 0.4 0.5 Variance Decomposition of RGDP quarter FEVD of RGDP FEVD of RGDP (benchmark) 1 Note: This figure summarizes impulse response functions and forecast error variance decompositions of the panel VAR models after controlling for sovereign and external risks. The first column shows results from a trivariate panel VAR model including {∆GDPi,t,∆EMBIi,t,∆EF Ii,t}as endogenous variables. The second column shows results from a trivariate panel VAR model including {∆GDPi,t,∆EMBIi,t,∆EFIi,t}as endogenous variables, and {∆V IXt} as an exogenous variable. The top row presents the impulse response function of annual real GDP growth to a 1 standard deviation shock to ∆EFI. Red dotted lines represent a 95% confidence interval calculated using 500 draws of Monte Carlo simulations. The bottom row presents the forecast error decomposition of real GDP associated with shocks to ∆EFI. Green-triangle dotted lines represent results from Figure 4. Korea is excluded due to data availability on sovereign risk (EMBI). 1999.Q2 EMBI observation for Chile is dropped due to EMBI data availability. 29 6.4 Alternative Filtering Our benchmark results are computed using variables that are expressed in growth rates (or annual differences for spreads and interest rates). This is reasonable to the extent that it is a simple and tractable measure of changes in economic activity and foreign financial conditions. Yet, strictly speaking, this is not a transformation that allows to distinguish between trend and cyclical components of the variables considered, the latter being often the main object of analysis when thinking about changes in economic activity in the literature (see Introduction). We now assess the robustness of the benchmark results when variables are detrended. For this purpose we use the Hodrick-Prescott filter, a commonly used detrending procedure in business cycle analysis.37 The upper panel of Table 4reports the panel regression results with this alternative filter. All variables have been detrended using a smoothing parameter equal to 1,600. The table reproduces the same columns reported in our benchmark case (Table 2). The results are both qualitatively and quantitatively robust. The estimated coefficient for EFI is now bγ=−0.000019 for Specification 5, very close to the benchmark, and statistically significant at 10 percent. The Appendix contains the impulse responses which closely track those in the benchmark specification. 6.5 Alternative Forecasting Horizons In our benchmark analysis we arbitrarily fixed the forecasting horizon to be one quarter, h= 1, in (3). We now extend our analysis by considering alternative forecasting horizons. In particular, we consider the cases of h= 0,2,3,4. The case of h= 0 is one of “nowcasting” and can be thought as one where, because of reporting lags, economists typically do not observe current output growth, while financial asset prices that are used to construct EF I may be readily available. The results of these alternative forecasting horizons are presented in the lower panel of Table 4. For comparison, the first column reports the results in our benchmark case where h= 1 and the other columns present, respectively, the cases of h= 2,3,4,and 0.In all cases, the specification presented is the one with all controls active (Spec. 5 from Table 2). 37 A potential shortcoming that this detrending measure poses is the fact that it is a two-sided filter and thus may display less accuracy when extracting the trend component in the end points of any time series. This may be particularly inconvenient in this case, given that our objective is to study the information content within a forecasting regression framework. We try to minimize this problem by using out-of-sample observations on the variables used for the period 2013.Q3-2014.Q4. 30 Table 4. Alternative Filtering Method and Forecasting Horizons Spec 1 Spec 2 Spec 3 Spec 4 Spec 5 Panel A. Alternative filter ∆GDPc t0.74*** 0.77*** 0.73*** 0.66*** 0.70*** (24.94) (25.86) (26.07) (13.63) (15.66) EFIc t-0.000027** -0.000025** -0.000026** -0.000021* -0.000019* (-2.93) (-2.84) (-3.11) (-2.27) (-2.29) US Y ield Curvec t0.0010 0.0018* (1.23) (1.99) RFFc t0.0019** 0.0022** (2.73) (2.76) R Local Ratec t0.00048 0.000091 (1.14) (0.21) R Pcomc t0.015 0.017* (1.74) (2.27) Adjusted R20.675 0.688 0.677 0.686 0.701 Observations 399 399 399 399 399 Panel B. Alternative forecasting horizon h=1 h=2 h=3 h=4 h=0 ∆GDPt−10.70*** (13.39) ∆GDPt0.75*** 0.40*** 0.063 -0.24** (21.42) (7.18) (0.81) (-2.68) ∆EFIt-0.000022** -0.000032*** -0.000034*** -0.000024** -0.000013 (-2.97) (-3.88) (-4.24) (-2.74) (-1.60) ∆US Y ield Curvet0.0032** 0.0042* 0.0027 -0.00054 0.0011 (2.66) (2.41) (1.48) (-0.29) (0.88) ∆RFFt0.0026** 0.0048*** 0.0057*** 0.0046*** -0.00024 (2.67) (3.76) (5.71) (5.57) (-0.41) ∆R Local Ratet0.00046 0.00100 0.00092* 0.00056 0.00041 (0.86) (1.54) (2.19) (0.95) (0.79) ∆R Pcomt0.015* 0.020* 0.014 0.012 0.028** (2.44) (2.29) (1.66) (1.29) (3.46) Adjusted R20.718 0.449 0.360 0.328 0.721 Observations 371 371 371 371 371 Note: This table shows two robustness checks. Panel A reproduces results in Table 2using an alternative filter (Hodrick-Prescott filter with smoothing parameter λ= 1600; a superscript crepresents the cyclical component of the filtered series). Panel B reproduces Spec. 5 in Table 2for different forecasting horizons where the dependent variables are ∆GDPt+hand his a forecasting horizon. In Panel B, all variables are identically defined as in Table 2. Numbers in parentheses are t-statistics adjusted for standard errors clustered by country. * indicates significance at 10 percent level, ** indicates significance at 5 percent level, and *** indicates significance at 1 percent level. 