push factors and capital flows to ems: why knowing your ...€¦ · why knowing your lender matters...

Push factors and capital flows to EMs: Why knowing your lender matters

more than fundamentals

Eugenio Cerutti (IMF, Res. Dept.) Stijn Claessens (Federal Reserve Board)

Damien Puy (IMF, Res. Dept.)

August 14 – Banco Central Do Brasil

Disclaimer! This presentation represents our own views and not necessarily those of the IMF, IMF policy makers, or the Federal Reserve

Board of Governors or its staff.

MOTIVATION: Common wisdom on Capital Flows to EMs

• Capital inflows to EMs co-move due, at least partly, to push factorsCalvo et al. (1993, 1996), Chuhan, Claessens, and Mamingi (1998), Fratzscher (2012), Forbes and Warnock (2012), Rey (2013).

• Main push factors: U.S. monetary policy, the supply of global liquidity (especially in U.S. dollars), and global risk aversion –although importance varies across studies

• Impact of these push factors vary across EMs, but research conflicts on how and why

- Some say fundamentals matter (good / bad /neither): Ghosh et al. (14)

- Better macro fundamentals do reduce sensitivity: Prachi et al. (14), Ahmed et al. (14) versus Better macro fundamentals do not reduce sensitivity: Aizenman et al. (14), Eichengreen and Gupta (14)

2

MOTIVATION: Three reasons to reexamine common wisdom

1. Some facts: “Counter-intuitive” capital movements during the Taper Tantrum, with “better” countries affected more.

2. Recent research: little (or counter-intuitive) discrimination across EMs by global investors (such as global funds) during in- and outflows (Jotikastira et al, JF ‘12, Raddatz and Schmukler, JIE ‘12, IMF GFSR, ‘15). Some evidence also for global banks (Cetorelli & Goldberg ‘12, Cerutti & Claessens ’14, Bruno & Shin, ‘15)

3. Previous studies shortcomings:⇒ focused mostly on dynamics of prices (rather than flows) to recipient markets

and short-lived episodes of stress.⇒ General shortcoming of the typical panel regression w/ observed proxies

We revisit how common factors (differentially) impact EMs using:⇒ BOP gross inflows to 34 EMs between 2000 and 2013.⇒ A new methodology relying on latent factors to measure co-movement.

3

FOUR KEY QUESTIONS AND RESULTS

1. Do we really observe co-movement in gross inflows to EMs? YES, BUT

• Gross inflows to EMs do co-move, but much heterogeneity across asset classes

• Equity, Bond and OI-Bank flows co-move - not FDI and OI-Non Bank

2. If yes, what is driving this EM dynamic? PUSH, BUT VARIES BY FLOWS• Push factors in core countries explain EM common dynamic but their importance varies

across type of flows

3. Who is more sensitive? MUCH HETEROGENEITY• Some countries very sensitive across all assets (e.g., Brazil, Indonesia, Turkey, etc), others

to particular assets (e.g., India to Equity; Mexico to Bonds).

4. Why more sensitive? NOT FUNDAMENTALS• Market characteristics rather than institutional/macro fundamentals• Liquidity and composition of the foreign investor base (reliance on international funds

and global banks)

4

KEY CONTRIBUTIONS1. Estimates co-movement in flows across EMs by type

• Disaggregate gross inflows: assets do not all co-move, respond differently to various push factors, with very heterogeneous effects across, even within a single country.

• Use of latent factors helps to better capture “true” co-movement

=> Qualifies generalizations about the drivers of capital inflows to EMs

2. Qualifies the role of fundamentals in explaining EMs response to AE conditions

• Episodes of surges/retrenchments sometimes found to be at odds with fundamentals

• Recent literature on the pro-cyclical behavior of international investors (funds, banks)⇒Knowing your lender, and its mandate/incentives/constraints matters more

3. Adds to policy debate

• Helps to identify which country is more sensitive to core countries’ conditions (and through which BOP component)

=> Watch your lender5

HOW DO WE GET THERE ? – 5 STEPS

1. Collect gross inflows from BOP (total & by type of flow)

Sample: 2001-2013, 34 countries, quarterly frequencyFlows: - % GDP , 5 sub-categories

