corporate levarage imf 2015.9

7212019 Corporate Levarage IMF 20159

httpslidepdfcomreaderfullcorporate-levarage-imf-20159 134

he corporate debt of nonfinancial firms across major emerging market economies quadrupled between2004 and 2014

At the same time the composition of that corporate debt has been shifting awayfrom loans and toward bonds Although greater leverage can be used for investment thereby boost-ing growth the upward trend in recent years naturally raises concerns because many financial crises in

emerging markets have been preceded by rapid leverage growth Tis chapter examines the evolving influence of firm country and global factors on emerging market leverage

issuance and spread patterns during the past decade For this purpose it uses large rich databases Although thechapter does not aim to provide a quantitative assessment of whether leverage in certain sectors or countries isexcessive the analysis of the drivers of leverage growth can help shed light on potential risks

Te three key results of the chapter are as follows

First the relative contributions of firm- and country-specificcharacteristics in explaining leverage growth issuance and spreads in emerging markets seem to have diminishedin recent years with global drivers playing a larger role Second leverage has risen more in more cyclical sectorsand it has grown most in construction Higher leverage has also been associated with on average rising foreigncurrency exposures Tird despite weaker balance sheets emerging market firms have managed to issue bonds atbetter terms (lower yields and longer maturities) with many issuers taking advantage of favorable financial condi-tions to refinance their debt

Te greater role of global factors during a period when they have been exceptionally favorable suggests thatemerging markets must prepare for the implications of global financial tightening Te main policy recommenda-tions are the following First monitoring vulnerable and systemically important firms as well as banks and othersectors closely linked to them is crucial Second such expanded monitoring requires that the collection of dataon corporate sector finances including foreign currency exposures be improved Tird macro- and micropruden-tial policies could help limit a further buildup of foreign exchange balance sheet exposures and contain excessive

increases in corporate leverage Fourth as advanced economies normalize monetary policy emerging marketsshould prepare for an increase in corporate failures and where needed reform corporate insolvency regimes

SUMMARY

International Monetary Fund | October 2015 83

3 C H

A P

T E R

CORPORATE LEVERAGE IN EMERGING MARKETS991252A CONCERN

Prepared by Selim Elekdag (team leader) Adrian Alter Nicolas Arregui Hibiki Ichiue Oksana Khadarina Ayumu Ken Kikkawa SuchitraKumarapathy Machiko Narita and Jinfan Zhang with contributions from Raphael Lam and Christian Saborowski under the overall guidanceof Gaston Gelos and Dong He





CHAPTER 3 COR POR ATE L EV ER AGE I N EM ER GI NG M AR KETS 991252 A CONCER N


(Box 31) Another important recent development hasbeen the decline in cross-border bank lending largelydriven by supply-side factors specifically banksrsquoefforts to strengthen their balance sheets and satisfynew supervisory and regulatory requirements (seeChapter 2 of the April 2015 Global Financial Stability

Report [GFSR]) Accommodative global monetary conditions can

encourage leverage growth in emerging marketsthrough several channels In line with Caruana(2012) and He and McCauley (2013) three transmis-sion channels are worth highlighting (see also Brunoand Shin 2015) First emerging market central banksset lower policy rates than they would otherwisein response to the prevailing low interest rates inadvanced economies to alleviate currency apprecia-tion pressures Second large-scale bond purchases in

advanced economies reduce bond yields not only intheir own bond markets but also to varying degreesin emerging market bond markets through portfoliobalancing effects Likewise accommodative monetarypolicies in advanced economies are typically accompa-nied by greater capital flows into emerging marketsseeking higher returns Tird changes in policy rates

in advanced economies are promptly reflected in thedebt-servicing burden on outstanding emerging mar-ket foreign currency-denominated debt with variablerates Trough these channels expansionary globalmonetary conditions can facilitate greater corporateleverage through the relaxation of emerging marketborrowing constraints owing to the widespread avail-ability of lower-cost funding and appreciated collat-eral values3

A key risk for the emerging market corporate sec-tor is a reversal of postcrisis accommodative globalfinancial conditions Firms that are most leveragedstand to endure the sharpest rise in their debt-service costs once monetary policy rates in somekey advanced economies begin to rise Furthermoreinterest rate risk can be aggravated by rollover andcurrency risks Although bond finance tends to have

longer maturities than bank finance it exposes firmsmore to volatile financial market conditions (Shin2014b) In addition local currency depreciations

3Moreover expectations of continued local currency appreciationare likely to have created incentives to incur foreign currency debt incertain regions and sectors

60

90

120

150

180

210

2004 05 06 07 08 09 10 11 12 13

Figure 32 Emerging Market Economies Selected Leverage Ratios

Total liabilities to EBT

Aggregate debt-to-GDP ratio

Total liabilities to total equity

30

37

44

51

58

65

1994 96 98 2000 02 04 06 08 10 12

Total liabilities to EBIT

Total liabilities to EBITDA

Sources Orbis Thomson Reuters Worldscope and IMF staff calculat ionsNote EBT = earnings before taxes EBIT = earnings before interest and taxes EBITDA = earnings before interest taxes depreciation and amortization

1 Aggregate and Firm-Level Measures of Emerging MarketEconomiesrsquo Corporate Leverage(Index 2007 = 100 )

2 Emerging Market Economiesrsquo Corporate Leverage Listed Firms(Ratio median total liabilities to EBIT or EBITDA)



GLOBAL FINANCIAL STABILITY REPORT VULNERABILITIES LEGACIES AN D POLICY CHALLENG ES RISKS ROTATING TO EMERGING MARKETS

86 International Monetary Fund | October 2015

associated with rising policy rates in the advancedeconomies would make it increasingly difficultfor emerging market firms to service their foreigncurrency-denominated debts if they are not hedgedadequately

Corporate distress could be readily transmittedto the financial sector and contribute to adversefeedback loops Greater corporate leverage canrender firms less able to withstand negative shocksto income or asset values Tis vulnerability hasimportant implications for the financial system inpart because corporate debt constitutes a significantshare of emerging market banksrsquo assets (Figure 34)Terefore shocks to the corporate sector couldquickly spill over to the financial sector and generatea vicious cycle as banks curtail lending Decreasedloan supply would then lower aggregate demand

and collateral values further reducing access tofinance and thereby economic activity and in turnincreasing losses to the financial sector (Gertler andKiyotaki 2010)

Tis chapter highlights the financial stability implica-tions of recent patterns in emerging market corporatefinance by disentangling the role of domestic and exter-nal factors Te focus is on nonfinancial firmsrsquo corporateleverage bond issuance and spreads Key external fac-tors include measures of global economic and financialconditions Domestic factors considered include bond-firm- and country-level characteristics Although thechapter does not aim to provide a quantitative assess-ment of whether leverage in certain sectors or countriesis excessive the analysis of the key drivers of leveragegrowth can still help shed light on potential risks4

If rising leverage and issuance have recently beenpredominantly influenced by external factors thenfirms are rendered more vulnerable to a tightening ofglobal financial conditions Similarly a decline in therole of firm- and country-level factors in recent years would be consistent with the view that markets mayhave been underestimating risks In contrast if firmsissuing foreign currency debt have been reducingtheir net foreign exchange exposure through hedging

or other means simply focusing on the volume offoreign currency bond issuance would tend to over-state risks related to local depreciations

4Scenario analysis to assess emerging market corporate vulnerabilities has been discussed in various IMF studies includingChapter 1 of the April 2014 GFSR and in the latest IMF Spillover

Report (IMF 2015a) see also Chow (forthcoming)

1 EM Corporate Debt Composition(Billions of US dollars)

0

500

1000

1500

2000

2500

3000

3500

2003 04 05 06 07 08 09 10 11 12 13 14

2 EM Corporate Bond Composition(Billions of US dollars)

0

2

4

6

8

10

12

14

16

18

75

76

77

78

79

80

81

82

83

84

85

2003 04 05 06 07 08 09 10 11 12 13 14

3 EM Corporate Debt CompositionmdashBonds versus Loans(Percent of total debt)

0

2000

4000

6000

8000

10000

12000

14000

16000

1800020000

2003 04 05 06 07 08 09 10 11 12 13 14

Total bonds

Total loans

Bonds in foreign currency

Bonds in local currency

Bonds

Foreign bank lending

Domestic bank lending (right scale)

Sources Ayala Nedeljkovic and Saborowski 2015 Bank for InternationalSettlements Dealogic IMF International Financial Statistics database nationalauthorities and IMF staff calculationsNote EM = emerging market economy figure depicts major EMs

Figure 33 Emerging Market Economies Changing

Composition of Corporate Debt





Tis chapter addresses these issues by consideringthe following questions bull How have corporate leverage and bond issu-

ance in the emerging market nonfinancial sectorchanged over time and across regions sectorsand firms How have these funds been used Hashigher leverage or bond issuance been accom-panied by an increase in net foreign exchangeexposure

bull What is the relative role of domestic factorscompared with that of external factorsmdashsuch as

accommodative global financial and monetaryconditionsmdashin the change in leverage issuanceand corporate spread patterns Is there evidence of asmaller role for firm- and country-level factors dur-ing the postcrisis period

Te chapter goes beyond existing studies by jointlyanalyzing firm country and global factors as determi-nants of emerging market corporate leverage issu-ance and spreads Starting with Rajan and Zingales(1995) many papers have concluded that both firm-and country-specific factors influence corporate capi-tal structure internationally5 However these papersdo not focus on the way in which global financial

5Emerging market corporate capital structure including leveragehas been studied in the context of Asia in IMF (2014a) and forcentral eastern and southeastern Europe in IMF (2015c) Kalemli-Ozcan Sorensen and Yesiltas (2012) present novel stylized facts usingbank- and firm-level data with a focus on advanced economies

and monetary conditions may have influenced firmsrsquocapital structure decisions Relatedly some studieshave examined recent developments in bond issuanceby emerging markets mostly relying on aggregatedissuance data6 Te chapter builds upon the literature

by examining how global factors affect firmsrsquo deci-sions to issue bonds while explicitly accounting forbond- and firm-specific characteristics using largerich and relatively underexploited databases7 Finallythe chapter also considers emerging market corporatespreads a novel feature of that analysis is the useof relatively unexplored data on secondary marketcorporate spreads

Te main results of the chapter can be summarizedas follows bull he relative roles of firm- and country-specific

factors as drivers of leverage issuance and spreads

in emerging markets have declined in recent yearsGlobal factors appear to have become relativelymore important determinants in the postcrisisperiod In some cases evidence of a structuralbreak appears in these relationships with areduced role for firm- and country-level factors inthe postcrisis period

bull Leverage has risen relatively more in vulnerable

sectors and has tended to be accompanied by worsen-

ing firm-level characteristics For example higherleverage has been associated with on average ris-ing foreign exchange exposures Moreover leveragehas grown most in the cyclical construction sectorbut also in the oil and gas subsector Funds havelargely been used to invest but there are indica-

6For instance Lo Duca Nicoletti and Vidal Martinez (2014) andFeyen and others (2015) focus on bond issuance data aggregatedat the country and country-industry level respectively LikewiseRodriguez Bastos Kamil and Sutton (2015) study issuance in fiveLatin American countries

7Tis chapter is also related to a large literature on emergingmarket capital flows Various studies find that unconventionalmonetary policy in advanced economies has had a significantimpact on emerging market asset prices yields and corporatebond issuance (Chen and others 2014 Chen Mancini-Griffoliand Sahay 2014 Fratzscher Lo Duca and Straub 2013 Gilchrist

Yue and Zakrajsek 2014 Lo Duca Nicoletti and Vidal Martinez2014) IMF (2014b) identifies that global liquidity conditionsdrive cross-border bank lending and portfolio flows but areaffected by country-specific policies Other studies find thatthe exit from unconventional monetary policy appears to havedifferentiated effects across emerging markets depending ontheir initial conditions (Aizenman Binici and Hutchison 2014Eichengreen and Gupta 2014 Sahay and others 2015) see alsoNier Saadi Sedik and Mondino (2014)

0

10

20

30

40

50

60

70

M a l a y s i a

T h a i l a n d

S o u t h A f r i c a

B r a z i l

M e x i c o

C o l o m b i a

I n d o n e s i a

K o r e a

R o m a n i a

P h i l i p p i n e s

C h i l e

B u l g a r i a

T u r k e y

Sources IMF International Financial Statistics database and IMF staff

calculations

Figure 34 Domestic Banks Ratio of Total Corporate Loans to

Total Loans in 2014(Percent)





Shadow rates are indicators of the monetary policystance and can be particularly useful once the policy

rate has reached the zero lower bound (ZLB) Ashadow rate is essentially equal to the policy interestrate when the policy rate is greater than zero but itcan take on negative values when the policy rate is atthe ZLB Tis property makes the shadow rate a usefulgauge of the monetary policy stance in conventionaland unconventional policy regimes in a consistentmanner Shadow rates are estimated using shadowrate term structure models which take the ZLB intoaccount as originally proposed by Black (1995)1

Although shadow rate models are not easy toestimate because of the nonlinearity arising from theZLB the literature began to estimate shadow rates

with Japanrsquos data by applying nonlinear filtering

techniques (Ichiue and Ueno 2006 2007) Recentlythe shadow rates of other countries also have beenestimated by many researchers (for example Wu and

Xia forthcoming) and discussed by policymakers (forexample Bullard 2012)2

Tis box was prepared by Hibiki Ichiue1In term structure models interest rates of various maturities

are represented as a function of a small set of common factorsTis function is derived from a no-arbitrage condition

2Tere are limited papers that estimate shadow rates withoutusing term structure models Kamada and Sugo (2006) and

Estimated shadow rates reasonably reflect mon-etary policy events in unconventional policy regimes

Te US shadow rate estimated by Krippner (2014)turned negative in November 2008 when the FederalReserve started the Large Scale Asset Purchases pro-gram (Figure 311 panel 1) Te shadow rate furtherdeclined as the Fed adopted additional unconven-tional policies However it bottomed out in May2013 when the Fed raised the possibility of taperingits purchases of reasury and agency bonds and hascontinued to increase since then Te current level ofthe shadow rate is only slightly negative Te shadowrate estimates in the euro area Japan and the UnitedKingdom are consistent with their respective monetarypolicies (Figure 311 panel 2) Tese observationssupport the utility of shadow rates although their

limitations should be recognized Te global shadowrate which is calculated as the first principal compo-nent has been virtually flat in recent years reflectingthat the tighter stances in the United States and theUnited Kingdom have been offset by accommodativestances in Japan and the euro area

Lombardi and Zhu (2014) summarize multiple financial indica-tors such as monetary aggregates

Box 31 Shadow Rates

ndash6

ndash4

ndash2

0

2

4

6

8

1995 97 99 2001 03 05 07 09 11 13 15

1 In the United States 2 In Other Countries

Federa l funds rate Shadow rate

ndash10

ndash8

ndash6

ndash4

ndash2

0

2

4

6

8

10

1995 97 99 2001 03 05 07 09 11 13 15

Euro area Japan United Kingdom Global

Figure 311 Shadow Rates(Percent)

Sources Reserve Bank of New Zealand and IMF staff calculationsNote The global shadow rate is the first principal component of the shadow rates of the four central banks (Bank of England Bank ofJapan European Central Bank and US Federal Reserve)





tions that the quality of investment has declinedrecently hese findings point to increased vulner-ability to changes in global financial conditionsand associated capital flow reversalsmdasha pointreinforced by the fact that during the 2013 ldquotaper

tantrumrdquo more leveraged firms saw their corporatespreads rise more sharply

bull Despite weaker balance sheets emerging market firms

have managed to issue bonds at better terms (lower

yields longer maturities) with many issuers taking

advantage of favorable financial conditions to refi-

nance their debt No conclusive evidence has beenfound that greater foreign currency-denominateddebt has increased overall net foreign exchangeexposures