31 The coefficient associated with ∆EF I increases in size (and statistical significance) as h increases above 1, although not monotonically. The highest value, in absolute terms, is found for h= 3, in which case bγ=−0.000034, a 55 percent increase relative to the one found in our benchmark case. And its statistical significance increases to 1 percent. The opposite occurs for the nowcast specification, where the coefficient reduces roughly to half of that in the benchmark specification, and is no longer significant at 10 percent. These results highlight the forecasting information content of EF I for economic activity in emerging economies. They are also in line with the serial correlations presented in Figure 3, which showed the strongest comovement between contemporaneous economic activity and lagged values of EFI in all the EMEs considered.38 6.6 Alternative Lag Order In the benchmark SVAR specification (4) we arbitrarily set the number of lags to be one, p= 1. We now consider alternative lag specifications.39 The upper left plot in Figure 7reports the impulse responses of output growth for the alternative cases when p= 2,3. For comparison, we also report those in the benchmark case. Solid lines represent impulses that are statistically significant at 95 percent confidence level. The results are qualitatively identical to the benchmark case. An orthogonal one S.D. shock to ∆EF I leads to a protracted fall in economic activity for all lag specifications considered. The trough also continues to lie one year after the shock. Quantitatively, the depth of the trough is 0.55 percent, slightly below that in the benchmark case where the trough is around 0.75 percent.40 6.7 Alternative Identification Scheme As argued before, the identification assumption used in the SVAR model—that shocks to ∆EF I affect output growth only with a lag while shocks to this variable may impact ∆EFI contemporaneously— has been common in the literature on both developed economies and EMEs. Nonetheless, we test the robustness of our results to an opposite timing/identification assumption where shocks to ∆EF I can affect output growth contemporaneously, but not vice-versa. The results of this alternative identifying assumption are reported in the upper right plot of Figure 7. The plot reports the point estimates of the impulse response of output growth from a one S.D. shock to ∆EF I under the new identification scheme, along with its 95 percent confi- 38 In terms of the controls, the U.S. yield curve reduces its significance, unlike the real FED’s fund rate which continues to be significant for h≥1, but not at h= 0. It also increases in magnitude until h= 3.The coefficient on the real local rate appears to be significant only at 10 percent when h= 3 and with a positive sign, which would signal that domestic monetary policy behaves in a countercyclical and forward-looking way. Finally, the coefficient on real commodity prices is strongest and statistically significant when h= 0 and then decreases in magnitude and statistical significance as the forecast horizon increases. 39 The Appendix contains also extensions of the forecasting panel regression (3) with additional lags. 40 Because the specification changes in these two alternatives, the size of the S.D. of the shock to ∆EF I also varies slightly. The shock has a magnitude of 147 and 142 basis points for the cases where p= 2,3, respectively. 32 dence bands. For comparison, we also include the point estimate of the impulse responses in the benchmark case. The results are robust. On impact, there is a small drop in output growth of about 0.1percentage point, although not statistically different from zero. The fall, however, continues until it bottoms after a year when output growth falls by 0.8percentage points. The persistence of the shock on real economic activity is also similar, as output growth returns to its long-run mean around three years after the initial shock. 