- FDI, Portfolio Equity, Portfolio Bond, OI-Bank, OI-Non Bank

2. Estimate a (latent) factor model to break flows into global, regional and idiosyncratic components

Kose et al. (IBC: world, regional and country-specific factors, AER 2003) - Bayesian Estimation

tiregion

tregioni

EMt

EMiti ffy ,, εββ ++=

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METHODOLOGY

- We estimate the following latent factor model for each type of flows:

Same methodology as in Kose et al. (2003, AER)

- Key Assumptions:

- Factors follow AR processes => dynamic latent factor model

- Factors are orthogonal to each other

- Regional decomposition:- 4 regions: LatAm, Asia, Emerging Europe, MEA

- Estimation:- Data augmentation to estimate parameters and factors (Gibbs sampling)

- Same priors as Kose et al. (2003) – others yield almost identical results

- Posterior distribution properties (parameters, factors) based on 200,000 MCMC replications and 20,000 burn-in replications

tiregion

tregioni

EMt

EMiti ffy ,, εββ ++=

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HOW DO WE GET THERE – 5 STEPS

3. Recover the common factor (total & by type of flow)

=> Question 1

4. Relate the common EM factors to observables (total & by type of flow)

=> Question 2

5. Measure and explain factor sensitivities “beta”

=> Question 3 and 43. Show the dispersion in across countries4. Explain betas by running a horse race between Fundamentals (Macro & Institutional) versus Market (Destination and Source) characteristics

EMtf

EMiβ

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ANSWER 1: CO-MOVEMENT IN INFLOWS VARIES

- Commonalities in Equity, Bond and Bank flows but not in FDI and OI-Non Bank

- Commonality in factors captures key events (Lehman, Euro, Taper Tantrum)

corr equity-bond = 0.53corr bank-bond = 0.6corr equity-bank = 0.29

Estimated Common Factors – All inflows and Sub-components

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COMMON FACTOR RELATES TO VIX, BUT IMPERFECTLY

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VIX Common Factor - Total Gross Inflows 10

ANSWER 2: WHAT IS DRIVING THE EM DYNAMIC ?We run the following regression for total flows and flows by type:

Key results: • Most push factors significant in total flow, but importance greatly varies

across types (Commodity prices most significant pull factor)• Using all push factors is far from capturing true extent of co-movement

Bank Specific

BondSpecific

Equity Specific

TotalAggregate

Push Variables GDP GrowthUS VIX

US Policy Rate Exp.Slope of US yield curve

US REER

Pull VariablesEM GDP Growth (lag)

Commodity prices growth

Type Specific Variables

GlobalLeverage

TED spread

US 10 year BondEMBI return (lag)

MSCI return (lag)US Equity market

return

All?

EMt t t t tf Push Pull Type Specificα β γ ε= + + +

11

ANSWER 3: WHO IS MORE SENSITIVE?

BOP Equity flows to Philippines vs Common EM Equity factor

correlationPhilippines - EM Equity factor = 0.74

12

ANSWER 3: WHO IS MORE SENSITIVE? SOME COUNTRIES, AS THE (EQUITY) BETAS VARY A GREAT DEAL…

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ANSWER 3: WHO IS MORE SENSITIVE? THE BOND BETAS…

Bond Beta on the common EM factor

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ANSWER 3: WHO IS MORE SENSITIVE? A SUMMARY

Equity Bond BankTurkey 0.56 0.42 0.42South Africa 0.46 0.58 0.50Israel 0.17 0.36 -0.03

Argentina 0.37 0.14 0.32Brazil 0.58 0.52 0.46Chile -0.06 0.15 0.19Colombia 0.16 0.02 0.23Mexico 0.30 0.38 0.27Peru 0.27 0.33 0.45Uruguay -0.09 0.44 0.02Venezuela, Rep. Bol. -0.06 0.29 -0.18

India 0.67 0.16 0.23China,P.R.: Mainland 0.41 -0.08 0.57Indonesia 0.51 0.69 0.43Korea, Republic of 0.49 0.27 0.43Malaysia 0.38 0.29 0.45Pakistan 0.90 0.40 0.12Philippines 0.64 0.36 0.19Thailand 0.58 0.36 0.40