Tese results suggest that policy action is war-

ranted to guard against the risks associated with thetightening of global financial conditions as mon-etary policy in advanced markets begins to normal-ize

Te chapter makes the following five policyrecommendations bull Careful monitoring of vulnerable sectors of the

economy and systemically important firms as well astheir linkages to the financial sector is vital

bull he collection of financial data on the corporatesector including foreign exchange exposures needsimprovement

bull Macroprudential policies can be deployed to limitexcessive increases in corporate sector leverage inter-

mediated by banks Possible tools include higher capi-tal requirements (for example implemented via risk weights) for foreign exchange exposures and caps onthe share of such exposures on banksrsquo balance sheets

bull Microprudential measures should also be consideredFor instance regulators can conduct bank stress testsrelated to foreign currency risks including deriva-tives positions

bull Emerging markets should be prepared for corporatedistress and sporadic failures in the wake of mon-etary policy normalization in advanced economiesand where needed and feasible should reform

insolvency regimes

The Evolving Nature of Emerging MarketCorporate Leverage

his section documents the main patterns of cor-

porate leverage across emerging market regions

and sectors A formal empirical analysis focuses on

the changing relationship between corporate lever-

age and key firm country and global factors

The Evolution of Emerging Market Corporate Leverage

wo complementary data sets indicate noteworthy dif-ferences in the evolution of emerging market leverageacross regions and sectors8 bull For publicly listed firms leverage has risen in emerg-

ing Asia in the emerging Europe Middle East and Africa (EMEA) region in Latin America and acrosskey sectors (Figure 35)

bull he striking leverage increase in the construc-tion sector is most notable in China and inLatin America his increase relates to concernsexpressed in recent years about the connec-tion between global financial conditions capital

flows and real estate price developments in someemerging markets (Cesa-Bianchi Ceacutespedes andRebucci 2015)9

bull Leverage has grown in mining and even moreso in the oil and gas subsector hese sectors areparticularly sensitive to changes in global growthand commodity price fluctuations In particular oilprice declines can cut into the profitability of energyfirms and strain their debt-repayment capacity (seeChapter 1 of the April 2015 GFSR)

bull he patterns shift somewhat in relation to small-and medium-sized enterprises (SMEs) For instanceSME leverage seems to have declined in emerging Asia and in the manufacturing sector during thepast decade One reason for such contrasts is thedifferences in country composition across the twodata sets A key similarity across both data sets is theincrease in construction-sector leverage particularlyacross EMEA and Latin America

Both firm- and country-specific factors appear onaverage to have deteriorated across emerging mar-kets in the postcrisis period At the country levellower real GDP growth and higher current accountand fiscal deficits are examples of worsening post-

crisis macroeconomic conditions (able 31) Te

8One data set Tomson Reuters Worldscope contains publiclylisted firms which tend to be larger and have received greaterattention Te other Orbis predominantly includes unlistedsmall- and medium-sized enterprises and has been relativelyunderutilized

9See also httpblog-imfdirectimforg20140611era-of-benign-neglect-of-house-price-booms-is-over





International Country Risk Guide (ICRG) index

summarizes these and other key macroeconomicfundamentals and corroborates the bleaker domesticconditions in 2010ndash13 Even though liquidity hasedged up at the firm level since the crisis profit-ability solvency and a measure of asset quality havedeteriorated

Firms that took on more leverage have on averagealso increased their foreign exchange exposures

bull Net foreign exchange exposures are indirectly esti-

mated for listed firms using the sensitivity of theirstock returns to changes in trade-weighted exchangerates (Box 32)10

bull he estimated foreign exchange exposures highlightsectoral differences (Figure 36) Firms in nontrad-able sectors such as construction tend to have

10See also Acharya and others (2015)

Figure 35 Emerging Market Economies Corporate Leverage by Selected Regions and Sectors(Percent ratio of total liabilities to total equity)

1 Listed Firms

0

50

100

150

200

250

300

C o n s t r u c t i o n

M a n u f a c t u r i n g

M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s

0

20

40

60

80

100

120

140

160

180

A s i a

E M E A

E M E A

L a t i n

A m e r i c a

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

O i l a n d g a s

2 Listed and Private Firms Including Small- and Medium-Sized Enterprises

0

20

40

60

80

100

120

140

160

180

200

A s i a

C h i n a

L a t i n A m e r i c a



M i n i n g

O i l a n d g a s

2007

2013

2007

2013

2004

2013

Asia China EMEA Latin America

0

50

100

150

200

250

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

2004

2013

Asia EMEA Latin America

Sources Orbis Thomson Reuters Worldscope and IMF staff calculationsNote Total liabilities refer to total (nonequity) liabilities Mining includes oil and gas Panel 1 begins in 2007 to account for the relative scarcity of Chinese firms in thebeginning of the sample period a balanced sample is used to highlight t rends across larger firms The relative scarcity of data particularly in the first few years ofthe sample is the main reason Chinese patterns are not shown individually in the bottom panels The regional breakdown of the oil and gas subsector is alsoexcluded for similar reasons EMEA = Europe Middle East and Africa





positive foreign exchange exposures reflecting theirneed for imports Firms in tradable sectors suchas mining tend to have negative foreign exchangeexposures because exporting firms benefit froma depreciation of the local currency11 he evolu-tion of foreign exchange exposures after the globalfinancial crisis differs across regions Outside of Asiathe fraction of firms with positive foreign exchangeexposures increased across all sectors after the crisis

bull Interestingly the construction sector where leveragegrew rapidly is among the sectors perceived by stockmarkets in emerging market economies as havingstrongly increased their exposure to exchange ratefluctuations in recent years (Figure 37)

11Tese results are consistent with the literature (for exampleBodnar and Gentry 1993 Griffin and Stulz 2001)

Te data suggest a growing concentration of indebt-edness in the weaker tail of the corporate sector

Teshare of liabilities held by listed firms is split accord-ing to a measure of their solvency that is the interestcoverage ratio (ICR) (Figure 38) An ICR lower than

2 often means that a firm is in arrears on its interestpayments Note that the share of liabilities held byfirms with ICRs lower than 2 has grown during thepast decade and is now greater than the 2008 levelTe rise of corporate leverage amassed at the tail endof the distribution also raises concerns about China(Box 33)

Firm-Level Dynamics of Emerging Market Corporate

Leverage

Te empirical analysis focuses on the firm-level dynam-

ics of emerging market corporate leverage

Te corpo-rate finance literature (focusing mostly on advancedeconomies) has converged to a set of variables that areconsidered reliable drivers of corporate leverage firmsize collateral profitability and the market-to-bookratio Te literaturersquos selection of these variables can betraced to various corporate finance theories on depar-tures from the Modigliani-Miller irrelevance proposi-tion which holds that the specific proportions of debtand equity in a firmrsquos capital structure are irrelevant toits market value (Box 34) Building on these studiesthis chapter considers both domestic (firm-specific

and macroeconomic) factors and global economicand financial conditions as potential determinants ofcorporate leverage Te focus is on the change in theleverage ratio

Te rise of global factors

Te increase in emerging market corporate leverageappears to be closely associated with favorable globalconditions

Econometric analysis confirms that firm-and country-specific characteristics are key determi-nants of emerging market corporate leverage growththese terms have the expected signs and are statisti-

cally significant (Figure 39 panel 1) In particularprofitability tangibility and the measure of macro-economic conditions are positively correlated withleverage growth Tese positive relationships wouldimply that leverage should have declined given thedeterioration in these determinants in the postcrisisperiod discussed above (able 31) However thefact that the opposite happened suggests that global

Table 31 Worsening Emerging Market Firm-Leveland Macroeconomic Fundamentals(Percent unless otherwise noted)

Precrisis(2004ndash07)

Postcrisis(2010ndash13)

Firm-Level FundamentalsProfitability

Return on Assets 36 33Liquidity

Quick Ratio 09 10Solvency

Interest Coverage Ratio 34 28Asset Quality

Tangible Asset Ratio 305 229

Macroeconomic Fundamentals

Real GDP Growth 62 39CPI Inflation 48 39Short-Term Interest Rate 42 36Current Account Balance1 06 ndash09External Debt1 359 356Fiscal Balance1 ndash09 ndash28

Public Debt1 381 392

ICRG (macroeconomicfundamentals summary) Index2 387 382

Source IMF staff

Note Historical averages of median firm-level fundamentals reported for allcountries in the sample Interest coverage ratio is EBITDA (earnings beforeinterest taxes depreciation and amortization) to interest expenses thequick ratio is cash cash equivalents short-term investments and accountsreceivables to current liabilities the tangible asset ratio is the ratio of fixedassets (which include property plant and equipment) to total assets1Percent of GDP2The average of the International Country Risk Guide (ICRG) Economicand Financial Risk Ratings which aim to provide an overall assessment ofa countryrsquos economic situation and ability to finance its debt obligationsrespectively The ICRG index is fairly stable indicating that small changescan be meaningful the decline in the index between the two periods is about

one-half standard deviation





factors may be behind the rise in emerging marketcorporate leverage Precisely identifying the role ofindividual global factors is difficult however there-fore the analysis initially captures global economicand financial conditions using time dummiesmdashwhich

can be thought of as unobservable global factorsTe time dummies indeed suggest that global factorsare becoming more important as drivers of emergingmarket corporate leverage growth in the postcrisisperiod

When specific global factors are considered theinverse of the US shadow rate and to a lesserextent global oil prices seem to be particularly associ-ated with leverage growth

Tis result emerges whenincluding various global factors simultaneously inthe regression12 Further econometric analysis pointsto a greater role for global factors in particular the

shadow rate in the postcrisis rise of leverage Teirinfluence during the period was examined throughtwo complementary regression models Te firstexplicitly accounts for possible structural breaks andsuggests that the US shadow rate became a moresignificant postcrisis determinant of emerging marketleverage growth13 Te second model contrasts theprecrisis (2004ndash07) and postcrisis (2010ndash13) periodsand finds a significant positive postcrisis correlationbetween the shadow rate and no significant role forcountry-specific factors

Te role of easier global financial conditions iscorroborated through evidence on the relaxation offinancing constraints Te relevance of relaxed financ-ing constraints for leverage can be assessed by focus-ing on SMEs and weaker firms which typically havemore limited access to finance Similarly a closer lookcan be taken at sectors that are intrinsically moredependent on external finance (Rajan and Zingales

12In the baseline regression model the inverse of the US shadowrate and the change in global oil prices are the main global factorsTe results hold if the US shadow rate is replaced with the globalshadow rate Te results are also robust to the inclusion of otherglobal factors such as changes in the Chicago Board OptionsExchange Volatility Index (VIX) global commodity prices and

global GDP as well as other controls and to GDP weighting(Annex 31) Although robustness of these alternative specificationsis encouraging longer time series would be needed to make moredefinitive statements on the precise relationship between emergingmarket leverage growth and specific global factors

13Te analysis of a longer sample (1994ndash2013) of listed firmsreveals a positive and statistically significant correlation between theinverse shadow rate and emerging market leverage growth even aftercontrolling for other global factors Evidence based on this longersample also confirms the presence of a postcrisis structural break

1 Foreign Exchange Exposure by Sector 2001ndash14(Percent)

0

10

20

30

40

50

60

70


2 Share of Firms with Positive Foreign Exchange Exposureby Region(Percent)

0

10

20

30

40

50

60

70

80

Construction Manufacturing Mining Services

3 Share of Non-Asian Firms with Positive Foreign Exchange Exposure(Percent)

40

50

60

70

80

ndash04

ndash02

00

02

04

06

08

10

A g r i c u l t u r e


M i n i n g


R e t a i l

S e r v i c e s

T r a n s p o r t a t i o n

W h o l e s a l e

Share of firms with positive foreign

exchange exposure(left scale)

Median foreignexchange exposure(right scale)

2001ndash07

2010ndash14

2001ndash07

2010ndash14

Tradables Nontradables

Sources IMF Information Notice System Thomson Reuters Datastream ThomsonReuters Worldscope and IMF staff estimatesNote EMEA = Europe Middle East and Africa

Figure 36 Foreign Exchange Exposures in Emerging Market

Economies (Listed Firms)





1998) Evidence indicates that leverage for all thesetypes of firms is more responsive than for other firmsto prevailing global monetary conditions Moreoverin countries that have more open capital accountsand that received larger capital inflows firmsrsquo leveragegrowth tends to be more responsive to global finan-cial conditions

How have firms been using borrowed funds

Estimates based on listed firmsrsquo balance sheets suggestthat greater borrowing has been used more for netinvestment than for the accumulation of cash (Figure

310)14

Te results also suggest that in the postcri-

14 Although these estimates are indicative it is possible forexample that net investment in any one year may have beenfinanced with working capital or retained earnings (captured in theldquootherrdquo term) including from earlier years Te close associationbetween changes in leverage and investment are confirmed by firm-level investment equations As expected the level of leverage isnegatively associated with investment (see also IMF 2015d)

sis period financing availability has become moreimportant than profitability in driving investment Forexample during 2010ndash13 the relationship betweeninvestment and leverage strengthened but it weakenedfor cash flows and became statistically insignificant fora forward-looking measure of profitability (obinrsquos Q)Possibly the more favorable postcrisis global financialconditions relaxed financing constraints allowing moredebt-financed capital expenditure for less profitableprojects15

15 As in Magud and Sosa (2015) the classic Fazzari Hubbardand Petersen (1988) modelmdashwhich builds on the standard Q

theory of investmentmdashis augmented by a measure of leverageIn addition to leverage growth the other main determinants ofinvestment are obinrsquos Q (to capture marginal profitability andgrowth opportunities) cash flow measures (a proxy for financingconstraints) and the cost of capital A positive and statisticallysignificant cash flow coefficient suggests that firms face financialconstraints because they would need to rely on internal funds tofinance investment projects Estimates using the full and precrisis(2004ndash07) samples reveal that all variables are statistically significantand have the expected signs

Figure 37 Change in Foreign Exchange Exposures and Corporate Leverage by Sector(Percentage points)

1 All Firms

0

5

10

15

0 5 10 15

2 Mining

3 Construction

ndash15

ndash10

ndash5

0

5

10

15

ndash10 ndash5ndash15

ndash10

ndash5

ndash10 ndash5

ndash15

ndash10

ndash5


ndash10

ndash5

ndash10 ndash5

0 5 10 15 20

0

5

10

15

0 5 10 15

4 Manufacturing

0

5

10

15

0 5 10 15

Source IMF staff estimatesNote The vertical axes depict the changes (from 2001ndash07 to 2010ndash14) in estimated foreign exchange exposure and the horizontal axes depict the changes in theleverage ratio (total liabilities to market equity) for listed firms The slopes are statistically significant at least at the 5 percent level The results are robust to outliers andto other measures of leverage





Summary

Overall the relative role of global factors as key driv-ers of emerging market corporate leverage dynamicshas increased in recent years Te evidence showssome signs of elevated corporate exposure to a poten-tial worsening in global financial conditions Tebuildup in leverage in the construction sector andthe related rise in net foreign exchange exposure as well as growing concentrat ion of indebtedness in the weaker tail of the corporate sector provide particularreasons for concern However the growth in leverage

appears to have fostered investment although invest-ment projects may have become less profitable morerecently