6.8 The Role of the World Financial Crisis and Subsequent Recovery The stylized facts presented in Section 3 showed that, while the increasing trend of international debt securities started since the early 2000s, the trend accelerated most vigorously during the post-crisis recovery that began in mid-2009. Moreover, the time series of economic activity and EF I presented in Section 4 show that the negative comovement between the two is most evident during the world financial crisis of 2008/9 and the subsequent recovery years. In this subsection we investigate how much the post-2008 period matters for our benchmark results. To do so we re-estimate (3) from the beginning of our sample, 1999.Q2, until 2007.Q4, three quarters prior to the collapse of Lehman Brothers. We then sequentially reestimate (3) by adding to the sample one more observation at a time while keeping the starting period fixed at 1999.Q2. For each case we document the estimated values and p-values of γ, the coefficient that links ∆EFI with future states of economic activity. Results of this experiment are reported in the left and right middle panels of Figure 7. The left plot reports the recursive p-value statistics for each of the five specifications considered in Table 2. Four of the five coefficients considered are statistically significant in the first period considered up to 2007.Q4, and all five exhibit a decreasing trend in the p-values as new observations are added. Likewise, the right plot with the estimated coefficients for γshows that, as data of the crisis and the post-recovery are added, the negative coefficient increases in absolute terms.41 We view this as indicating that the crisis and, more importantly, the post-recovery period account for most of the large information content of EF I in terms of economic activity in emerging economies, although there is evidence that some of the predictive power predates the crisis. We posit that this result is a consequence of the large expansion in corporate bond issuance that began before the financial crisis, but accelerated afterwards, as documented in Section 3. 41 The decrease in the p-values is, however, not monotonic for Specs. 2 and 5, which control for U.S. interest rates and monetary policy stance. In particular, the p-values increase between 2008.Q3 and 2009.Q3. One also observes that, for this period, the decrease in the estimated coefficients of γslows down. This coincides with the most turbulent times of the financial crisis and the active use of monetary policy. The results seem to point that, for these two quarters, EFI0s information content decreased in favor of the information already embedded in the U.S. interest rates. 33 6.9 Removing Financial Corporations’ Bonds We now assess the extent to which the high information content in EF I comes from bonds issued by non-financial corporates in EMEs. For that purpose we test how much do our result hold if we remove from the construction of the EFI the bonds issued by financial corporations in each of the seven EMEs considered, using the methodology described in (1) and (2). This entails removing roughly half of the total number of bonds considered in the construction of the benchmark EF I.42 With this modified EF I we re-run the panel SVAR model (4) and compare the new impulse responses of output growth to those from the benchmark case. Results are reported in the lower plot in Figure 7. They are virtually identical to those coming from the benchmark case. There continues to be a large and protracted fall in economic activity following an orthogonal one S.D. shock to changes in the modified EF I. We view this as complementary to the evidence discussed in Section 3 that a considerable part of the rise in external bond issuance in emerging economies has been channeled via the corporate non-financial sector. 6.10 Excluding Countries How much are the benchmark results driven by one of the EMEs considered? We assess this by redoing the panel forecasting regression (3) excluding each of the seven countries considered, one at a time. The results are presented in Table 5which reports the coefficients from Specification 5 in Table 2sequentially, excluding each of the seven EMEs considered one at a time. Qualitatively, the results are robust for each of the seven cases/columns considered. In all of them the estimated coefficient of γcontinues to be statistically significant and of similar magnitude to the one estimated in our benchmark case. Our results are therefore not driven by an outlier country in the sample.43 42 Among the total of 2,339 bonds considered in the benchmark case, 1,206 are bonds issued by banks or other financial institutions. The Appendix plots the evolution in time of the size of the financial bonds that we remove for this experiment. 43 The Appendix also reports the results for cases where the SVAR and forecasting regressions are estimated for each country independently. 