Equity Bond BankBelarus 0.02 0.22 0.20Kazakhstan 0.62 0.43 -0.09Bulgaria 0.45 0.04 0.18Russian Federation 0.29 0.36 0.39Ukraine 0.22 0.31 0.20Czech Republic 0.14 0.41 0.43Slovak Republic -0.05 0.44 0.20Estonia 0.13 -0.22 -0.05Latvia 0.12 0.25 0.10Hungary -0.07 0.43 -0.14Lithuania -0.09 0.35 -0.12Croatia 0.21 0.12 -0.40Slovenia 0.64 0.22 0.13Poland 0.21 0.49 -0.12Romania 0.60 0.34 -0.02

Three Groups of EMs:- High sensitivity (Turkey, Brazil..)- Varying by flows (China, Mexico…)- Low Sensitivity (Chile, Estonia…)

15

ANSWER 4: WHY MORE SENSITIVE? FUNDAMENTALS LESS, MARKETS MORE

Key question: Why do some countries always gain (or lose) more flows when conditions in AE change?

Approach: we regress the for each asset on the following variables:• Fundamentals

• Macro: Public Debt, Growth, Trade openness, Reserves/GDP, FX regime

• Institutions: Law and order, Investor protection, Political risk

• Market Structure Characteristics • Openness, Size, Liquidity, Foreign Investor Base Composition

Baseline regression for each cross section:

EMiβ

εβαβ ++= iiEM

i MarketFund **16

MARKET STRUCTURE DATA

Foreign Openness Size LiquidityComposition of the Foreign Investor

Base

Equity Market

Stock of Foreign Equity funding/GDP

Local size: Stock market Capitalization/GDP

Relative to EMs:Stock of Foreign Equity/ Total Stock of Foreign

Equity in EMs

- Stock Market turnover (as % of

Market Cap)- Listed in MSCI

benchmark (Emerging or

Frontiers)

-Share of Foreign Equity funding

coming from AEs

- Correlation of BOP equity flows with

EPFR equity flows

Bond Market

Stock of Foreign Bond funding/GDP

Local size: Bond market Capitalization/GDP

Relative to EMs:Stock of Foreign Equity/ Total Stock of Foreign

Equity in EMs

- Listed in EMBI benchmark

-Share of Foreign Bond funding coming

from AEs

- Correlation of BOP bond flows with

EPFR bond flows

Banking Sector

Stock of Foreign Bank Claims/GDP

Private credit /GDP - Correlation of BOP bank flows with BIS

global bank flows

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ANSWER 4 – WHY MORE SENSITIVE? FUNDAMENTALS VS. MARKETSEquity Beta Bond Beta Bank Beta

(1) (2) (3) (4) (5) (6) (7) (8) (9)Fundamentals

Trade Openness -0.0236 0.0179** 0.0110** 0.0708

Debt/GDP 0.0163 0.0193 -0.0104

Reserves/GDP 0.0190 -0.450-

0.559*** 0.0567

FX Regime 0.0192 0.0315*** 0.0203** 0.0342** 0.0766Average Growth 0.0508 0.0122 0.0486Investor Protection -0.0114 -0.0423 0.0348Law and Order -0.0199 -0.0256 -0.0696

Market CharacteristicsForeign Openness 0.0124 0.00281 -0.00306Local Market Size (%GDP) 0.0714 0.000733Relative Market Size -0.0328 0.00608EM Benchmark -0.0648 0.0233Frontiers Benchmark 0.228*** 0.242***Turnover Ratio 0.0176*** 0.0170***Share of Funding from AE 0.00167 0.00108Correlation with EPFR (or BIS) flows 0.474*** 0.454*** 0.260** 0.241** 0.705*** 0.75***

constant 0.0123 -0.0369 0.0637 -0.0130 0.110 0.101 -0.207 0.146 0.0994

R-sq 0.244 0.546 0.521 0.409 0.288 0.429 0.324 0.520 0.530

• No role for Institutional Quality or Fiscal Fundamentals

• Some Role for fundamentals (TO and FX regime in Bonds)

• Liquidityimportant for equity flows

• Investor base is always significant

ACTUAL VS. PREDICTED BETA – EQUITY

Turkey

South Africa

Argentina Brazil

Chile

ColombiaMexico

PeruUruguayVenezuela, Rep. Bol.