Emerging Market Corporate Bond Finance

he growth in emerging market corporate leverage has

been accompanied by a change in its composition In

particular the importance of bond finance has grown

rapidly in recent years herefore this section examines

the role of firm country and global factors in explain-

ing patterns of bond issuance to help determine whether

the patterns are associated with rising vulnerabilities

Emerging market corporate bond issuance hasrisen sharply since 2009 becoming an increasinglyimportant source of corporate financing in thoseeconomies Starting from a low base the share ofcorporate finance accounted for by bonds has nearlydoubled since the crisis and totaled more than $900

billion in 2014 (Figure 311 panel 1) Likewise issu-ance via subsidiaries in offshore financial centers hasincreased significantly since the crisis driven primar-ily by borrowers headquartered in Brazil and China

0

10

20

30

40

50

60

7080

90

100

2004 05 06 07 08 09 10 11 12 13

ICR lt 1 1 le ICR lt 2 2 le ICR lt 3 3 le ICR

Sources Thomson Reuters Worldscope and IMF staff estimatesNote The figure shows the share of liabilities held by firms according to theirinterest coverage ratio (ICR) The ICR is a measure of firmsrsquo solvency calculated asthe ratio of earnings (before interest and taxes) to interest expenses

Figure 38 Corporate Liabilities and Solvency(Percent solvency measured using the ICR)

1 Determinants of Leverage Growth

2007 08 09 10 11 12 13

2 The Changing Relationship between Leverage and Global Factors(Percentage points)

3 Specific Determinants of Leverage Growth(Percentage points)

ndash10

ndash5

0

5

10

15

20

25Sales Profitability

Tangibility MacroeconomicconditionsShadow rate (inverse)Oil price

Sources Orbis and IMF staff calculationsNote Sample period 2004ndash13 An empty bar (panel 2) denotes that the timedummy is not statisticall y significant at the 10 percent level The standardizedcoefficients (panel 3) are statistically significant at the 1 percent level Firm-levelvariables are lagged sales and tangibility are changes See Annex 31 for further

details

Figure 39 Key Determinants of Emerging Market Economiesrsquo

Corporate Leverage

BaselineDeterminants

ExpectedSign

Firm Level Sales +ndash Profitability + Tangibility +Country Level Macroeconomic Conditions +Global Shadow Rate (inverse) + Oil Prices +

0

5

10

15

20

25

30

35

40

45

50





(McCauley Upper and Villar 2013 see also Shin2013 Avdjiev Chui and Shin 2014)16 Issuance ismost notable in the oil and gas sector (with a sizableforeign exchange component) and in constructionespecially since 201017 Although China has been animportant part of this development the uptrend inissuance is broad based across emerging markets Inparticular emerging markets other than China haveon average returned to the rapid pace of issuanceobserved before the global financial crisis Withincountries however the postcrisis growth in access hasnot been even One-third of emerging markets haveseen aggregate increases in the total amount issuedalongside declines in the total number of issuers o asignificant extent the growth in international bondissuance can be traced to the decline in cross-borderlending which in turn appears to be largely drivenby a retrenchment on the part of banks (Chapter 2 ofthe April 2015 GFSR)

A shift to bond financing has benefits and draw-backs from both firm and macroeconomic perspectives A key benefit of greater access to bond finance is that

16Te general trends discussed in this section are however robustto the use of alternative notions of nationality such as issuersrsquonationality of risk country of incorporation or ultimate parentnationality

17 Although currency mismatches are likely to be smaller in the oiland gas sector than in other sectors to the extent that export receiptsare denominated in dollars this sector is still vulnerable to oil pricedeclines (see for example BIS 2015)

it can provide financing to the real economy even when banks are distressed but it also exposes compa-nies to more volatile funding conditions Since bondfinancing is unsecured it does not entail the macro-economic amplification mechanisms associated with

collateral valuations (whereby an economic downturndepresses collateral values thus constraining borrow-ing capacity and investment even more [Kiyotaki andMoore 1997])18 Compared with cross-border banklending the participation by international investors inlocal markets can also have advantages in dampeningthe impact of global financial conditionsmdashfor exampleif foreign lenders want to withdraw part of the balanceof payments impact is cushioned by bond valuationeffects On the other hand bond financing tends to beassociated with weaker monitoring standards due to alarger pool of bond investors who may ldquochooserdquo not to

monitor the business activities of the bond issuers Tiscan create incentives for excessive risk-taking behav-ior by firms Moreover the growing intermediationthrough bond mutual funds can entail its own risks asextensively discussed in Chapter 3 of the April 2015GFSR

Te share of bond issuance denominated in euroshas grown appreciably in recent years (Figure 312) Although foreign currency issuance continues to bedominated by US dollar bonds the rise in eurodenominations likely reflects expectations of tighterUS monetary conditions and more accommodativemonetary policy by the European Central Bank andassociated exchange rate expectations For all emerg-ing markets the share of bonds issued in foreigncurrency has declined by more than 10 percentagepoints relative to the precrisis period However thatreading is mainly driven by the sharp rise in bondissuance by China which is predominantly in localcurrency Although firms in some emerging marketssuch as Colombia Malaysia the Philippines Russiaand Tailand have issued relatively more in localcurrency firms in many other emerging marketshave increased their bond financing in foreign cur-rency However tentative evidence indicates that

listed firms that have issued in foreign currency donot appear to have raised their foreign exchangeexposures possibly because of higher exports

18In line with this the effects of banking crises on the economyare found to be worse than in other types of crises (see CardarelliElekdag and Lall 2011 Giesecke and others 2014)

ndash20

0

20

40

60

80

100

120

Precrisis Postcrisis

Capital investment Changes in cash Other

Sources Thomson Reuters Worldscope and IMF staff calculationsNote ldquoOtherrdquo refers to other net assets and retained earnings All variables werenormalized by lagged total assets Firms with an increase in leverage above thefirst quartile of the leverage distribution were included

Figure 310 Leverage Cash Holdings and Corporate Investment(Percent contributions to the change in debt as a share of total assets)





increased hedging or a substitution of foreign cur-rency bank loans19

Te financial conditions of issuing firms appearto have broadly deteriorated in recent years Sincethe crisis bonds have been issued by more leveragedand less profitable firms on average (Figure 313)Indices of solvency (ICR) and liquidity (quick ratio)have also generally deteriorated among issuing

firms20 Since 2010 firms have used bond issuance

19Te correlation between foreign currency bond issuance and thechange in foreign exchange exposure is statistically insignificant inthe postcrisis period however the sample of firms considered wasrelatively small

20See Fuertes and Serena (2014) for a description of balancesheet trends in a broad range of emerging markets for firms tappinginternational bond markets

less for investment and more to refinance debt mostlikely to take advantage of the favorable financingconditions (see also Rodriacuteguez Bastos Kamil andSutton 2015)21 Indeed the share of issuers report-ing refinancing as their intended use of proceeds hasbeen rising

Emerging market firms have managed to issueat better terms (Figure 314) Average maturity at

issuance for domestic and external bonds has gener-ally lengthened by more than one year relative tothe precrisis average mitigating rollover risk for

21Te fact that firms report lower use of proceeds for investmentpurposes is not inconsistent with the information presented earlierthat more leverage had been associated with higher investment (forexample firms may have used proceeds to pay off bank debt whileincreasing their overall leverage and investment)

Figure 311 Bond Issuance by Regions and Sectors

1 Total Bond Issuance(Billions of US dollars)

0

5

10

15

20

25

30

00

02

04

06

08

10

12

14

2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14

2 Bond Issuance Concentration

0

50

100

150

200

250

300

350

400

2003ndash07 10ndash14 2003ndash07 10ndash14 2003ndash07 10ndash14 2003ndash07 10ndash14

3 Issuance by Region(Billions of US dollars yearly average)

0

100

200

300

400

500

600

700

800

900

1000

1990 92 94 96 98 2000 02 04 06 08 10 12 14

EMs excluding China

China

Local currency

Foreign currency

0

20

40

60

80

100

120

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

4 Issuance by Selected Sectors(Billions of US dollars yearly average)

Issuance by top 10 issuers (percent of total)

Herfindahl index (right scale)

Asia excludingChina

China EMEA Latin America

Local currency

Foreign currency

Metal andsteel

Mining Oil andgas

Construction Real estate Retail


Sources Dealogic and IMF staff calculations

Note Nationality is based on a firmrsquos country of risk The general trends in these charts are robust to alternative notions of na tionality such as issuerrsquos nationality ofincorporation or ultimate parent nationality A lower value of the Herfindahl index value indicates a lower degree of concentration EMs = emerging marketeconomies EMEA = Europe Middle East and Africa







0

200

400

600

800

1000

2000 02 04 06 08 10 12 14

0

1

2

3

4

5

6

2000 02 04 06 08 10 12 14

Figure 313 Deteriorating Firm-Specific Fundamentals for Bond-Issuing Firms

1 Profitability(Percent)

3 Interest Coverage Ratio(Percent)

5 Use of Proceeds Capital Expenditures(Percent of net fixed assets)

0

5

10

15

20

25

30

2003 05 07 09 11 13

02

03

04

05

06

07

08

09

2000 02 04 06 08 10 12 14

0

5

10

15

20

25

30

ndash04

01

06

11

16

2000 02 04 06 08 10 12 14

25

30

35

40

45

2000 02 04 06 08 10 12 14

2 Leverage(Percent)

6 Use of Proceeds Refinancing(Percent of responses)

4 Quick Ratio(Percent)

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Actual mean

Actual weighted mean

Index (percent of responses right scale)

Sources Bloomberg LP Dealogic and IMF staff calculat ionsNote Profitability is the return on assets Leverage is total debt to total assets Interest coverage ratio is EBITDA (earnings before interest taxes depreciation andamortization) to interest expenses Liquidity is measured by the quick ratio (cash cash equivalents short-term investments and receivables to current liabilities) All variables correspond to the year prior to issuance Nationality is based on the country of risk Listed and nonlisted firms are included (although coverage islimited for the latter) Panel 5 shows the actual capital expenditures in percent of net fixed assets on the year of issuance Index constructed based on intendeduse of proceeds as reported to Dealogic as percentage of total responses per year The index in panel 6 includes the categories ldquoRefinancingrdquo ldquoDebt repaymentrdquoand ldquoRestructuringrdquo Wgt mean = mean weighted by deal value





0

2

4

6

8

10

12

2000 02 04 06 08 10 12 14

Figure 314 Bond Issuance Yields and Maturity

1 Bond Yield to Maturity(Mean percent)

2 Bond Maturity at Issuance(Mean years)

EMs EMs excluding China

0

1

2

3

4

5

6

7


Sources Dealogic and IMF staff calculationsNote Precrisis 2003ndash07 crisis 2008ndash09 postcrisis 2010ndash14 Nationality is based on a firmrsquos country of risk These general trends are robust to alternative notionsof nationality such as issuerrsquos nationality of incorporation or ultimate parent nationality EMs = emerging market economies

Precrisis Crisis Postcrisis

ndash2

0

2

4

6

8

10

Size Profitability Leverage Seasonedissuer

dummy

Shadowrate

(inverse)

VIX

Figure 315 Factors Influencing the Probability of Bond Issuance

1 Sensitivity Analysis(Percentage points)

2 Change in the Probability of Issuance(Yearly average percentage points)

Before 2010

Since 2010

ndash02

0002

04

06

08

10

12

14

16

2004ndash07 2010ndash13

From changes in firm variables

From changes in global variables

Firm variables Global variables

Sources Bloomberg LP Thomson Reuters Worldscope and IMF staff calculationsNote The shaded bars denote statistical significance at least at the 5 percent level The probability of issuance is estimated using a pooled probit model with a timetrend and country and sector dummies Standard errors are clustered at the country level Nationality is based on firms country of risk The attribution analysisshown in panel 2 is computed using the coefficients of the pre- and postcrisis estimates and is not standard because of the nonlinear nature of the probit model Theanalysis decomposes the average yearly change in probability of issuance into that explained by changes in firm or global variables For each annual change allvariables are kept at their initial mean except firm- and global-level variables which are assigned their initial and end-period means to obtain their contributions Thepre- and postcrisis contributions are obtained by averaging yearly contributions for 2004ndash07 and 2010ndash13 respectively The calculation is done for nonseasonedissuers and for the median country and sector fixed ef fects A seasoned issuer is a firm that has issued before See Annex 32 VIX = Chicago Board Options Exchange

Volatility Index





rity26 In particular larger and less leveraged firmsfirms in countries with smaller government debt-to-GDP ratios and with depreciating exchange ratesand companies facing lower investor uncertainty(measured by the Chicago Board Options Exchange Volatility Index [VIX]) tend to issue at longer maturi-

ties27

Favorable global financial conditions have beena key determinant of the lengthening of maturity inthe postcrisis period Indeed in recent years accom-modative US monetary policy explains more of therecent lengthening in maturities than do firm char-acteristics (Figure 316)28 Moreover US shadowrate fluctuations have a greater impact on maturityfor external issuances and for non-investment-gradeissuances

26Fuertes and Serena (2014) and Shin (2014a) document alengthening in maturities for external bond issuances by nonfinancialcorporations and nonbank financial corporations in a broad range of

emerging markets27Te finding that maturities tend to be longer in countries

with larger government debt is in line with the idea that a largeliquid government bond market can have a positive effect on thedevelopment of corporate debt markets

28Feyen and others (2015) show that global factors have an impacton maturity structure of emerging market financial and nonfinancialcorporate bond issuance Te specification in this section is similarto theirs but it focuses only on nonfinancial firms and controls forfirm-level characteristics as is standard in the literature (Annex 32)

Summary

Global factors seem to have become relatively moreimportant determinants of bond issuance and maturityin the postcrisis period Emerging market corporatebond issuance has grown on a broad basis since 2009Te decline in the share of foreign currency issuance

in emerging markets reflects activity in China wherefirms have issued mostly in local currency Despite weaker domestic fundamentals emerging market firmshave managed to issue bonds with lower yields andlonger maturities

Emerging Market Corporate Spreads

Tis section examines changes in the balance betweendomestic and global factors in the behavior of emerg-ing market corporate spreads Extending the approachof the preceding sections it uses a price-based analy-

sis in which spreads are linked to firm-level country-level and global characteristics A novel feature ofthis analysis is the use of data on secondary marketspreads29

29Te literature on emerging market corporate spreads mainlyuses issuance-level launch yield data Te approach gives rise toendogeneity issues (Eichengreen and Mody 1998) because duringpoor market conditions when secondary spreads rise primary

Figure 316 Factors Influencing Bond Maturity

1 Bond Maturity at Issuance

00

02

04

06

08

1012

14

From changes infirm variables

From changes inmacroeconomic

variables

From changes inglobal variables

2 Changes in Maturity at Issuance 2009ndash13(Years)

Variable Expected Sign

Issuance in Local Currency ndash ndash

Investment Grade + +

Firm Size + +

Profitability + ndash

Leverage ndash ndash

Inverse Shadow Rate1 + +

VIX ndash ndash

Currency Depreciation ndash ndash

Size of Government Debt + +

Estimates

Sources Bloomberg LP and IMF staff calculationsNote The baseline specification estimates bond maturity at issuance as a function of bond firm macro and global factors with country and sector fixed effects anda time trend Firm factors include a measure of size (total assets) profitability (return on assets) and leverage (debt-to-assets) all at the year prior to issuance Bondfactors include dummies for bond currency denomination investment grade and put call and sink options Global factors are the VIX and the inverse shadow rate