34 Figure 7. Various Robustness Checks 5 10 15 20 25 −0.010 −0.005 0.000 Alternative Lag Order Baseline lag=2 lag=3 5 10 15 20 25 −0.010 −0.005 0.000 Alternative Identification GDP in response to EFI 95% CI IRF of GDP (benchmark) P−value of Recursive Regressions 2008 2009 2010 2011 2012 2013 0.00 0.05 0.10 0.15 0.20 spec1 spec2 spec3 spec4 spec5 Coefficient of Recursive Regressions 2008 2009 2010 2011 2012 2013 −0.35 −0.25 −0.15 −0.05 0.00 spec1 spec2 spec3 spec4 spec5 x10−4 5 10 15 20 25 −0.010 −0.005 0.000 Only Non−Financials GDP in response to FI 95% CI IRF of GDP (benchmark) 1 Note: This figure presents different robustness checks to our baseline results. The top left panel presents impulse response functions of annual real GDP growth rate to a 1 standard deviation shock to ∆EFI for two different lag order specifications (p= 2 and p= 3). Solid lines represent statistically significant responses at the 95% confidence level. The top right panel presents the impulse response function of the bivariate panel VAR with ∆EFI and ∆GDP with an alternative ordering in the Cholesky decomposition so that ∆EFI affects ∆GDP contemporaneously but not vice-versa. The middle left panel presents the p-value of the coefficients corresponding to ∆EFI from rolling panel regressions for the five specifications in Table 2 (starting in 1999.Q2 through 2007.Q4 and adding one quarter at a time). The middle right panel presents the estimated coefficients of ∆EFI from these rolling regressions. The lower panel presents the impulse response function of the benchmark bivariate panel VAR with ∆GDP and ∆EFI, where EFI is calculated after excluding bonds issued by financial firms. 35 Table 5. Panel Regression Results Excluding Countries One at a Time w.o Brazil w.o Chile w.o Korea w.o Mexico w.o Malaysia w.o Peru w.o Philippines ∆GDPt0.74*** 0.76*** 0.73*** 0.76*** 0.76*** 0.72*** 0.76*** (17.34) (24.05) (17.11) (23.31) (21.14) (16.02) (21.41) ∆EFIt-0.000033*** -0.000022** -0.000020** -0.000020** -0.000020** -0.000021* -0.000020** (-4.69) (-2.92) (-2.81) (-2.87) (-2.88) (-2.20) (-2.91) ∆US Y ield Curvet0.0024 0.0038** 0.0028* 0.0034** 0.0030* 0.0029 0.0040** (1.92) (3.05) (2.07) (2.67) (2.21) (1.87) (3.63) ∆RFFt0.0017** 0.0027* 0.0022* 0.0027* 0.0024* 0.0029* 0.0031** (2.59) (2.38) (2.23) (2.37) (2.30) (2.54) (3.34) ∆R Local Ratet0.00068 0.00067 0.00043 0.00048 0.000095 0.00021 0.00066 (1.10) (1.12) (0.74) (0.78) (0.23) (0.40) (0.91) ∆R Pcomt0.0098 0.014 0.020** 0.013* 0.014* 0.017* 0.018** (1.80) (1.92) (3.63) (2.10) (2.08) (2.23) (2.80) Adjusted R20.734 0.721 0.724 0.694 0.725 0.713 0.728 Observations 318 318 318 318 318 318 318 Note: This table reproduces Spec (5) in Table 2dropping one country at a time from the pool of EMEs considered in the benchmark estimation (the dropped country is reported in the top of each column). All variables are identically defined as in Table 2. Numbers in parentheses are t-statistics adjusted for standard errors clustered by country. * indicates significance at 10 percent level, ** indicates significance at 5 percent level, and *** indicates significance at 1 percent level. 36 7 Conclusions Access to world capital markets by the corporate sector may be viewed as a necessary condition for emerging economies to achieve sustainable long-run growth. But it can also entail the risk that changes in the financial conditions under which the private sector borrows in these markets may carry destabilizing consequences for real economic activity. These considerations are more pressing now than ever because the corporate sectors of many emerging markets have largely increased their reliance on foreign debt. Motivated by this observation, we have provided a comprehensive analysis that sheds light on the extent to which changes in the international financing conditions by the corporate sector in emerging economies are related to macroeconomic fluctuations. We do so first by providing a summary of the theoretical literature that postulates the channels through which such economic effects may occur. We then provide empirical evidence of the increased access using information for a pool of 17 emerging economies, particularly in the form of larger corporate bond issuance denominated in USD. We relied on this stylized fact to construct an indicator of external financial conditions for several of these economies using option-adjusted spreads from bonds issued in foreign capital markets by the corporate sector that are traded in secondary markets. We show that changes in this indicator are strongly correlated with future economic activity in EMEs and that identified shocks to the indicator entail large and protracted falls in economic activity. While we have been silent about the policy implications of our analysis, the results we have presented do warrant a more normative analysis of the extent to which policy actions can (or should try to) mitigate the effects that changes in foreign financing conditions of the corporate sector may have on economic activity in EMEs. The large increase in the stock of foreign debt in the balance sheet of EMEs’ corporates will certainly keep this question at the forefront of international macroeconomics for the years to come. Hopefully the results in this paper will motivate further work to shed light on this question. References Abrigo, M. R. and Love, I. (2016). Estimation of Panel Vector Autoregression in Stata: A Package of Programs. Working Papers 201602, University of Hawaii at Manoa, Department of Economics. Ag´ enor, P.