Israel

India

Indonesia

Korea, Republic of

Malaysia

Pakistan

Philippines

ThailandKazakhstan

Bulgaria

Russian Federation

China,P.R.: Mainland

Ukraine

Czech Republic

Slovak Republic

EstoniaLatvia

Hungary

Lithuania

Croatia

Slovenia

PolandRomania

0.2

.4.6

.8Fi

tted

valu

es

0 .2 .4 .6 .8 1Predicted Equity response

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ROBUSTNESS 1

• Estimated Betas do not only reflect the Global Financial Crisis, but also Taper Tantrum, so something more structural

• Many countries had different macro situations in both episodes but reacted in ways consistent with estimated model

The model vs the Crisis (left) vs the Tantrum (right)

Turkey

South Africa

Argentina

Brazil

Chile

Colombia

MexicoPeru

UruguayVenezuela, Rep. Bol.

Israel

India

Indonesia Korea, Republic of

Malaysia

Pakistan

Philippines

Thailand

Belarus

Kazakhstan

Bulgaria

Russian Federation


Ukraine

Czech Republic

Slovak Republic

Estonia Latvia

HungaryLithuania

Croatia

Slovenia

Poland

Romania

0.2

.4.6

.81

Equ

ity B

eta

coef

ficie

nt

-2 0 2 4 6Equity Drop - Crisis

Turkey

South Africa

Argentina

Brazil

Chile

Colombia

Mexico


Israel

IndonesiaKorea, Republic of

Pakistan

Philippines

Thailand

Belarus

Kazakhstan

Bulgaria

Russian Federation


Ukraine

Czech Republic

Slovak Republic

EstoniaLatvia

HungaryLithuania

Croatia

Slovenia

Poland

Romania

0.2

.4.6

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ity B

eta

coef

ficie

nt

-2 -1 0 1 2 3Equity Drop - Tantrum (as %GDP, std)(as %GDP, std)

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ROBUSTNESS 2 AND 3

• Bayesian averaging exercise confirms sensitivity determinants

• Institutional fundamentals themselves do not drive importance of global investors (fund or bank) in our sample

Turkey

South AfricaArgentina

Brazil

Chile

Colombia

Mexico

Peru

Uruguay

Venezuela, Rep. Bol.

Israel

India

Indonesia

Korea, Republic of

Malaysia

Pakistan

Philippines

Thailand

Belarus Kazakhstan

Bulgaria

Russian Federation

China,P.R.: MainlandUkraine

Czech Republic

Slovak RepublicEstonia

Latvia

HungaryLithuania

CroatiaSlovenia

Poland

Romania

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34

5La

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rder

-.5 0 .5 1EPFR Equity Correlation

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SUMMARY AND POLICY IMPLICATIONS

• Sensitivity of EMs to push factors is more about financial market characteristics/conditions than about (institutional) fundamentals – Still Fundamentals matter in general .

• Consistent with:• More micro-based evidence on mutual funds investing internationally

(Raddatz and Schmukler, 2012)

• Banking flows evidence (Bruno and Shin, 2015)

• Implications: Need to monitor lenders/investors

• P.S. Sensitivity per se does not mean high macroeconomic risks• High sensitivity is problematic if flows are macro-relevant

• Other factors might amplify (or dampen) the price effect of a high sensitivity (ex: Malaysia) 22