(three-month average prior to issuance) interacted with a postcrisis dummy Macro factors include the government debt and exchange rate depreciation relative tothe US dollar Standard errors are clustered at the country level Nationality is based on country of risk Chinese firms are excluded VIX = Chicago Board OptionsExchange Volatility Index See Annex 32 and denote statistical significance at the 5 and 1 percent levels respectively 1Refers to the coefficient in the postcrisis period





In recent years emerging market corporate spreadshave been hovering above the average of the precrisisperiod (Figure 317) Te secondary-market corporate(Corporate Emerging Markets Bond Index [CEMBI])spreads move in unison with their sovereign counter-part (the Emerging Market Bond Index spread) andthe US BBB corporate spread (a gauge of global creditconditions) but inversely with the US policy rate (thefederal funds rate)30 More recently US corporate andCEMBI spreads have been diverging mainly becauseof relatively better US economic conditions corporate

spreads also differ across some regions

How has the relationship between spreads and

fundamentals changed over time

Regression analysis confirms that CEMBI spreadsare closely linked to country-specific and global fac-tors Cross-country panel regressions reveal a strongstatistical relationship between CEMBI spreadsleverage and macroeconomic factors (Figure 318)

spreads do not rise proportionately (and can indeed sometimesfall) a reflection of the tendency for only the most creditworthyborrowers to remain in the market Although Eichengreen and

Mody (1998) and other studies attempt to correct for the biasthe model can be unstable if not properly specified Only a fewstudies use secondary market data and then only with a limitedscope for instance Dittmar and Yuan (2008) and Zinna (2014)focus on the relationship between sovereign and corporatespreads

30Te secondary-market spreads are from JP Morganrsquos CEMBITe CEMBI tracks US dollar-denominated debt instruments issuedby emerging market firms the spread is calculated against the USreasury yield

Te behavior of emerging market corporate spreadsis also closely linked to the US corporate spread Although not reported similar results are foundusing individual-issuance-level data covering morethan 1000 issuances for 20 emerging markets from1990 to 2015

Te empirical analysis suggests that the relation-ship between corporate spreads and their determinantshas also changed with domestic factors becomingless influential in the postcrisis period For instancethe significantly positive precrisis correlation between

spreads and leverage broke down since 2010 Further-more the negative correlation between spreads andcountry-level factors has also declined in the postcri-sis period Tis breakdown suggests firms would berelatively more susceptible to a worsening in globalfinancial conditionsmdasha case in point is the 2013 ldquotapertantrumrdquo episode in which spreads for more leveragedfirms rose sharply (Box 36)

Policy Implications

Emerging markets should prepare for the eventual

reversal of postcrisis accommodative global financialconditions because those conditions have become moreinfluential determinants of emerging market corporatefinance Weaker firms and cyclical sectors such as con-struction are likely to be especially susceptible to suchglobal changes Once market access declines elevateddebt-servicing costs (resulting from the combination ofhigher interest rates and depreciating currencies) and

0

2

4

6

8

10

12

2003 05 07 09 11 13 15

Figure 317 Emerging Market Economies Secondary Market Corporate Spreads(Percent)

1 US Interest Rates and EM Spreads 2 EM Corporate Spreads by Region

0

5

10

15

20

25

2003 05 07 09 11 13 15

CEMBI Broad Federal funds rate

US BBB spread EMBI Global


Sources Bloomberg LP Federal Reserve Bank of St Louis FRED Economic Data and JP Morgan ChaseNote CEMBI = Corporate Emerging Markets Bond Index EM = emerging market economies EMBI = Emerging Markets Bond Index EMEA = EuropeMiddle East and Africa





rollover problems may hit some firms especially hardTerefore it is important to closely monitor sectorsand systemically important firms most exposed to risksand the sectors and large firms closely connected tothem including across the financial system and to pre-pare for contingencies Emerging markets should alsobe prepared for the eventuality of corporate failures where needed insolvency regimes should be reformedto enable rapid resolution of both failed and salvage-

able firms Tis section further discusses (1) measuresthat could be taken relatively quickly and that wouldhelp contain the further buildup of vulnerabilities ortheir impact although they would not eliminate these vulnerabilities in the short term (2) medium-termrecommendations and (3) actions to be taken in theevent of large capital outflows

Measures that could be taken now

Macroprudential measures could be used to limit risksfrom a further buildup of foreign exchange exposuresand leverage in emerging markets with latent vulner-

abilities Potential instruments include higher bankcapital requirements for corporate exposures as well asrisk weights and caps on the share of foreign currencyexposures on banksrsquo balance sheets Active provisioningand increasing equity capital can also bolster financialsystem resilience Where relevant loan-to-value anddebt-service-coverage ratios can be introduced to addressrisks related to commercial real estate31 Howeverrisks associated with market-based funding may provedifficult to manage Tis may require an even greateremphasis on macroprudential measures to enhance theresilience of banks and other important nonbank classesof intermediaries (IMF 2014d) For example securities

regulators should adopt a macroprudential orientationin their supervision of asset managers and the funds theymanage that have significant corporate bond exposures(see Chapter 3 of the April 2015 GFSR)

Microprudential and other tools can play a comple-mentary role Regulators can conduct bank stress testsrelated to foreign currency risks including derivativespositions Hedging foreign exchange exposures couldalso be more actively encouraged Nevertheless thehedges used by some corporations to limit their expo-sure risks may be compromised when most needed sothey should be assessed conservatively by regulators32

Financial turbulence in emerging markets could alsohave important implications for advanced economiesSome evidence indicates that if shocks from advancedeconomies generate financial volatility in emergingmarkets significant ldquospillbacksrdquo of that volatility tothe advanced economies could ensue in periods offinancial stress33 Such risks are particularly relevant forbanks mutual funds and other investors in advancedeconomies that have increased their emerging market

31However it should be recognized that corporate borrowers cansubstitute borrowing from unregulated financial institutions or incapital markets for domestic bank credit especially in emergingmarkets in which capital markets are well developed and globally

integrated32 As noted in Chui Fender and Sushko (2014) although

derivatives with ldquoknock-in knock-outrdquo features can insure againstmodest foreign exchange movements they leave the firm exposed tolarge losses if the domestic currency were to depreciate sharply

33Spillbacks are often underestimated because they tend to flowthrough channels that are inadequately tracked owing to theircomplexitymdashfor instance in the financial sector See 2014 Spillover

Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25

US BBB spread Shadow rate Macroeconomic Leverage

Before 2010 Since 2010

Global factors Domestic factors

Source IMF staff calculationsNote The figure is based on country-level panel regressions (see Annex 33 fordetails) The dependent variable is the CEMBI spreads for 20 emerging marketsover December 2001ndashDecember 2014 Explanatory variables include globalfactors (US BBB spread and the US shadow rate) as well as domestic factors(macroeconomic conditions [based on the International Country Risk Guide index]and leverage [median across firms]) The bars show the effects of a one standarddeviation increase in each variable on the CEMBI spread before 2010 and in thepostcrisis period (2010ndash14) These effects are calculated by multiplying theestimated coefficient of regression by the standard deviation of the correspondingindependent variable over all country-month observations Nonshaded bars arestatistically insignificant at the 5 percent level CEMBI = Corporate EmergingMarkets Bond Index

Figure 318 Emerging Market Economies Effects of Domestic

and Global Factors on Corporate Spreads(Percentage points)





Foreign exchange exposures are indirectly measuredusing stock returns Following a seminal paper by Adler

and Dumas (1984) the foreign exchange exposure offirm i is estimated as the value of βi in the followingaugmented capital asset pricing model (CAPM)

R it = αi + γ i R t M + βi R t

FX + εit

in which R it is firm i rsquos stock return R t M is the

market return and R t FX is the percentage change

in the trade-weighted nominal exchange rate (anincrease indicates an appreciation) A positive for-

eign exchange exposure means that the firmrsquos returnfalls when its local currency depreciates Te valueof βi can be interpreted as firm i rsquos foreign exchangeexposure net of financial and operational (ldquonaturalrdquo)hedging after accounting for market conditions(Bartram and Bodnar 2005) Te foreign exchangeexposures are estimated for about 5000 listed non-financial firms in 31 emerging market economiesover 2001ndash14

Box 32 Corporate Foreign Exchange Rate Exposures

Tis box was prepared by Machiko Narita

Corporate leverage is high in China China has reliedon investment to drive growth in recent years Te rapidincrease in investment has been financed by credit lead-ing to a sharp increase in corporate debt otal socialfinancing a measure of overall credit to the economyin China has risen dramatically (32 percentage pointsof GDP) since the global financial crisis1 Te credit-to-GDP ratio remains high and exceeds the level impliedby economic factors and cross-country comparisons2

External corporate debt has also risen albeit from alow level relative to GDP international reserves and

domestic credit Onshore banks have served as inter-mediaries for corporate borrowing overseas throughthe provision of bank guarantees and letters of creditChinese firms have also taken advantage of low globalinterest rates through offshore bond issuance whichhas increased substantially since 2010 Half of the debtissued abroad has been for operations in China Since2009 real estate developers have been the largest issu-ers of offshore bonds among nonfinancial firms

Te increase in corporate leverage is largely concentratedat the tail end of the distribution of firmsrsquo liabilities as

well as in state-owned enterprises (SOEs) and the real

Tis box was prepared by Raphael Lam1Te Bank for International Settlements ldquocredit gaprdquo measuredefined as the gap between the credit-to-GDP ratio relativeto its trend is used to assess whether credit is greater thanthe levels implied by fundamentals (see Arslanalp and othersforthcoming)

2Offshore issuance is generally conducted by an offshoreentity and as a result the borrowing is not captured by officialexternal debt statistics

estate sector (Chivakul and Lam 2015) otal liabili-ties of listed firms have risen dramatically and becomemore concentrated Although the median leverageratiomdashmeasured by the ratio of total liabilities to totalequitymdashhas largely stayed flat since 2006 leverage hassignificantly increased at the tail end (the 90th percen-tile) of the distribution of firms (see Figure 331) Inaddition highly leveraged firms account for a growingshare of total debt and liabilities in the corporate sector

Box 33 Corporate Leverage in China

0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs

Median private companies

90th percentile SOEs

90th percentile private companies

Figure 331 China Leverage Ratios(Percent)

Sources Wind Info Inc database and IMF staff estimatesNote SOE = state-owned enterprise





exposures warranting preparation for possible illiquid-ity in certain asset markets

Medium-term measures

In the medium term preventive policies could helpavert the buildup of excessive risks For exampleconsideration should be given to changes in the taxcode that remove fiscal incentives in favor of debt orthat encourage foreign currency debt34 Measures toreduce liquidity risks could be gradually phased in fordomestic open-end mutual funds holding debt andoffering daily redemptions (see Chapter 2 of this report

and Chapter 3 of the April 2015 GFSR) In additiongovernments can promote specific forms of financialdeepening such as development of a local investorbase (both banks and nonbanks) to help dampen

34Other policies that may encourage rapid leverage growthsuch as implicit or explicit government guarantees should also bereconsidered

global financial shocks Te move toward more flexibleexchange rates may enable emerging markets to adjustmore readily to shocks could facilitate an independentmonetary response to financial imbalances and maydiscourage banks and corporations from building uplarge foreign exchange exposures in the first place

Significant data gaps need to be addressed toenhance the effectiveness of surveillance Data gapsprevent a full assessment of the financial stability risksposed by corporate balance sheets from being madeFor instance firm-level data on foreign currencyexposures and the degree to which they are hedged are

generally unavailable Offshore bond issuance intro-duces another complication because the true externalexposure of firms with cross-border activities may notbe fully captured by using only residence-based statis-tics Renewed global efforts by authorities to collectand provide better information on foreign currencycorporate indebtedness and offsetting factors (such ashedges) are desirable (see IMF 2015b) Investing in

Across industries most of the buildup in leverage wasin the real estate and construction sector and to a lesser

extent in mining and utilities Across ownership typesSOEsmdashmainly local onesmdashaccount for a large shareof increased borrowing For instance in the real estateand construction sector only about 60 firms with highleverage ratios account for more than two-thirds of thesectorrsquos liabilities a rise of nearly three times over thedecade Tis elevated concentration of debt in the mostleveraged tail of the leverage distribution raises corporate

vulnerabilities to shocks

Te high level of credit could weigh on Chinarsquos growthand financial stability Te efficiency of the investmentfinanced by credit has been falling with a commensu-rate drop in corporate sector profitability Tis situa-

tion makes servicing debt obligations more difficult Inparticular the interest coverage ratio has fallen in SOEs which have contributed to the bulk of the rise in credit At the same time deleveraging by firms could weigh ongrowth while mounting corporate defaults would haveadverse effects on bank balance sheets and credit avail-ability and thereby further weaken growth

Te Chinese corporate sector is vulnerable to aslowdown in the real estate and construction sectorSensitivity analysis finds that although on average

firms can withstand a moderate 1 percent interest rateincrease SOEs appear to be relatively exposed to an

interest rate shock because of their low interest coverageand relatively higher leverage aking into account the value-added linkages of each sector to real estate andconstruction a severe slowdown in the real estate sector(a 20 percent profit decline) would have a significantimpact on the corporate sector including a drop in themedian interest coverage ratio to only 2frac12 times profits

with nearly 20 percent of firms in the real estate sector(accounting for 11 percent of total corporate debt) infinancial distress

In the future some debt write-offs would helpimprove credit flow and investment efficiency andreduce risks in China Write-offsmdashcombined with the

restructuring of viable companies and steps to facilitategreater tolerance of defaults exit and bankruptcy ofnonviable firmsmdashcould reduce the burden on banksand allow them to reallocate credit to more efficientsectors Banks can embark on rigorous quality assess-ments of their loan portfolios setting the stage foraddressing nonperforming loans and the potentialneed for bank recapitalization Continuing reforms topromote capital market development would help pro-

vide an alternative financing channel for healthy firms

Box 33 (continued)





reporting systems to help more effectively monitor thecorporate sectormdashincluding foreign currency expo-suresmdashis therefore warranted

Measures to address disruptive outflows

In the event of rapid capital outflows macroeco-nomic and financial sector policies can be deployed Worsening global financial conditions can induce

investors to reassess emerging market risks thereforethe likelihood of sudden outflows is considerablyhigher in the presence of latent corporate sector vulnerabilities In fact mounting emerging marketleverage has typically been associated with a subse-quent reversal of capital flows (for instance Men-doza and errones 2008 Elekdag and Wu 2011) Insuch a scenario nontradable sectors are likely to be

Tis box summarizes the theoretical and empirical litera-

ture on capital structure

Te capital structure of a firm is defined as the mixtureof debt and equity the firm uses to finance its opera-tions Te term is often used in conjunction with vari-ous measures of borrowing such as the debt-to-equityratio (one measure of the leverage ratio) In a seminalpaper Modigliani and Miller (1958) put forth the capi-tal structure irrelevance proposition the market value ofthe firm is independent of its capital structure

Departures from the Modigliani-Miller proposition

Subsequent research has shown that the Modigliani-Miller proposition fails under a variety of circumstanc-es1 Tis finding has led to three broad alternative

theories of firmsrsquo decisions on their capital structureTe first is the trade-off theory in which firms issue debtuntil the benefits (tax incentives) and costs (bankruptcy)of debt are balanced Te second is the pecking ordertheory (Myers and Majluf 1984) which governs theorder of financing sources and not the amount of debta firm issuesmdashfirms prefer to finance themselves first byusing internal funds then by issuing debt and last byissuing equity Te third is the market timing theoryin which managers are more likely to tap markets withthe most favorable conditions (for example during assetprice rallies)