-R., Aizenman, J., and Hoffmaister, A. W. (2008). External Shocks, Bank Lending Spreads, and Output Fluctuations. Review of International Economics, 16(1):1–20. Aguiar, M. and Gopinath, G. (2007). Emerging Market Business Cycles: The Cycle Is the Trend. Journal of Political Economy, 115:69–102. Akinci, O. (2013). Global Financial Conditions, Country Spreads and Macroeconomic Fluctuations in Emerging Countries. Journal of International Economics, 91(2):358–371. 37 Appendix Figure A2. Stock of Private Sector International Debt in EMEs by Country Brazil 0 100 200 300 400 500 600 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans Chile 0 20 40 60 80 100 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans Colombia 0 10 20 30 40 50 60 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans Mexico 0 50 100 150 200 250 300 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans Peru 0 10 20 30 40 50 60 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans South Africa 0 20 40 60 80 100 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans Czech Republic 0 10 20 30 40 50 60 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans Hungary 0 20 40 60 80 100 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans Poland 0 20 40 60 80 100 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans Russia 0 100 200 300 400 500 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans Turkey 0 50 100 150 200 250 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans Israel 0 10 20 30 40 50 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans Indonesia 0 50 100 150 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans Korea 0 100 200 300 400 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans Malaysia 0 20 40 60 80 100 140 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans Philippines 0 10 20 30 40 50 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans Thailand 0 20 40 60 80 100 2000 2005 2010 2015 Total Debt (Billion US dollars) Bonds Loans 1 Note: This figure presents country dissaggregations of the stocks of international debt shown by region in Figure 1. As in Figure 1, this figure shows the aggregate stock of private sector international debt for 17 emerging economies, decomposing the outstanding stock into cross-border bank loans and international debt securities. The stock of securities is on a nationality basis. The private sector includes all financial institutions and non-financial corporations. The regional aggregations are as follows: East Asia and Pacific: Indonesia, Korea, Malaysia, Philippines, and Thailand. East Europe and Central Asia: Czech Republic, Hungary, Poland, Russia, and Turkey. Latin America: Brazil, Chile, Colombia, Mexico and Peru. Other Regions: South Africa and Israel. The data are presented in billions of current U.S. dollars and sourced from the BIS Locational Banking Statistics and BIS Securities Statistics databases. 44 Appendix Figure A3. Corporate Gross Bond Issuance in EMEs by Region East Asia Pacific 0 100 200 300 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bond Domestic Bond East Europe and Central Asia 0 50 100 150 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bond Domestic Bond Latin America and Caribbean 0 50 100 150 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bond Domestic Bond Other Emerging Economies 0 5 10 15 20 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bond Domestic Bond 1 Note: This figure presents regional aggregates of country-by-country gross issuance shown in Figure 2. As in Figure 2, this figure shows gross issuance of international and domestic debt securities based on a nationality basis. The regional aggregations are as follows: East Asia and Pacific: Indonesia, Korea, Malaysia, Philippines, and Thailand. East Europe and Central Asia: Czech Republic, Hungary, Poland, Russia, and Turkey. Latin America: Brazil, Chile, Colombia, Mexico and Peru. Other Regions: South Africa and Israel. The data are presented in billions of current U.S. dollars and sourced from Dealogic’s DCM database. See the Appendix for a description of how country aggregates are obtained from transaction-level data and for a definition of international and domestic debt securities. 45 Appendix Figure A4. Gross Corporate Bond Issuance by Region (on a Residence Basis) East Asia Pacific 0 50 100 200 300 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bond Domestic Bond East Europe and Central Asia 0 50 100 150 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bond Domestic Bond Latin America and Caribbean 0 50 100 150 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bond Domestic Bond Other Emerging Economies 0 5 10 15 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Bond Domestic Bond 1 Note: This figure reproduces regional aggregates of gross issuance shown in Figure A3, but on a residence basis. As in Figure A3, the regional aggregations are as follows: East Asia and Pacific: Indonesia, Korea, Malaysia, Philippines, and Thailand. East Europe and Central Asia: Czech Republic, Hungary, Poland, Russia, and Turkey. Latin America: Brazil, Chile, Colombia, Mexico and Peru. Other Regions: South Africa and Israel. The data are presented in billions of current U.S. dollars and sourced from Dealogic’s DCM database. See the Appendix for a description of how country aggregates are obtained from transaction-level data and for a definition of international and domestic debt securities. 