POLICY IMPLICATIONS

Turkey

South Africa

Argentina

Brazil

Chile

Colombia

MexicoPeru


Israel

India

Indonesia Korea, Republic of

Malaysia

Pakistan

Philippines

Thailand

Belarus

Kazakhstan

Bulgaria

Russian Federation


Ukraine

Czech Republic

Slovak Republic

Latvia

HungaryLithuania

Croatia

Slovenia

Poland

Romania

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dict

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quity

resp

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0 .2 .4 .6 .8EquityBOPvar

Variance of Equity inflows vs Equity Beta

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POLICY IMPLICATIONS

Variance of Bond inflows vs Bond Beta

Turkey

South Africa

Argentina

Brazil

Chile

Colombia

Mexico

Peru

Uruguay


Israel

India

Indonesia

Korea, Republic ofMalaysia

PakistanPhilippinesThailand

Belarus

Kazakhstan

Bulgaria

Russian Federation


Ukraine

Czech RepublicSlovak Republic

Estonia

Latvia

Hungary

Lithuania

Croatia

Slov

Poland

Romania

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BACKGROUND SLIDES

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A FIRST LOOK AT THE DATA

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Gross inflows to 34 EMs – Aggregated (source: BOP)

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SAMPLE OF COUNTRIES

Latin America Asia Emerging Europe OtherArgentina India Belarus TurkeyBrazil China, Mainland Kazakhstan South AfricaChile Indonesia Bulgaria IsraelColombia Republic of Korea Russian FederationMexico Malaysia UkrainePeru Pakistan Czech RepublicUruguay Philippines Slovak RepublicVenezuela, Rep. Bol. Thailand Estonia

LatviaHungaryLithuaniaCroatiaSloveniaPolandRomania

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BOP HETEROGENEITY IN PRACTICE

Gross Inflows versus inflows by component - INDIA

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DATA AND SOURCES

Capital Flows: focus on gross inflows, i.e. transaction by non-residents, which can be positive or negative. Three types of data:

- BOP Capital Inflows (gross inflow as % of GDP, total and by component) Source: IFS- Global Bank Flows (inflows as % of GDP) Source: BIS LBS- Mutual Fund Flows (inflows as % of GDP) Source: EPFR

Push/Pull Factors (Real GDP growth, US VIX, US Yield curve, etc)

Macroeconomic and Institutional fundamentals (Trade openness, Public Debt, Rule of Law, Investor Protection, etc.)

Market Characteristics (Foreign openness, Market capitalization, MSCI EM, EMBI EM, etc.)

29

CO-MOVEMENT OF EQUITY FLOWS WITH MSCI

Correlation MSCI – Equity EM factor = 0.6

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Table 4 - Drivers of EM Common Capital Flows Dynamics(3) (4) (7) (8) (11) (12) (15) (16)

VARIABLES bank bank bond bond equity equity total total

Core_GDP_Growth 0.0736* 0.0547 -0.0416 -0.0308 0.0588 0.0329 0.00857 -0.0351(0.0412) (0.0426) (0.0583) (0.0627) (0.0526) (0.0532) (0.0388) (0.0417)

US VIX -0.0161 0.00183 -0.0356*** -0.0353*** -0.0218* -0.0232* -0.0209** 0.00264(0.0112) (0.0154) (0.0106) (0.0104) (0.0130) (0.0116) (0.00991) (0.0167)

Exp. Change in Policy Rate -0.101 -0.00785 -0.194 -0.198 0.126 -0.0690 -0.372* -0.415*(0.232) (0.253) (0.304) (0.289) (0.370) (0.333) (0.214) (0.206)

US yield_curve -0.127 -0.0374 -0.1000 -0.0131 -0.392** -0.268** -0.233** -0.164(0.104) (0.107) (0.167) (0.126) (0.147) (0.108) (0.102) (0.132)

US REER -0.0567*** -0.0855*** -0.0454*** -0.0172 -0.00481 0.00386 -0.0334*** -0.0617***(0.0115) (0.0188) (0.00942) (0.0121) (0.00903) (0.00808) (0.00940) (0.0182)

Commodityprice_pch 0.0314** 0.0282** 0.0241* 0.0291** 0.00262 -0.00673 0.0382*** 0.0279**(0.0119) (0.0124) (0.0129) (0.0133) (0.0153) (0.0156) (0.00954) (0.0119)

L.RGDP_EM_growth -0.0396 -0.0399 -0.120** -0.0896* -0.0551 -0.0153(0.0551) (0.0600) (0.0530) (0.0530) (0.0417) (0.0445)