Te role of business cycles

Another strand of the literature examines the aggregatedeterminants of corporate capital structure Empiricalpapers provide differing evidence regarding the cyclical-ity of leverage2 For example in Covas and Den Haan

Tis box was prepared by Ayumu Ken Kikkawa1Such as taxes transaction and bankruptcy costs agency con-

flicts adverse selection and time-varying market opportunitiesamong others (Frank and Goyal 2003 de Mooij 2012)

2Many papers have looked at how other aspects of businesscycles affect capital structures Beaudry Caglayan and Schian-

(2011) firm-level leverage is procyclical Fernaacutendez andGulan (2015) find that leverage is countercyclical for

emerging markets With regard to theory HackbarthMiao and Morellec (2006) argue that leverage is coun-tercyclical Kiyotaki and Moore (1997) argue that it isprocyclical and Bhamra Kuehu and Strebulaev (2010)argue that these opposing views are reconcilable

Te role of monetary conditions

Monetary policy can be transmitted to the nonfinancialcorporate sector through several channels and therebyinfluence firmsrsquo capital structure Te traditional interestrate channel stimulates aggregate demand by loweringinterest rates and thereby encouraging firms to borrowBarry and others (2008) find that firm leverage increases

when interest rates are low Based on a survey of chief

financial officers Graham and Harvey (2001) reportthat the level of interest rates is one of the most impor-tant factors influencing the decision to issue debt

In addition to the interest rate channel manypapers have investigated the credit channel (Bernanke2007) Te credit channel focuses on the change inthe availability of credit and has two dimensions (1)the balance sheet channel which focuses on bank loandemand and (2) the bank lending channel which ismore about the supply of bank loans (Kashyap Steinand Wilcox 1993) Bernanke Gertler and Gilchrist(1996) develop a model of the balance sheet chan-nel in which lower monetary policy rates raise equity

prices and a firmrsquos net worth and thereby lower thecost of external (debt) financing Tis generates a vir-tuous cycle (or financial accelerator) as firms use debtto finance investment which boosts aggregate demandand raises equity prices again allowing for even greaterdebt-financed investment

tarelli (2001) and Baum and others (2006) find that at times ofhigh macroeconomic volatility firmsrsquo investment and financingdecisions become more alike as uncertainty constrains managersrsquoability to make decisions based on firm-specific information

Box 34 Firm Capital Structure the Business Cycle and Monetary Policy







hit disproportionately o dampen adverse macro-economic consequences the policy response couldinclude if warranted exchange rate depreciation andthe use of monetary policy and reserves Te publicprovision of emergency foreign exchange hedging

facilities could also be considered Te combina-tion of policies would be based on macroeconomicconditions taking into consideration financial stabil-ity risks such as foreign exchange exposures Fiscalpolicy may need to be adjusted depending on mac-roeconomic circumstances and available policy spaceIf the financial system comes under stress liquidityprovision may be required

ConclusionTis chapter considers the evolving influence of firm-level country-level and global factors in driving lever-age patterns bond issuance and corporate spreadsTree key results emerge from the investigation

bull he relative contributions of firm- and country-specific characteristics in explaining leverage growthissuance and spreads seem to have diminished inrecent years In contrast global financial factorsappear to have become relatively more importantdeterminants in the postcrisis period

bull Leverage has risen more in sectors that are more vul-nerable to cyclical and financial conditions and it

Tis box investigates the impact of the ldquotaper tantrumrdquoon corporate spreads across emerging market econo-

mies On May 22 2013 during testimony to Congressthe chairman of the US Federal Reserve raised the pos-sibility of tapering its purchases of reasury and agencybonds Following this ldquotapering talkrdquo there were sharpcorrections in emerging market economiesrsquo asset pricesand a reversal of capital flows (Sahay and others 2015)

An event study is used to investigate how emerg-ing market corporate spreads reacted to the taperingshock Firm-level factors (leverage size profitabilityand growth prospects) are used to explain the changein corporate credit default swap (CDS) spreads threesix and eight days after May 21 Te analysis covers309 firms from 21 emerging markets

Borrowing costs increased disproportionately for

more leveraged and smaller firms following the taper-ing shock Moreover these effects tended to becomestronger over time as investors digested fundamen-tals and differentiated across emerging market firmsaccordingly (Figure 361) For example after eightdays a one standard deviation increase in the lever-age ratio (corresponding to 16 percentage points) isassociated with a 7 basis point increase (correspond-ing to an annualized rate of 33 percent) in the CDSspread Tese effects are substantial given that thefirms experienced an increase in spreads of 18 basispoints on average In other words a one standarddeviation increase in the leverage ratio of a firm

pushes up its borrowing cost by 40 percent relative toits average peer In sum the results suggest that when search-for-yield effects reverse firms with weaker fun-damentals may disproportionately suffer from greaterexposure to credit risk

Box 36 Taper Tantrum Did Firm-Level Factors Matter

ndash40

ndash30

ndash20

ndash10

0

10

3 days 6 days 8 days 3 days 6 days 8 days

Leverage (Log) sales

Figure 361 Effects of the Shock on Credit

Default Swap Spreads

(Basis points for one standard deviation increase)

Sources Bloomberg LP and IMF staff estimatesNote The shaded bars denote statistical significance atleast at the 10 percent level The explanatory variablesare leverage ratio (total debt to total assets) log salesincome-to-sales ratio and Tobins Q Country and sectorfixed effects are included

Tis box was prepared by Ayumu Ken Kikkawa





has grown most in construction Higher leverage hasalso been associated with on average rising foreigncurrency exposures

bull Despite weaker balance sheets emerging marketfirms have managed to issue at better terms (lower

yields longer maturities) on the positive side manyissuers have taken advantage of favorable financialconditions to refinance their debt

Te expanded role of global financial factors duringa period when they have been extraordinarily accom-modative means that emerging markets must preparefor the adverse domestic stability implications of globalfinancial tighteningbull Monitoring vulnerable and systemically impor-

tant firms as well as banks and other parts of theeconomy closely linked to them is crucial

bull Such expanded monitoring requires that collec-tion of data on corporate sector finances includingforeign currency exposures be improved

bull Macroprudential policies can be deployed to limitexcessive increases in corporate sector leverage Pos-sible tools include higher bank capital requirements(for instance implemented via risk weights) forcorporate foreign currency exposures and caps onthe share of such exposures on banksrsquo balance sheetsManaging risks associated with market-based fundingmay be challenging however potentially requiring aneven greater emphasis on macroprudential measuresto enhance the resilience of the financial system

bull Microprudential measures should also be consideredRegulators can conduct bank stress tests related toforeign currency risks

bull Finally as advanced economies normalize monetarypolicy emerging markets should prepare for anincrease in corporate failures and where neededshould reform corporate insolvency regimes

Annex 31 Emerging Market CorporateLeverage Data and Empirics

Tis annex discusses the data and the empirical meth-

odology used to analyze the main determinants ofemerging market corporate leverage Data sources anddefinitions are summarized in able 31135

Te author of this annex is Adrian Alter35Emerging market economies included in the analysis comprise

Argentina Bahrain Brazil Bulgaria Chile China Colombia CroatiaEgypt Hungary India Indonesia Jordan Kazakhstan Korea KuwaitLebanon Lithuania Malaysia Mauritius Mexico Morocco Nigeria

Measures of leverage

Leverage or financial leverage is the degree to which acompany uses debt Leverage is usually presented as aratio such as debt to capital Te broadest definitions of

leverage consider total nonequity liabilities An advan-tage of using total liabilities is that it implicitly recog-nizes that some firms can use trade credit as a means offinancing rather than purely for transactions (Rajan andZingales 1995) Another benefit of using total liabilitiesis its availability In contrast debt may not be reportedin larger data sets that include nonlisted firms

Data

Although firm-level databases contain an abundanceof information they do have limitations particularlyin the context of emerging market corporate leverage

For example data can vary greatly over the time periodcovered Accounting standards and reporting require-ments vary widely across countries so it is important touse databases with harmonized definitions Worldscope(Tomson Reuters) and Orbis (Bureau van Dijk) are twoexamples of such cross-country harmonized databases thatprovide annual firm-level balance sheet and income state-ment information Worldscope contains publicly listedfirms the main advantage of the Orbis database is its wide coverage of both listed and nonlisted firmsmdashinclud-ing SMEsmdashwhich enrich the cross-sectional informationin the data set o avoid double counting unconsolidated

accounts are considered36

Firm-level data are merged with country-specific indicators of macroeconomic condi-tions and global factors Te firm-country-global data setused comprises more than 1 million active nonfinancialfirms (with assets of more than $1 million) and 43million firm-year observations for 24 emerging marketeconomies during 2004ndash13

Methodology

Panel regressions link firm-level leverage growth withkey firm- and country-specific as well as global deter-minants For firm i in sector s country c at time t

Oman Pakistan Peru Philippines Poland Qatar Romania Rus-sia Saudi Arabia Serbia South Africa Sri Lanka Tailand urkeyUkraine United Arab Emirates and Venezuela

36Orbis has the advantage of being more comprehensive withmillions of firms represented in the database but more granularbalance sheet data can be incomplete For example debt is notreported for many emerging market firms in Orbis More detailedinformation on financial statements is even harder to come by





Annex Table 311 Definition of Variables

Variable Description Source

Firm-Level Variables

Leverage MetricsRatio of Liabilities to Book Equity Total liabilities divided by book equity Orbis Worldscope

Ratio of Liabilities to Book Assets Total liabilities divided by book assets Bloomberg LP Orbis WorldscopeRatio of Liabilities to Market Equity Total liabilities divided by market capitalization WorldscopeRatio of Liabilities to Market Assets Total liabilities divided by the sum of total liabilities and market capitalization WorldscopeRatio of Debt to Book Assets Total debt divided by book assets Orbis WorldscopeRat io of Debt to Market Assets Total debt divided by the sum of total l iabili ties and market capitalization WorldscopeRatio of Debt to EBIT Total debt divided by earnings before interest and taxes Orbis WorldscopeRatio of Debt to EBITDA Total debt divided by earnings before interest taxes depreciation and amortization Orbis Worldscope

Fundamental VariablesSales Total sales (Worldscope code WC01001) Orbis WorldscopeTobinrsquos Q Sum of market value of equity and book value of debt divided by book value of assets WorldscopeReturn on Assets Net income divided by total assets Bloomberg LP Orbis WorldscopeReturn on Equity Net income divided by shareholdersrsquo equity Orbis WorldscopeInterest Coverage Ratio Earnings before EBITDA or earnings before EBIT divided by interest expense Orbis WorldscopeTangibility Tangible fixed assets (or net PPE in Worldscope) divided by total assets Orbis Worldscope

Tradable and Nontradable SectorsTradable sectors agriculture mining and manufacturing nontradable sectors

construction transportation communications utilities wholesaleretail trade

servicesSeasoned Issuer Dummy Dummy equal to 1 if firm has issued a bond before a given year Bloomberg LP Dealogic

Firm Size DefinitionsSize Total assets in logs Bloomberg LP Orbis WorldscopeVery Large1 Operating revenue ge $130 million total assets ge $260 million employees ge 1000Large1 Operating revenue ge $13 million total assets ge $26 million employees ge 150Medium1 Operating revenue ge $13 million total assets ge $26 million employees ge 15Small Not included in any of the categories listed above

Bond-Level Variables

Local Currency Dummy equal to 1 if bond is denominated in country of riskrsquos local currency Bloomberg LP DealogicExternal Dummy equal to 1 if market type is not domestic DealogicInvestment Grade Dummy equal to 1 if rating is equal to or higher than BBB Bloomberg LPCallPutSink Dummy equal to 1 if maturity type includes callputsink option Bloomberg LP

Country-Level Variables

ICRG Economic and Financial RiskRating

The average of ICRG Economic and Financial Risk Ratings following Bekeart andothers (2014)

PRS Group

Corporate Spread JP Morgan CEMBI Broad Bloomberg LPRatio of Government Debt to GDP General government debt-to-GDP ratio WEOExchange Rate EM currency per US dollar WEOFinancial Openness Index The Chinn-Ito index (KAOPEN) is an index measuring a countryrsquos degree of capital

account opennesshttpwebpdxedu~itoChinn-

Ito_websitehtmFinancial Development Index Index that summarizes information regarding financial institutions (banks and non-

banks) and financial markets across three dimensions depth access and efficiencySahay and others (2015)

Financial Integration Total portfolio investment liabilit ies from an emerging market economy toward asubset of advanced economies (euro area Japan United Kingdom and UnitedStates) scaled by nominal GDP

CPIS

Exchange Rate Regime De facto exchange rate regime classification in which a higher value indicatesgreater exchange rate flexibility

Ilzetzki Reinhart and Rogoff(2008)

Global-Level Variables

VIX Chicago Board Options Exchange Market Volatility Index DatastreamUS BBB Spread Bank of America Merrill Lynch US Corporate BBB Option-Adjusted Spreadcopy FREDregUS Shadow Rate Estimated from a term-structure model (see Krippner 2014) RBNZ

US Real Shadow Rate The US shadow rate minus the approximately one-year-ahead US inflation forecast(Blue Chip Economic Indicators)

RBNZ Haver Analytics

US GDP Growth Annual average growth rate WEOGlobal Shadow Rate Principal component of the shadow rates of the euro area Japan and United States RBNZ and authorsrsquo calculationsCommodity Price Index Commodity price index WEOGlobal Real GDP Growth Global real GDP growth WEO

Source IMF staff

Note CEMBI = Corporate Emerging Markets Bond Index CPIS = Coordinated Portfolio Investment Survey EBIT = earnings before interest and taxes EBITDA = earnings beforeinterest taxes depreciation and amortization EM = emerging market economy EMBI = Emerging Markets Bond Index FRED = Federal Reserve Economic Data ICRG = InternationalCountry Risk Guide PPE = property plant and equipment RBNZ = Reserve Bank of New Zealand WEO =World Economic Outlook1At least one of the criteria is met







global factors and bank lending conditions A probitmodel is estimated with standard errors clustered at thecountry level with country and sector dummies as wellas a time trend Te baseline model is estimated usingthe Bloomberg LPndashTomson Reuters Worldscope

matched database described above Te full samplebegins in 1995 Te postcrisis estimation starts in 2010but the findings are robust to starting in 2009 For anadditional robustness check the exercise is repeatedusing the Dealogic-Orbis matched database alsodescribed above Te model takes the following form

Prob(Issuance it = 1) = F(α + β1 firmit ndash1 + β2macroit ndash1 + β3bank it ndash1 + β4 global it + εit )

in which Issuance a dummy variable is 1 if firm i issued at least once in a given year t

A wide range of macroeconomic (macro) and banklending (bank ) variables are considered includingrule of law index exchange rate regime real GDPgrowth per capita GDP ICRG political financial andeconomic indexes inflation inflation volatility cur-rent account and fiscal balances external public andcorporate debt exchange rate changes and domesticand cross-border bank claims to the private sectorHowever these variables are generally not statisticallysignificant

Firm ( firm) characteristics are generally robust acrosstime and databases considered

Global ( global ) factors included are the inverseshadow rate and the VIX A higher VIX reading isrelated to a lower probability of issuance over theentire sample