46 Appendix Figure A5. Stocks of Sovereign Debt by Country (scaled by GDP) Brazil 0 20 40 60 80 100 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond Chile 0 5 10 15 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond Colombia 0 10 20 30 40 50 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond Mexico 0 10 20 30 40 50 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond Peru 0 5 10 15 20 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond South Africa 0 20 40 60 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond Czech Republic 0 2 4 6 8 10 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond Hungary 0 20 40 60 80 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond Poland 0 5 10 15 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond Russia 0 2 4 6 8 10 12 14 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond Turkey 0 10 20 30 40 50 60 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond Israel 0 10 20 30 40 50 60 70 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond Indonesia 0 5 10 15 20 25 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond Korea 0 10 20 30 40 50 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond Malaysia 0 10 20 30 40 50 60 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond Philippines 0 20 40 60 80 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond Thailand 0 10 20 30 2006 2008 2010 2012 2014 Total Debt Securities (% of GDP) International Bond Domestic Bond 1 Note: This figure shows the aggregate stock of sovereign debt in 17 emerging economies, decomposing the outstanding stock into international and domestic debt securities. The quarterly stocks are scaled by annual GDP (i.e., the sum of last 4 quarters). Data on stocks are from the BIS Securities Statistics database and data on GDP are from the IMF International Financial Statistics Database. 47 Appendix Figure A6. Corporate Gross Bond Issuance by Type of Issuer East Asia Pacific 0 100 200 300 2000 2005 2010 2015 Total Issuance (Billion US dollars) International Financial Bonds International non−Financial Bonds Domestic Financial Bonds Domestic non−Financial Bonds East Europe and Central Asia 0 50 100 150 2000 2005 2010 2015 Latin America and Caribbean 0 50 100 150 2000 2005 2010 2015 Other Emerging Economies 0 5 10 15 20 2000 2005 2010 2015 1 Note: This figure shows gross issuance of international and domestic debt securities by region based on a nationality basis, as in Figure A3, further decomposing issuance by issuer into non-financial and financial corporations. Financial corporations include issuance by any firm classified by the data vendor as operating in the “Finance” and “Insurance” sectors, and issuance by Closed End funds and Holding Companies. The regional aggregations are as follows: East Asia and Pacific: Indonesia, Korea, Malaysia, Philippines, and Thailand. East Europe and Central Asia: Czech Republic, Hungary, Poland, Russia, and Turkey. Latin America: Brazil, Chile, Colombia, Mexico and Peru. Other Regions: South Africa, and Israel. The data are presented in billions of current U.S. dollars and sourced from Dealogic’s DCM database. See the Appendix for a description of how country aggregates are obtained from transaction-level data and for a definition of international and domestic debt securities. 48 Appendix Figure A7. Corporate Gross Bond Issuance by Region (Scaled by GDP) East Asia Pacific 0 2 4 6 8 10 12 2000 2005 2010 2015 Total Issuance (% of GDP) International Bond Domestic Bond East Europe and Central Asia 0 1 2 3 4 2000 2005 2010 2015 Total Issuance (% of GDP) International Bond Domestic Bond Latin America and Caribbean 01234 2000 2005 2010 2015 Total Issuance (% of GDP) International Bond Domestic Bond Other Emerging Economies 0 1 2 3 4 2000 2005 2010 2015 Total Issuance (% of GDP) International Bond Domestic Bond 1 Note: This figure shows gross issuance of international and domestic debt securities by country based on a nationality basis, as in Figure A3, but scaling it by GDP. Data on quarterly gross issuance are scaled by annual GDP (i.e., the sum of last 4 quarters). Data on gross issuance are soured from Dealotic’s DCM database and data on GDP are from the IMF International Financial Statistics Database. The regional aggregations are as follows: East Asia and Pacific: Indonesia, Korea, Malaysia, Philippines, and Thailand. East Europe and Central Asia: Czech Republic, Hungary, Poland, Russia, and Turkey. Latin America: Brazil, Chile, Colombia, Mexico and Peru. Other Regions: South Africa, and Israel. 49 Appendix Figure A8. The Size of Non-financial and Financial Bonds Ammount of Bonds (Aggregate) Ammount of Bonds (unit: 2003 billion USD) 100 200 300 400 500 2000 2004 2008 2012 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of non−financial bonds Size of bonds (total) Size of Non−financial Bonds Fraction of Non−financial Bonds (right−axis) Brazil Ammount of Bonds (unit: 2003 billion USD) 20 40 60 80 100 120 2000 2004 2008 2012 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of non−financial bonds Chile Ammount of Bonds (unit: 2003 billion USD) 10 20 30 40 50 60 2000 2004 2008 2012 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of non−financial bonds Korea Ammount of Bonds (unit: 2003 billion USD) 20 40 60 80 100 2000 2004 2008 2012 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of non−financial bonds Mexico Ammount of Bonds (unit: 2003 billion USD) 20 40 60 80 100 120 140 2000 2004 2008 2012 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of non−financial bonds Malaysia Ammount of Bonds (unit: 2003 billion USD) 5 10 15 20 25 30 35 2000 2004 2008 2012 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of non−financial bonds Peru Ammount of Bonds (unit: 2003 billion USD) 0 5 10 15 20 2000 2004 2008 2012 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of non−financial bonds Philippines Ammount of Bonds (unit: 2003 billion USD) 2 4 6 8 10 12 14 2000 2004 2008 2012 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of non−financial bonds 1 Note: This figure shows the sum of the size of sample bonds (in 2003 Billion USD) used in constructing EFI, at a given quarter. The black solid line represents sum of both financial and non-financial bonds. The black dotted line only includes non-financial bonds (left-axis). The red solid line with triangles represents the fraction (right-axis) of non-financial bonds to total bonds. The top left panel represents aggregated statistics across countries, and other panels represents country-by-country statistics. As noted in Footnote 25, these figures show that the subsample of bonds with OAS data used in the paper is representative of the universe of bonds studied in Section 3insofar as they depict a large increase in issuance after the 2008/2009 crisis. 50 Appendix Figure A9. IRF and FEVD from the Uribe-Yue panel VAR 5 10 15 20 25 −0.025 −0.015 −0.005 0.005 IRF of GDP (Shock : EFI) quarter GDP in response to FI 95% CI IRF of GDP (benchmark) 5 10 15 20 25 −0.025 −0.015 −0.005 0.005 IRF of Investment (Shock : EFI) quarter Invesment in response to FI 95% CI 5 10 15 20 25 0.0 0.1 0.2 0.3 0.4 0.5 Variance Decomposition of RGDP quarter FEVD of RGDP FEVD of RGDP (benchmark) 5 10 15 20 25 0.0 0.1 0.2 0.3 0.4 0.5 Variance Decomposition of Investment quarter 1 Note: This figure summarizes impulse response functions and forecast error variance decompositions of a panel VAR model as in Uribe and Yue (2006). This model includes {∆GDPi,t,∆Invi,t,∆T rade Balancei,t,∆EFIi,t}as endogenous variables, and {∆RF Ft}as an exogenous variable. The top row presents an impulse response function of year-on-year real GDP, and investment growth rate to a 1 standard deviation shock in ∆EFI. Red dotted lines represent 95% confidence interval calculated using 500 draws of Monte Carlo simulations. The bottom row summarizes forecast error decompositions of real GDP and investment growth rates. Green-triangle dotted lines represent results from Figure 4. Data for Malaysia starts in 2005.Q1 due to lack of investment data. 51 Appendix Figure A10. Impulse Response Controlling for Sovereign and External Risk (Alternative Identifying Assumption) 5 10 15 20 25 −0.010 −0.005 0.000 Response of GDP to a shock in EFI quarter GDP in response to EFI 95% CI IRF of GDP (benchmark) 5 10 15 20 25 −0.010 −0.005 0.000 Response of GDP to a shock in EFI quarter GDP in response to EFI 95% CI IRF of GDP (benchmark) 5 10 15 20 25 0.0 0.1 0.2 0.3 0.4 0.5 Variance Decomposition of RGDP quarter FEVD of RGDP FEVD of RGDP (benchmark) 5 10 15 20 25 0.0 0.1 0.2 0.3 0.4 0.5 Variance Decomposition of RGDP quarter FEVD of RGDP FEVD of RGDP (benchmark) 1 Note: This figure reproduces results in Figure 6with an alternative ordering in the Cholesky decomposition; ∆EMBI responds to ∆EFI only in lags. The first column shows results from a trivariate panel VAR model which includes {∆GDPi,t,∆EF Ii,t,∆EMBIi,t}as endogenous variables. The second column shows results from a trivariate panel VAR model which includes {∆GDPi,t,∆EFIi,t,∆EMBIi,t}as endogenous variables, and {∆V IXt}as an exogenous variable. The top row presents an impulse response function of year-on-year real GDP to a 1 standard deviation shock in ∆EFI. Red dotted lines represent 95% confidence interval calculated using 500 draws of Monte Carlo simulations. The bottom row presents forecast error decomposition of real GDP associated with ∆EFI shocks. Green-triangle dotted lines represent results from Figure 4. Korea is excluded due to EMBI data availability. 