Bond Maturity at Issuance

Te analysis of bond maturity at issuance excludesChinese firms and includes bonds issued bothdomestically and externally Issuances are related tobond- and firm-level macroeconomic bank lendingand global variables Te model is estimated usingordinary least squares with standard errors clustered atthe country level and it includes country and sectordummies as well as a time trend Te model takes thefollowing form

Maturity i = α + β0bond i + β1 firmi + β2macroi + β3bank i + β4 global i + εi

in which Maturity is each bondrsquos maturity at issu-ance measured in years Bond characteristics (bond )

include dummies for local currency denominationinvestment grade and put call and sink optionsFirm-level variables ( firm) include size profitabilityleverage and a dummy for firms that have issued inthe past All bond and firm characteristics (except for

profitability) are significant with the expected sign Asabove a wide range of macroeconomic and bank-level variables are considered but are generally not statisti-cally significant

Global controls include the inverse shadow rate andthe VIX Bonds tend to be issued with shorter matu-rity in times of financial uncertainty (measured by the VIX) Te inverse shadow rate is not significant overthe entire sample but becomes strongly statisticallysignificant in the postcrisis period (defined as startingeither in 2009 or 2010) Te addition of interactionterms shows that the effect of the inverse shadow rate

on maturity was stronger for bonds issued in foreigncurrency and for non-investment-grade bonds

Annex 33 Regression Analysis ofDeterminants of Emerging Market CorporateSpreads

Tis annex describes the data and the country-levelregression model used to examine determinants ofemerging market corporate spreads

Te regression model takes the following form

spread it = αi + β1 global t + β2 domestic it

+ β3 post t + β4 post t global t + β5 post t domestic it + εit

in which spread denotes the corporate spread of emerg-ing market country i in month t Tis analysis uses sec-ondary market spread data which are not susceptibleto endogeneity of issuance decisions Te term global is a vector of a US corporate spread and real shadowrate Te term domestic is a vector of a macroeconomicfundamentals index (the ICRG risk rating) and aleverage indicator (debt-to-book assets the medianof firms within each country) Tese variables are de-meaned Te term post is a postcrisis dummy that takes

the value of one from January 2010 onward End-of-month market variables are used for 20 emergingmarkets the previous yearrsquos leverage is used

Te results are generally robust to using a globalreal shadow rate or the US one-year real reasury rateinstead of the US real shadow rate

Te author of this annex is Hibiki Ichiue





References

Acharya Viral Stephen Cecchetti Joseacute De Gregario Sebnem

Kalemli-Oumlzcan Philip R Lane and Ugo Panizza 2015

ldquoCorporate Debt in Emerging Economies A Treat to Finan-

cial Stabilityrdquo Committee on International Economic Policyand Reform Washington

Adler Michael and Bernard Dumas 1984 ldquoExposure to Cur-

rency Risk Definition and Measurementrdquo Financial Manage-

ment 13 41ndash50

Aizenman Joshua Mahir Binici and Michael M Hutchison

2014 ldquoTe ransmission of Federal Reserve apering News

to Emerging Financial Marketsrdquo NBER Working Paper

19980 National Bureau of Economic Research Cambridge

Massachusetts

Arslanalp Serkan Raphael Lam Wei Liao and Wojciech Mal-

iszewski Forthcoming ldquoDeleverage Chinardquo IMF Working

Paper International Monetary Fund Washington

Avdjiev Stefan Michael Chui and Hyun Song Shin 2014

ldquoNon-Financial Corporations from Emerging MarketEconomies and Capital Flowsrdquo BIS Quarterly Review

(December)

Ayala Diana Milan Nedeljkovic and Christian Saborowski

2015 ldquoWhat Slice of the Pie Te Corporate Bond Market

Boom in Emerging Economiesrdquo IMF Working Paper 15148

International Monetary Fund Washington

Bank for International Settlements (BIS) 2014a 84th Annual

Report Basel

mdashmdashmdash 2014b BIS Quarterly Review (September) Basel

mdashmdashmdash 2014c BIS Quarterly Review (December) Basel

mdashmdashmdash 2015 85th Annual Report Basel

Barry Christopher B Steven C Mann Vassil Mihov and

Mauricio Rodriguez 2008 ldquoCorporate Debt Issuance and theHistorical Level of Interest Ratesrdquo Financial Management 37

(3) 413ndash30

Bartram Soumlhnke M and Gordon Bodnar 2005 ldquoTe Exchange

Rate Exposure Puzzlerdquo MPRA Paper 6482 httpmpra

ubuni-muenchende6482

Baum Christopher F Mustafa Caglayan Neslihan Ozkan and

Oleksandr alavera 2006 ldquoTe Impact of Macroeconomic

Uncertainty on Non-Financial Firmsrsquo Demand for Liquidityrdquo

Review of Financial Economics 15 (4) 289ndash304

Beaudry Paul Mustafa Caglayan and Fabio Schiantarelli

2001 ldquoMonetary Instability the Predictability of Prices

and the Allocation of Investment An Empirical Investiga-

tion Using UK Panel Datardquo American Economic Review 91

648ndash62

Bekaert Geert Campbell Harvey Christian Lundblad and

Stephan Siegel 2014 ldquoPolitical Risk Spreadsrdquo Journal of

International Business Studies 45 (4) 471ndash93

Bernanke Ben S 2007 ldquoTe Financial Accelerator and the

Credit Channelrdquo Paper presented at Te Credit Channel of

Monetary Policy in the wenty-first Century Conference

Federal Reserve Bank of Atlanta Atlanta June 15

mdashmdashmdash Mark Gertler and Simon Gilchrist 1996 ldquoTe Finan-

cial Accelerator and the Flight to Qualityrdquo Review of Econom-

ics and Statistics 78 (1) 1ndash15

Bhamra Harjoat S Lars-Alexander Kuehu and Ilya A Strebulaev

2010 ldquoTe Aggregate Dynamics of Capital Structure and Mac-

roeconomic Riskrdquo Review of Financial Studies 23 4187ndash241Black Fischer 1995 ldquoInterest Rates as Optionsrdquo Journal of

Finance 50 (5) 1371ndash76

Bodnar Gordon M and William M Gentry 1993 ldquoExchange

Rate Exposure and Industry Characteristics Evidence from

Canada Japan and the USArdquo Journal of International Money

and Finance 12 (1) 29ndash45

Borensztein Eduardo Kevin Cowan Barry Eichengreen and

Ugo Panizza 2008 Bond Markets in Latin America On the

Verge of a Big Bang Cambridge Massachusetts Te MI

Press

Bruno Valentina and Hyun Song Shin 2015 ldquoCapital Flows

and the Risk-aking Channel of Monetary Policyrdquo Journal of

Monetary Economics 71 (C) 119ndash32

Bullard James 2012 ldquoShadow Interest Rates and the Stance ofUS Monetary Policyrdquo Presentation at the Center for Finance

and Accounting Research Annual Corporate Finance Confer-

ence Olin Business School Washington University St Louis

November 8

Cardarelli Roberto Selim Elekdag and Subir Lall 2011ldquoFinan-

cial Stress and Economic Contractionsrdquo Journal of Financial

Stability 7 (2) 78ndash97

Caruana Jaime 2012 ldquoInternational Monetary Policy Interac-

tions Challenges and Prospectsrdquo Speech at the CEMLA-

SEACEN Conference Punta del Este Uruguay November

16

Cesa-Bianchi Ambrogio Luis Felipe Ceacutespedes and Alessandro

Rebucci 2015 ldquoGlobal Liquidity House Prices and theMacroeconomy Evidence from Advanced and Emerging

Economiesrdquo BOE Working Paper 522 Bank of England

London

Chen Jiaqian ommaso Mancini-Griffoli and Ratna Sahay

2014 ldquoSpillovers from United States Monetary Policy on

Emerging Markets Different Tis imerdquo IMF Working

Paper 14240 International Monetary Fund Washington

Chen Qianying Andrew Filardo Dong He and Feng Zhu

2014 ldquoFinancial Crisis Unconventional Monetary Policy

and International Spilloversrdquo Working Paper 494 Bank for

International Settlements Basel

Chinn Menzie D and Hiro Ito 2006 ldquoWhat Matters for

Financial Development Capital Controls Institutions

and Interactionsrdquo Journal of Development Economics 81 (1)

163ndash92

Chivakul Mali and Raphael Lam 2015 ldquoAssessing Chinarsquos

Corporate Sector Vulnerabilitiesrdquo IMF Working Paper 1575


Chow Julian Forthcoming ldquoStress esting Corporate Balance

Sheets in Emerging Economiesrdquo IMF Working Paper Inter-

national Monetary Fund Washington







IMFPublications

bull Leading edge research meets innovative publishingbull Reaching a diverse community of millions worldwide

bull Keeping readers in touch with global economic

and financial issues

For the latest analysis on globaleconomy regional assessmentinequality and more

Visit imfbookstoreorggf105

I N T E R N A T I O N A L M O N E T A R Y F U N D

















60

90

120

150

180

210

2004 05 06 07 08 09 10 11 12 13





30

37

44

51

58

65

1994 96 98 2000 02 04 06 08 10 12



















0

500

1000

1500

2000

2500

3000

3500

2003 04 05 06 07 08 09 10 11 12 13 14


0

2

4

6

8

10

12

14

16

18

75

76

77

78

79

80

81

82

83

84

85

2003 04 05 06 07 08 09 10 11 12 13 14


0

2000

4000

6000

8000

10000

12000

14000

16000

1800020000

2003 04 05 06 07 08 09 10 11 12 13 14

Total bonds

Total loans



Bonds



























0

10

20

30

40

50

60

70

M a l a y s i a

T h a i l a n d


B r a z i l

M e x i c o

C o l o m b i a

I n d o n e s i a

K o r e a

R o m a n i a


C h i l e

B u l g a r i a

T u r k e y


calculations




















Box 31 Shadow Rates

ndash6

ndash4

ndash2

0

2

4

6

8

1995 97 99 2001 03 05 07 09 11 13 15



ndash10

ndash8

ndash6

ndash4

ndash2

0

2

4

6

8

10

1995 97 99 2001 03 05 07 09 11 13 15
























insolvency regimes






























1 Listed Firms

0

50

100

150

200

250

300



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s

0

20

40

60

80

100

120

140

160

180

A s i a

E M E A

E M E A

L a t i n

A m e r i c a

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

O i l a n d g a s


0

20

40

60

80

100

120

140

160

180

200

A s i a

C h i n a




M i n i n g

O i l a n d g a s

2007

2013

2007

2013

2004

2013


0

50

100

150

200

250

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

2004

2013














Leverage





















Source IMF staff

















0

10

20

30

40

50

60

70



0

10

20

30

40

50

60

70

80



40

50

60

70

80

ndash04

ndash02

00

02

04

06

08

10



M i n i n g


R e t a i l

S e r v i c e s


W h o l e s a l e




2001ndash07

2010ndash14

2001ndash07

2010ndash14












310)14







1 All Firms

0

5

10

15

0 5 10 15

2 Mining

3 Construction

ndash15

ndash10

ndash5

0

5

10

15


ndash10

ndash5

ndash10 ndash5

ndash15

ndash10

ndash5


ndash10

ndash5

ndash10 ndash5

0 5 10 15 20

0

5

10

15

0 5 10 15

4 Manufacturing

0

5

10

15

0 5 10 15






Summary













0

10

20

30

40

50

60

7080

90

100

2004 05 06 07 08 09 10 11 12 13





2007 08 09 10 11 12 13



ndash10

ndash5

0

5

10

15

20




details


Corporate Leverage


ExpectedSign


0

5

10

15

20

25

30

35

40

45

50















ndash20

0

20

40

60

80

100

120




















0

5

10

15

20

25

30

00

02

04

06

08

10

12

14

2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14


0

50

100

150

200

250

300

350

400



0

100

200

300

400

500

600

700

800

900

1000

1990 92 94 96 98 2000 02 04 06 08 10 12 14

EMs excluding China

China

Local currency

Foreign currency

0

20

40

60

80

100

120

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14




Asia excludingChina


Local currency

Foreign currency

Metal andsteel

Mining Oil andgas











0

200

400

600

800

1000

2000 02 04 06 08 10 12 14

0

1

2

3

4

5

6

2000 02 04 06 08 10 12 14





0

5

10

15

20

25

30

2003 05 07 09 11 13

02

03

04

05

06

07

08

09

2000 02 04 06 08 10 12 14

0

5

10

15

20

25

30

ndash04

01

06

11

16

2000 02 04 06 08 10 12 14

25

30

35

40

45

2000 02 04 06 08 10 12 14

2 Leverage(Percent)



Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Actual mean








0

2

4

6

8

10

12

2000 02 04 06 08 10 12 14





0

1

2

3

4

5

6

7




ndash2

0

2

4

6

8

10


dummy

Shadowrate

(inverse)

VIX




Before 2010

Since 2010

ndash02

0002

04

06

08

10

12

14

16






Volatility Index






ties27






Summary









00

02

04

06

08

1012

14



variables






Firm Size + +




VIX ndash ndash



Estimates


















Policy Implications



0

2

4

6

8

10

12

2003 05 07 09 11 13 15



0

5

10

15

20

25

2003 05 07 09 11 13 15





















Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications






















0

500

1000

1500

2000

2500

3000

3500

2003 04 05 06 07 08 09 10 11 12 13 14


0

2

4

6

8

10

12

14

16

18

75

76

77

78

79

80

81

82

83

84

85

2003 04 05 06 07 08 09 10 11 12 13 14


0

2000

4000

6000

8000

10000

12000

14000

16000

1800020000

2003 04 05 06 07 08 09 10 11 12 13 14

Total bonds

Total loans



Bonds



























0

10

20

30

40

50

60

70

M a l a y s i a

T h a i l a n d


B r a z i l

M e x i c o

C o l o m b i a

I n d o n e s i a

K o r e a

R o m a n i a


C h i l e

B u l g a r i a

T u r k e y


calculations




















Box 31 Shadow Rates

ndash6

ndash4

ndash2

0

2

4

6

8

1995 97 99 2001 03 05 07 09 11 13 15



ndash10

ndash8

ndash6

ndash4

ndash2

0

2

4

6

8

10

1995 97 99 2001 03 05 07 09 11 13 15
























insolvency regimes






























1 Listed Firms

0

50

100

150

200

250

300



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s

0

20

40

60

80

100

120

140

160

180

A s i a

E M E A

E M E A

L a t i n

A m e r i c a

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

O i l a n d g a s


0

20

40

60

80

100

120

140

160

180

200

A s i a

C h i n a




M i n i n g

O i l a n d g a s

2007

2013

2007

2013

2004

2013


0

50

100

150

200

250

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

2004

2013














Leverage





















Source IMF staff

















0

10

20

30

40

50

60

70



0

10

20

30

40

50

60

70

80



40

50

60

70

80

ndash04

ndash02

00

02

04

06

08

10



M i n i n g


R e t a i l

S e r v i c e s


W h o l e s a l e




2001ndash07

2010ndash14

2001ndash07

2010ndash14












310)14







1 All Firms

0

5

10

15

0 5 10 15

2 Mining

3 Construction

ndash15

ndash10

ndash5

0

5

10

15


ndash10

ndash5

ndash10 ndash5

ndash15

ndash10

ndash5


ndash10

ndash5

ndash10 ndash5

0 5 10 15 20

0

5

10

15

0 5 10 15

4 Manufacturing

0

5

10

15

0 5 10 15






Summary













0

10

20

30

40

50

60

7080

90

100

2004 05 06 07 08 09 10 11 12 13





2007 08 09 10 11 12 13



ndash10

ndash5

0

5

10

15

20




details


Corporate Leverage


ExpectedSign


0

5

10

15

20

25

30

35

40

45

50















ndash20

0

20

40

60

80

100

120




















0

5

10

15

20

25

30

00

02

04

06

08

10

12

14

2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14


0

50

100

150

200

250

300

350

400



0

100

200

300

400

500

600

700

800

900

1000

1990 92 94 96 98 2000 02 04 06 08 10 12 14

EMs excluding China

China

Local currency

Foreign currency

0

20

40

60

80

100

120

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14




Asia excludingChina


Local currency

Foreign currency

Metal andsteel

Mining Oil andgas











0

200

400

600

800

1000

2000 02 04 06 08 10 12 14

0

1

2

3

4

5

6

2000 02 04 06 08 10 12 14





0

5

10

15

20

25

30

2003 05 07 09 11 13

02

03

04

05

06

07

08

09

2000 02 04 06 08 10 12 14

0

5

10

15

20

25

30

ndash04

01

06

11

16

2000 02 04 06 08 10 12 14

25

30

35

40

45

2000 02 04 06 08 10 12 14

2 Leverage(Percent)



Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Actual mean








0

2

4

6

8

10

12

2000 02 04 06 08 10 12 14





0

1

2

3

4

5

6

7




ndash2

0

2

4

6

8

10


dummy

Shadowrate

(inverse)