1999.Q2 EMBI observation for Chile is dropped due to EMBI data availability. 52 Appendix Figure A11. Alternative Panel VAR 5 10 15 20 25 −0.025 −0.015 −0.005 0.005 Response of RGDP to an EFI shock quarter GDP in response to EFI 95% CI IRF of GDP (benchmark) 5 10 15 20 25 −0.025 −0.015 −0.005 0.005 Response of Investment to an EFI shock quarter Investment in response to EFI 95% CI 5 10 15 20 25 0.0 0.1 0.2 0.3 0.4 0.5 Variance Decomposition of RGDP quarter FEVD of RGDP FEVD of RGDP (benchmark) 5 10 15 20 25 0.0 0.1 0.2 0.3 0.4 0.5 Variance Decomposition of Investment quarter 1 Note: This figure summarizes impulse response functions and forecast error variance decomposition of the following panel VAR model which includes {∆GDPi,t,∆INVi,t,∆TBi,t,∆EMBIi,t,∆EFIi,t}as endogenous variables, and {∆RFFt,∆V IXt}as exogenous variables. The top row presents an impulse response function of year-on-year real GDP, and investment growth rate to a 1 standard deviation shock in ∆EFI. Red dotted lines represent 95% confidence interval calculated using 500 draws of Monte Carlo simulations. The bottom row presents forecast error decomposition of real GDP and investment growth rates. Malaysian sample starts in 2005.Q1 due to investment data availability. Korea is excluded due to EMBI data availability. Green-triangle dotted lines represent results from Figure 4. 1999.Q2 EMBI observation for Chile is dropped due to EMBI data availability. 53 Appendix Table A5. Panel Regression Results with Varying Number of Lagged Dependent Variables p=0 p=1 p=2 p=3 ∆GDPt0.75*** 1.02*** 0.97*** 0.96*** (21.42) (17.51) (17.31) (15.41) ∆GDPt−1-0.34*** -0.20*** -0.24*** (-7.63) (-4.34) (-4.67) ∆GDPt−2-0.12*** -0.0081 (-7.89) (-0.13) ∆GDPt−3-0.10 (-1.82) ∆EFIt-0.000022** -0.000014** -0.000013** -0.000013** (-2.97) (-2.86) (-2.92) (-3.09) ∆US Y ield Curvet0.0032** 0.0016 0.0013 0.0013 (2.66) (1.57) (1.32) (1.35) ∆RFFt0.0026** 0.0019** 0.0019** 0.0020** (2.67) (2.82) (3.10) (3.13) ∆R Local Ratet0.00046 0.00029 0.00024 0.00028 (0.86) (0.77) (0.65) (0.73) ∆R Pcomt0.015* 0.012** 0.011** 0.011** (2.44) (2.66) (2.65) (2.78) Adjusted R20.718 0.754 0.758 0.761 Observations 371 371 371 371 Note: This table reproduces Spec (5) in Table 2for alternatives values of p(the number of lags of ∆GDP). Numbers in parentheses are t-statistics adjusted for standard errors clustered by country. * indicates significance at 10 percent level, ** indicates significance at 5 percent level, and *** indicates significance at 1 percent level. 60 Appendix Table A6. Panel Regression Results with Time Fixed Effect and Crisis Dummy Variable Spec 5 Spec 6 Spec 7 Spec 8 Spec 9 Spec 10 ∆GDPt0.75*** 0.74*** 0.74*** 0.82*** 0.74*** 0.74*** (21.42) (17.27) (18.08) (17.08) (16.17) (16.89) ∆EF It-0.000022** -0.000021** -0.000018** -0.0000062 -0.0000062 -0.0000061 (-2.97) (-2.82) (-2.77) (-1.24) (-1.18) (-1.01) ∆US Y ield Curvet0.0032** 0.0037** 0.0038** (2.66) (2.73) (2.85) ∆RF Ft0.0026** 0.0027** 0.0026** (2.67) (2.62) (2.62) ∆R Local Ratet0.00046 0.00048 0.00044 -0.000023 (0.86) (0.89) (0.86) (-0.04) ∆R P comt0.015* 0.014* 0.012* -0.00017 (2.44) (2.20) (2.18) (-0.02) US ∆GDPt0.076 0.031 (1.31) (0.53) Adjusted R20.718 0.719 0.736 0.820 0.804 0.803 Observations 371 371 371 371 371 371 Country fixed effect Yes Yes Yes No Yes Yes Crisis dummy No No Yes - - - Time fixed effect No No No Yes Yes Yes Note: This table reports panel forecasting regression results with time fixed effect or a crisis dummy variable (1 if 2008.Q4, and 0 otherwise). The first column reports a baseline result (last column of Table 2). Numbers in parentheses are t-statistics adjusted for standard errors clustered by country. * indicates significance at 10 percent level, ** indicates significance at 5 percent level, and *** indicates significance at 1 percent level. 61 Appendix Table A7. Panel Regression Results with Time Fixed Effect and Crisis Dummy Variable and Alternative Forecasting Horizons h=1 h=2 h=3 h=4 h=0 ∆GDPt+1 ∆GDPt+2 ∆GDPt+3 ∆GDPt+4 ∆GDPt ∆GDPt−10.73*** (14.57) ∆GDPt0.74*** 0.45*** 0.19* -0.018 (16.89) (6.84) (2.42) (-0.19) ∆EF It-0.0000061 -0.0000098* -0.000013*** -0.000011 0.0000023 (-1.01) (-2.35) (-4.22) (-1.58) (0.55) ∆R Local Ratet-0.000023 0.000065 0.0000088 -0.000027 -0.000040 (-0.04) (0.10) (0.02) (-0.08) (-0.08) ∆R P comt-0.00017 0.011 0.026 0.029* -0.0083 (-0.02) (0.85) (1.69) (1.98) (-1.22) Adjusted R20.803 0.654 0.612 0.607 0.801 Observations 371 371 371 371 371 Country fixed effect Yes Yes Yes Yes Yes Time fixed effect Yes Yes Yes Yes Yes Note: This table reproduces the bottom panel of Table 4(panel forecasting regression for different forecasting horizons h) including both time and country fixed effects. Numbers in parentheses are t-statistics adjusted for standard errors clustered by country. * indicates significance at 10 percent level, ** indicates significance at 5 percent level, and *** indicates significance at 1 percent level. 62