VIX




Before 2010

Since 2010

ndash02

0002

04

06

08

10

12

14

16






Volatility Index






ties27






Summary









00

02

04

06

08

1012

14



variables






Firm Size + +




VIX ndash ndash



Estimates


















Policy Implications



0

2

4

6

8

10

12

2003 05 07 09 11 13 15



0

5

10

15

20

25

2003 05 07 09 11 13 15





















Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications






























0

10

20

30

40

50

60

70

M a l a y s i a

T h a i l a n d


B r a z i l

M e x i c o

C o l o m b i a

I n d o n e s i a

K o r e a

R o m a n i a


C h i l e

B u l g a r i a

T u r k e y


calculations




















Box 31 Shadow Rates

ndash6

ndash4

ndash2

0

2

4

6

8

1995 97 99 2001 03 05 07 09 11 13 15



ndash10

ndash8

ndash6

ndash4

ndash2

0

2

4

6

8

10

1995 97 99 2001 03 05 07 09 11 13 15
























insolvency regimes






























1 Listed Firms

0

50

100

150

200

250

300



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s

0

20

40

60

80

100

120

140

160

180

A s i a

E M E A

E M E A

L a t i n

A m e r i c a

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

O i l a n d g a s


0

20

40

60

80

100

120

140

160

180

200

A s i a

C h i n a




M i n i n g

O i l a n d g a s

2007

2013

2007

2013

2004

2013


0

50

100

150

200

250

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

2004

2013














Leverage





















Source IMF staff

















0

10

20

30

40

50

60

70



0

10

20

30

40

50

60

70

80



40

50

60

70

80

ndash04

ndash02

00

02

04

06

08

10



M i n i n g


R e t a i l

S e r v i c e s


W h o l e s a l e




2001ndash07

2010ndash14

2001ndash07

2010ndash14












310)14







1 All Firms

0

5

10

15

0 5 10 15

2 Mining

3 Construction

ndash15

ndash10

ndash5

0

5

10

15


ndash10

ndash5

ndash10 ndash5

ndash15

ndash10

ndash5


ndash10

ndash5

ndash10 ndash5

0 5 10 15 20

0

5

10

15

0 5 10 15

4 Manufacturing

0

5

10

15

0 5 10 15






Summary













0

10

20

30

40

50

60

7080

90

100

2004 05 06 07 08 09 10 11 12 13





2007 08 09 10 11 12 13



ndash10

ndash5

0

5

10

15

20




details


Corporate Leverage


ExpectedSign


0

5

10

15

20

25

30

35

40

45

50















ndash20

0

20

40

60

80

100

120




















0

5

10

15

20

25

30

00

02

04

06

08

10

12

14

2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14


0

50

100

150

200

250

300

350

400



0

100

200

300

400

500

600

700

800

900

1000

1990 92 94 96 98 2000 02 04 06 08 10 12 14

EMs excluding China

China

Local currency

Foreign currency

0

20

40

60

80

100

120

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14




Asia excludingChina


Local currency

Foreign currency

Metal andsteel

Mining Oil andgas











0

200

400

600

800

1000

2000 02 04 06 08 10 12 14

0

1

2

3

4

5

6

2000 02 04 06 08 10 12 14





0

5

10

15

20

25

30

2003 05 07 09 11 13

02

03

04

05

06

07

08

09

2000 02 04 06 08 10 12 14

0

5

10

15

20

25

30

ndash04

01

06

11

16

2000 02 04 06 08 10 12 14

25

30

35

40

45

2000 02 04 06 08 10 12 14

2 Leverage(Percent)



Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Actual mean








0

2

4

6

8

10

12

2000 02 04 06 08 10 12 14





0

1

2

3

4

5

6

7




ndash2

0

2

4

6

8

10


dummy

Shadowrate

(inverse)

VIX




Before 2010

Since 2010

ndash02

0002

04

06

08

10

12

14

16






Volatility Index






ties27






Summary









00

02

04

06

08

1012

14



variables






Firm Size + +




VIX ndash ndash



Estimates


















Policy Implications



0

2

4

6

8

10

12

2003 05 07 09 11 13 15



0

5

10

15

20

25

2003 05 07 09 11 13 15





















Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications


























Box 31 Shadow Rates

ndash6

ndash4

ndash2

0

2

4

6

8

1995 97 99 2001 03 05 07 09 11 13 15



ndash10

ndash8

ndash6

ndash4

ndash2

0

2

4

6

8

10

1995 97 99 2001 03 05 07 09 11 13 15
























insolvency regimes






























1 Listed Firms

0

50

100

150

200

250

300



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s

0

20

40

60

80

100

120

140

160

180

A s i a

E M E A

E M E A

L a t i n

A m e r i c a

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

O i l a n d g a s


0

20

40

60

80

100

120

140

160

180

200

A s i a

C h i n a




M i n i n g

O i l a n d g a s

2007

2013

2007

2013

2004

2013


0

50

100

150

200

250

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

2004

2013














Leverage





















Source IMF staff

















0

10

20

30

40

50

60

70



0

10

20

30

40

50

60

70

80



40

50

60

70

80

ndash04

ndash02

00

02

04

06

08

10



M i n i n g


R e t a i l

S e r v i c e s


W h o l e s a l e




2001ndash07

2010ndash14

2001ndash07

2010ndash14












310)14







1 All Firms

0

5

10

15

0 5 10 15

2 Mining

3 Construction

ndash15

ndash10

ndash5

0

5

10

15


ndash10

ndash5

ndash10 ndash5

ndash15

ndash10

ndash5


ndash10

ndash5

ndash10 ndash5

0 5 10 15 20

0

5

10

15

0 5 10 15

4 Manufacturing

0

5

10

15

0 5 10 15






Summary













0

10

20

30

40

50

60

7080

90

100

2004 05 06 07 08 09 10 11 12 13





2007 08 09 10 11 12 13



ndash10

ndash5

0

5

10

15

20




details


Corporate Leverage


ExpectedSign


0

5

10

15

20

25

30

35

40

45

50















ndash20

0

20

40

60

80

100

120




















0

5

10

15

20

25

30

00

02

04

06

08

10

12

14

2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14


0

50

100

150

200

250

300

350

400



0

100

200

300

400

500

600

700

800

900

1000

1990 92 94 96 98 2000 02 04 06 08 10 12 14

EMs excluding China

China

Local currency

Foreign currency

0

20

40

60

80

100

120

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14




Asia excludingChina


Local currency

Foreign currency

Metal andsteel

Mining Oil andgas











0

200

400

600

800

1000

2000 02 04 06 08 10 12 14

0

1

2

3

4

5

6

2000 02 04 06 08 10 12 14





0

5

10

15

20

25

30

2003 05 07 09 11 13

02

03

04

05

06

07

08

09

2000 02 04 06 08 10 12 14

0

5

10

15

20

25

30

ndash04

01

06

11

16

2000 02 04 06 08 10 12 14

25

30

35

40

45

2000 02 04 06 08 10 12 14

2 Leverage(Percent)



Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Actual mean








0

2

4

6

8

10

12

2000 02 04 06 08 10 12 14





0

1

2

3

4

5

6

7




ndash2

0

2

4

6

8

10


dummy

Shadowrate

(inverse)

VIX




Before 2010

Since 2010

ndash02

0002

04

06

08

10

12

14

16






Volatility Index






ties27






Summary









00

02

04

06

08

1012

14



variables






Firm Size + +




VIX ndash ndash



Estimates


















Policy Implications



0

2

4

6

8

10

12

2003 05 07 09 11 13 15



0

5

10

15

20

25

2003 05 07 09 11 13 15





















Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications





























insolvency regimes






























1 Listed Firms

0

50

100

150

200

250

300



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s

0

20

40

60

80

100

120

140

160

180

A s i a

E M E A

E M E A

L a t i n

A m e r i c a

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

O i l a n d g a s


0

20

40

60

80

100

120

140

160

180

200

A s i a

C h i n a




M i n i n g

O i l a n d g a s

2007

2013

2007

2013

2004

2013


0

50

100

150

200

250

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

2004

2013














Leverage





















Source IMF staff

















0

10

20

30

40

50

60

70



0

10

20

30

40

50

60

70

80



40

50

60

70

80

ndash04

ndash02

00

02

04

06

08

10



M i n i n g


R e t a i l

S e r v i c e s


W h o l e s a l e




2001ndash07

2010ndash14

2001ndash07

2010ndash14












310)14







1 All Firms

0

5

10

15

0 5 10 15

2 Mining

3 Construction

ndash15

ndash10

ndash5

0

5

10

15


ndash10

ndash5

ndash10 ndash5

ndash15

ndash10

ndash5


ndash10

ndash5

ndash10 ndash5

0 5 10 15 20

0

5

10

15

0 5 10 15

4 Manufacturing

0

5

10

15

0 5 10 15






Summary













0

10

20

30

40

50

60

7080

90

100

2004 05 06 07 08 09 10 11 12 13





2007 08 09 10 11 12 13



ndash10

ndash5

0

5

10

15

20




details


Corporate Leverage


ExpectedSign


0

5

10

15

20

25

30

35

40

45

50















ndash20

0

20

40

60

80

100

120




















0

5

10

15

20

25

30

00

02

04

06

08

10

12

14

2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14


0

50

100

150

200

250

300

350

400



0

100

200

300

400

500

600

700

800

900

1000

1990 92 94 96 98 2000 02 04 06 08 10 12 14

EMs excluding China

China

Local currency

Foreign currency

0

20

40

60

80

100

120

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14




Asia excludingChina


Local currency

Foreign currency

Metal andsteel

Mining Oil andgas











0

200

400

600

800

1000

2000 02 04 06 08 10 12 14

0

1

2

3

4

5

6

2000 02 04 06 08 10 12 14





0

5

10

15

20

25

30

2003 05 07 09 11 13

02

03

04

05

06

07

08

09

2000 02 04 06 08 10 12 14

0

5

10

15

20

25

30

ndash04

01

06

11

16

2000 02 04 06 08 10 12 14

25

30

35

40

45

2000 02 04 06 08 10 12 14

2 Leverage(Percent)



Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Actual mean








0

2

4

6

8

10

12

2000 02 04 06 08 10 12 14





0

1

2

3

4

5

6

7




ndash2

0

2

4

6

8

10


dummy

Shadowrate

(inverse)

VIX




Before 2010

Since 2010

ndash02

0002

04

06

08

10

12

14

16






Volatility Index






ties27






Summary









00

02

04

06

08

1012

14



variables






Firm Size + +




VIX ndash ndash



Estimates


















Policy Implications



0

2

4

6

8

10

12

2003 05 07 09 11 13 15



0

5

10

15

20

25

2003 05 07 09 11 13 15





















Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications





















1 Listed Firms

0

50

100

150

200

250

300



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s



M i n i n g

O i l a n d g a s

0

20

40

60

80

100

120

140

160

180

A s i a

E M E A

E M E A

L a t i n

A m e r i c a

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

O i l a n d g a s


0

20

40

60

80

100

120

140

160

180

200

A s i a

C h i n a




M i n i n g

O i l a n d g a s

2007

2013

2007

2013

2004

2013


0

50

100

150

200

250

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

C o n

s t r u c t i o n

M a n u

f a c t u r i n g

M i n i n g

2004

2013














Leverage





















Source IMF staff

















0

10

20

30

40

50

60

70



0

10

20

30

40

50

60

70

80



40

50

60

70

80

ndash04

ndash02

00

02

04

06

08

10



M i n i n g


R e t a i l

S e r v i c e s


W h o l e s a l e




2001ndash07

2010ndash14

2001ndash07

2010ndash14












310)14







1 All Firms

0

5

10

15

0 5 10 15

2 Mining

3 Construction

ndash15

ndash10

ndash5

0

5

10

15


ndash10

ndash5

ndash10 ndash5

ndash15

ndash10

ndash5


ndash10

ndash5

ndash10 ndash5

0 5 10 15 20

0

5

10

15

0 5 10 15

4 Manufacturing

0

5

10

15

0 5 10 15






Summary













0

10

20

30

40

50

60

7080

90

100

2004 05 06 07 08 09 10 11 12 13





2007 08 09 10 11 12 13



ndash10

ndash5

0

5

10

15

20




details


Corporate Leverage


ExpectedSign


0

5

10

15

20

25

30

35

40

45

50















ndash20

0

20

40

60

80

100

120




















0

5

10

15

20

25

30

00

02

04

06

08

10

12

14

2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14


0

50

100

150

200

250

300

350

400



0

100

200

300

400

500

600

700

800

900

1000

1990 92 94 96 98 2000 02 04 06 08 10 12 14

EMs excluding China

China

Local currency

Foreign currency

0

20

40

60

80

100

120

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14




Asia excludingChina


Local currency

Foreign currency

Metal andsteel

Mining Oil andgas











0

200

400

600

800

1000

2000 02 04 06 08 10 12 14

0

1

2

3

4

5

6

2000 02 04 06 08 10 12 14





0

5

10

15

20

25

30

2003 05 07 09 11 13

02

03

04

05

06

07

08

09

2000 02 04 06 08 10 12 14

0

5

10

15

20

25

30

ndash04

01

06

11

16

2000 02 04 06 08 10 12 14

25

30

35

40

45

2000 02 04 06 08 10 12 14

2 Leverage(Percent)



Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Actual mean








0

2

4

6

8

10

12

2000 02 04 06 08 10 12 14





0

1

2

3

4

5

6

7




ndash2

0

2

4

6

8

10


dummy

Shadowrate

(inverse)

VIX




Before 2010

Since 2010

ndash02

0002

04

06

08

10

12

14

16






Volatility Index






ties27






Summary









00

02

04

06

08

1012

14



variables






Firm Size + +




VIX ndash ndash



Estimates


















Policy Implications



0

2

4

6

8

10

12

2003 05 07 09 11 13 15



0

5

10

15

20

25

2003 05 07 09 11 13 15





















Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications




















Leverage





















Source IMF staff

















0

10

20

30

40

50

60

70



0

10

20

30

40

50

60

70

80



40

50

60

70

80

ndash04

ndash02

00

02

04

06

08

10



M i n i n g


R e t a i l

S e r v i c e s


W h o l e s a l e




2001ndash07

2010ndash14

2001ndash07

2010ndash14












310)14







1 All Firms

0

5

10

15

0 5 10 15

2 Mining

3 Construction

ndash15

ndash10

ndash5

0

5

10

15


ndash10

ndash5

ndash10 ndash5

ndash15

ndash10

ndash5


ndash10

ndash5

ndash10 ndash5

0 5 10 15 20

0

5

10

15

0 5 10 15

4 Manufacturing

0

5

10

15

0 5 10 15






Summary













0

10

20

30

40

50

60

7080

90

100

2004 05 06 07 08 09 10 11 12 13





2007 08 09 10 11 12 13



ndash10

ndash5

0

5

10

15

20




details


Corporate Leverage


ExpectedSign


0

5

10

15

20

25

30

35

40

45

50















ndash20

0

20

40

60

80

100

120




















0

5

10

15

20

25

30

00

02

04

06

08

10

12

14

2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14


0

50

100

150

200

250

300

350

400



0

100

200

300

400

500

600

700

800

900

1000

1990 92 94 96 98 2000 02 04 06 08 10 12 14

EMs excluding China

China

Local currency

Foreign currency

0

20

40

60

80

100

120

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14




Asia excludingChina


Local currency

Foreign currency

Metal andsteel

Mining Oil andgas











0

200

400

600

800

1000

2000 02 04 06 08 10 12 14

0

1

2

3

4

5

6

2000 02 04 06 08 10 12 14





0

5

10

15

20

25

30

2003 05 07 09 11 13

02

03

04

05

06

07

08

09

2000 02 04 06 08 10 12 14

0

5

10

15

20

25

30

ndash04

01

06

11

16

2000 02 04 06 08 10 12 14

25

30

35

40

45

2000 02 04 06 08 10 12 14

2 Leverage(Percent)



Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Actual mean








0

2

4

6

8

10

12

2000 02 04 06 08 10 12 14





0

1

2

3

4

5

6

7




ndash2

0

2

4

6

8

10


dummy

Shadowrate

(inverse)

VIX




Before 2010

Since 2010

ndash02

0002

04

06

08

10

12

14

16






Volatility Index






ties27






Summary









00

02

04

06

08

1012

14



variables






Firm Size + +




VIX ndash ndash



Estimates


















Policy Implications



0

2

4

6

8

10

12

2003 05 07 09 11 13 15



0

5

10

15

20

25

2003 05 07 09 11 13 15





















Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications























0

10

20

30

40

50

60

70



0

10

20

30

40

50

60

70

80



40

50

60

70

80

ndash04

ndash02

00

02

04

06

08

10



M i n i n g


R e t a i l

S e r v i c e s


W h o l e s a l e




2001ndash07

2010ndash14

2001ndash07

2010ndash14












310)14







1 All Firms

0

5

10

15

0 5 10 15

2 Mining

3 Construction

ndash15

ndash10

ndash5

0

5

10

15


ndash10

ndash5

ndash10 ndash5

ndash15

ndash10

ndash5


ndash10

ndash5

ndash10 ndash5

0 5 10 15 20

0

5

10

15

0 5 10 15

4 Manufacturing

0

5

10

15

0 5 10 15






Summary













0

10

20

30

40

50

60

7080

90

100

2004 05 06 07 08 09 10 11 12 13





2007 08 09 10 11 12 13



ndash10

ndash5

0

5

10

15

20




details


Corporate Leverage


ExpectedSign


0

5

10

15

20

25

30

35

40

45

50















ndash20

0

20

40

60

80

100

120




















0

5

10

15

20

25

30

00

02

04

06

08

10

12

14

2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14


0

50

100

150

200

250

300

350

400



0

100

200

300

400

500

600

700

800

900

1000

1990 92 94 96 98 2000 02 04 06 08 10 12 14

EMs excluding China

China

Local currency

Foreign currency

0

20

40

60

80

100

120

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14




Asia excludingChina


Local currency

Foreign currency

Metal andsteel

Mining Oil andgas











0

200

400

600

800

1000

2000 02 04 06 08 10 12 14

0

1

2

3

4

5

6

2000 02 04 06 08 10 12 14





0

5

10

15

20

25

30

2003 05 07 09 11 13

02

03

04

05

06

07

08

09

2000 02 04 06 08 10 12 14

0

5

10

15

20

25

30

ndash04

01

06

11

16

2000 02 04 06 08 10 12 14

25

30

35

40

45

2000 02 04 06 08 10 12 14

2 Leverage(Percent)



Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Actual mean








0

2

4

6

8

10

12

2000 02 04 06 08 10 12 14





0

1

2

3

4

5

6

7




ndash2

0

2

4

6

8

10


dummy

Shadowrate

(inverse)

VIX




Before 2010

Since 2010

ndash02

0002

04

06

08

10

12

14

16






Volatility Index






ties27






Summary









00

02

04

06

08

1012

14



variables






Firm Size + +




VIX ndash ndash



Estimates


















Policy Implications



0

2

4

6

8

10

12

2003 05 07 09 11 13 15



0

5

10

15

20

25

2003 05 07 09 11 13 15





















Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications
















310)14







1 All Firms

0

5

10

15

0 5 10 15

2 Mining

3 Construction

ndash15

ndash10

ndash5

0

5

10

15


ndash10

ndash5

ndash10 ndash5

ndash15

ndash10

ndash5


ndash10

ndash5

ndash10 ndash5

0 5 10 15 20

0

5

10

15

0 5 10 15

4 Manufacturing

0

5

10

15

0 5 10 15






Summary













0

10

20

30

40

50

60

7080

90

100

2004 05 06 07 08 09 10 11 12 13





2007 08 09 10 11 12 13



ndash10

ndash5

0

5

10

15

20




details


Corporate Leverage


ExpectedSign


0

5

10

15

20

25

30

35

40

45

50















ndash20

0

20

40

60

80

100

120




















0

5

10

15

20

25

30

00

02

04

06

08

10

12

14

2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14


0

50

100

150

200

250

300

350

400



0

100

200

300

400

500

600

700

800

900

1000

1990 92 94 96 98 2000 02 04 06 08 10 12 14

EMs excluding China

China

Local currency

Foreign currency

0

20

40

60

80

100

120

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14




Asia excludingChina


Local currency

Foreign currency

Metal andsteel

Mining Oil andgas











0

200

400

600

800

1000

2000 02 04 06 08 10 12 14

0

1

2

3

4

5

6

2000 02 04 06 08 10 12 14





0

5

10

15

20

25

30

2003 05 07 09 11 13

02

03

04

05

06

07

08

09

2000 02 04 06 08 10 12 14

0

5

10

15

20

25

30

ndash04

01

06

11

16

2000 02 04 06 08 10 12 14

25

30

35

40

45

2000 02 04 06 08 10 12 14

2 Leverage(Percent)



Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Actual mean








0

2

4

6

8

10

12

2000 02 04 06 08 10 12 14





0

1

2

3

4

5

6

7




ndash2

0

2

4

6

8

10


dummy

Shadowrate

(inverse)

VIX




Before 2010

Since 2010

ndash02

0002

04

06

08

10

12

14

16






Volatility Index






ties27






Summary









00

02

04

06

08

1012

14



variables






Firm Size + +




VIX ndash ndash



Estimates


















Policy Implications



0

2

4

6

8

10

12

2003 05 07 09 11 13 15



0

5

10

15

20

25

2003 05 07 09 11 13 15





















Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications













Summary













0

10

20

30

40

50

60

7080

90

100

2004 05 06 07 08 09 10 11 12 13





2007 08 09 10 11 12 13



ndash10

ndash5

0

5

10

15

20




details


Corporate Leverage


ExpectedSign


0

5

10

15

20

25

30

35

40

45

50















ndash20

0

20

40

60

80

100

120




















0

5

10

15

20

25

30

00

02

04

06

08

10

12

14

2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14


0

50

100

150

200

250

300

350

400



0

100

200

300

400

500

600

700

800

900

1000

1990 92 94 96 98 2000 02 04 06 08 10 12 14

EMs excluding China

China

Local currency

Foreign currency

0

20

40

60

80

100

120

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14




Asia excludingChina


Local currency

Foreign currency

Metal andsteel

Mining Oil andgas











0

200

400

600

800

1000

2000 02 04 06 08 10 12 14

0

1

2

3

4

5

6

2000 02 04 06 08 10 12 14





0

5

10

15

20

25

30

2003 05 07 09 11 13

02

03

04

05

06

07

08

09

2000 02 04 06 08 10 12 14

0

5

10

15

20

25

30

ndash04

01

06

11

16

2000 02 04 06 08 10 12 14

25

30

35

40

45

2000 02 04 06 08 10 12 14

2 Leverage(Percent)



Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Actual mean








0

2

4

6

8

10

12

2000 02 04 06 08 10 12 14





0

1

2

3

4

5

6

7




ndash2

0

2

4

6

8

10


dummy

Shadowrate

(inverse)

VIX




Before 2010

Since 2010

ndash02

0002

04

06

08

10

12

14

16






Volatility Index






ties27






Summary









00

02

04

06

08

1012

14



variables






Firm Size + +




VIX ndash ndash



Estimates


















Policy Implications



0

2

4

6

8

10

12

2003 05 07 09 11 13 15



0

5

10

15

20

25

2003 05 07 09 11 13 15





















Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications























ndash20

0

20

40

60

80

100

120




















0

5

10

15

20

25

30

00

02

04

06

08

10

12

14

2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14


0

50

100

150

200

250

300

350

400



0

100

200

300

400

500

600

700

800

900

1000

1990 92 94 96 98 2000 02 04 06 08 10 12 14

EMs excluding China

China

Local currency

Foreign currency

0

20

40

60

80

100

120

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14




Asia excludingChina


Local currency

Foreign currency

Metal andsteel

Mining Oil andgas











0

200

400

600

800

1000

2000 02 04 06 08 10 12 14

0

1

2

3

4

5

6

2000 02 04 06 08 10 12 14





0

5

10

15

20

25

30

2003 05 07 09 11 13

02

03

04

05

06

07

08

09

2000 02 04 06 08 10 12 14

0

5

10

15

20

25

30

ndash04

01

06

11

16

2000 02 04 06 08 10 12 14

25

30

35

40

45

2000 02 04 06 08 10 12 14

2 Leverage(Percent)



Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Actual mean








0

2

4

6

8

10

12

2000 02 04 06 08 10 12 14





0

1

2

3

4

5

6

7




ndash2

0

2

4

6

8

10


dummy

Shadowrate

(inverse)

VIX




Before 2010

Since 2010

ndash02

0002

04

06

08

10

12

14

16






Volatility Index






ties27






Summary









00

02

04

06

08

1012

14



variables






Firm Size + +




VIX ndash ndash



Estimates


















Policy Implications



0

2

4

6

8

10

12

2003 05 07 09 11 13 15



0

5

10

15

20

25

2003 05 07 09 11 13 15





















Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications
























0

5

10

15

20

25

30

00

02

04

06

08

10

12

14

2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14


0

50

100

150

200

250

300

350

400



0

100

200

300

400

500

600

700

800

900

1000

1990 92 94 96 98 2000 02 04 06 08 10 12 14

EMs excluding China

China

Local currency

Foreign currency

0

20

40

60

80

100

120

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14

2003ndash

07

10ndash

14




Asia excludingChina


Local currency

Foreign currency

Metal andsteel

Mining Oil andgas











0

200

400

600

800

1000

2000 02 04 06 08 10 12 14

0

1

2

3

4

5

6

2000 02 04 06 08 10 12 14





0

5

10

15

20

25

30

2003 05 07 09 11 13

02

03

04

05

06

07

08

09

2000 02 04 06 08 10 12 14

0

5

10

15

20

25

30

ndash04

01

06

11

16

2000 02 04 06 08 10 12 14

25

30

35

40

45

2000 02 04 06 08 10 12 14

2 Leverage(Percent)



Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Actual mean








0

2

4

6

8

10

12

2000 02 04 06 08 10 12 14





0

1

2

3

4

5

6

7




ndash2

0

2

4

6

8

10


dummy

Shadowrate

(inverse)

VIX




Before 2010

Since 2010

ndash02

0002

04

06

08

10

12

14

16






Volatility Index






ties27






Summary









00

02

04

06

08

1012

14



variables






Firm Size + +




VIX ndash ndash



Estimates


















Policy Implications



0

2

4

6

8

10

12

2003 05 07 09 11 13 15



0

5

10

15

20

25

2003 05 07 09 11 13 15





















Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications















0

200

400

600

800

1000

2000 02 04 06 08 10 12 14

0

1

2

3

4

5

6

2000 02 04 06 08 10 12 14





0

5

10

15

20

25

30

2003 05 07 09 11 13

02

03

04

05

06

07

08

09

2000 02 04 06 08 10 12 14

0

5

10

15

20

25

30

ndash04

01

06

11

16

2000 02 04 06 08 10 12 14

25

30

35

40

45

2000 02 04 06 08 10 12 14

2 Leverage(Percent)



Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Median

Mean

Wgt mean

Actual mean








0

2

4

6

8

10

12

2000 02 04 06 08 10 12 14





0

1

2

3

4

5

6

7




ndash2

0

2

4

6

8

10


dummy

Shadowrate

(inverse)

VIX




Before 2010

Since 2010

ndash02

0002

04

06

08

10

12

14

16






Volatility Index






ties27






Summary









00

02

04

06

08

1012

14



variables






Firm Size + +




VIX ndash ndash



Estimates


















Policy Implications



0

2

4

6

8

10

12

2003 05 07 09 11 13 15



0

5

10

15

20

25

2003 05 07 09 11 13 15





















Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications













0

2

4

6

8

10

12

2000 02 04 06 08 10 12 14





0

1

2

3

4

5

6

7




ndash2

0

2

4

6

8

10


dummy

Shadowrate

(inverse)

VIX




Before 2010

Since 2010

ndash02

0002

04

06

08

10

12

14

16






Volatility Index






ties27






Summary









00

02

04

06

08

1012

14



variables






Firm Size + +




VIX ndash ndash



Estimates


















Policy Implications



0

2

4

6

8

10

12

2003 05 07 09 11 13 15



0

5

10

15

20

25

2003 05 07 09 11 13 15





















Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications














ties27






Summary









00

02

04

06

08

1012

14



variables






Firm Size + +




VIX ndash ndash



Estimates


















Policy Implications



0

2

4

6

8

10

12

2003 05 07 09 11 13 15



0

5

10

15

20

25

2003 05 07 09 11 13 15





















Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications
























Policy Implications



0

2

4

6

8

10

12

2003 05 07 09 11 13 15



0

5

10

15

20

25

2003 05 07 09 11 13 15





















Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications

























Report (IMF 2014a)

ndash25

ndash20

ndash15

ndash10

ndash05

00

05

10

15

20

25














FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications
















FX + εit
















0

50

100

150

200

250

300

350

400

450

2003 04 05 06 07 08 09 10 11 12 13

Median SOEs






























Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications

































Box 33 (continued)
















































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications
























































ndash40

ndash30

ndash20

ndash10

0

10





























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications






























Data





Methodology























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications




























PRS Group






CPIS








Source IMF staff




































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications











































References







ment 13 41ndash50





Massachusetts






(December)






Report Basel






(3) 413ndash30












648ndash62






















Press







November 8







16




London












163ndash92













IMFPublications















IMFPublications









corporate levarage imf 2015.9

Documents