Global_bank_leverage 0.0682** 0.0569(0.0335) (0.0516)

TED -0.527** -0.778**(0.261) (0.292)

US 10 bond yield -0.240* 0.0382(0.135) (0.130)

L.EMBI_growth 0.0330* -0.00357(0.0165) (0.0139)

L.MSCI_growth 0.0153** 0.0195(0.00592) (0.0125)

Observations 50 50 50 50 50 50 50 50R-squared (overall) 0.710 0.731 0.569 0.580 0.421 0.436 0.733 0.789R-squared (push variables) 0.477 0.477 0.420 0.330 0.363 0.327 0.454 0.381R-squared (pull variables) 0.233 0.234 0.149 0.144 0.058 0.026 0.279 0.227R-squared (type variables) - 0.021 - 0.106 - 0.083 - 0.181Note: This table reports the regression results of equation 11 in the main text. Robust standard errors in parentheses. Asterisks denote significant coefficients, with ***, **, * indicating significance at 1%, 5%, and 10%

ANSWER 2: WHAT IS DRIVING THE EM DYNAMIC VARIES•Most push factors significant in Total EM common flow dynamics

•But, importance greatly varies across types

- REER significant for bank and bond

-VIX significant for bond and equity

- Slope of yield curve significant for equity

•Commodity prices most significant pull factor

•Type factors not reflected in total aggregate but in components

ANSWER 2: WHAT IS DRIVING THE EM DYNAMIC VARIES

• EM common dynamic explained mostly by push factors in core countries • The relative importance of push factors varies across type of flows• Pull variables somewhat more important for bond and bank flows (but as

function of commodity prices, so not really an EM internal driver)• Some other type-specific factors play minor roles for disaggregated flows

32

VARIANCE DECOMPOSITIONS

Share of (total) variance in each flow accounted for by the common EM factor Equity Bond Bank Equity Bond Bank

Argentina 25% 12% 29% Belarus 0% 10% 7%Brazil 24% 25% 25% Kazakhstan 30% 20% 1%

Chile 2% 2% 4% Bulgaria 16% 1% 5%Colombia 1% 1% 8% Russian Federation 8% 15% 23%Mexico 7% 29% 8% Ukraine 3% 11% 10%Peru 3% 8% 26% Czech Republic 1% 17% 21%Uruguay 2% 18% 0% Slovak Republic 1% 16% 5%Venezuela, Rep. Bol. 0% 7% 3% Estonia 1% 5% 1%Average Latam 8% 13% 13% Latvia 1% 5% 4%

Hungary 1% 14% 1%Lithuania 1% 12% 1%Croatia 3% 1% 15%

India 35% 4% 7% Slovenia 35% 4% 5%China,P.R.: Mainland 18% 2% 35% Poland 1% 22% 1%Indonesia 18% 43% 23% Romania 30% 11% 2%Korea, Republic of 7% 11% 19% Average Emerging Europe 9% 11% 7%Malaysia 18% 16% 22%Pakistan 60% 13% 1% Turkey 22% 26% 37%Philippines 48% 11% 6% South Africa 17% 30% 22%Thailand 32% 17% 20% Israel 3% 13% 2%Average Asia 29% 15% 16% Average Other 14% 23% 20%

Color code: red = upper 25% percentile ~ above 20% absolute value 33

FULL VARIANCE DECOMPOSITION RESULTS 1

Portfolio Equity Portfolio Bond OI Bank All inflowsLatin America Global Regional Global Regional Global Regional Global Regional

Argentina 25% 7% 12% 11% 29% 5% 12% 20%Brazil 24% 4% 25% 6% 25% 11% 27% 5%Chile 2% 10% 2% 20% 4% 4% 11% 13%

Colombia 1% 12% 1% 20% 8% 6% 1% 32%Mexico 7% 8% 29% 11% 8% 32% 20% 15%

Peru 3% 5% 8% 2% 26% 3% 34% 19%Uruguay 2% 16% 18% 14% 0% 8% 1% 3%

Venezuela, Rep. Bol. 0% 6% 7% 6% 3% 11% 3% 16%Average 8% 8% 13% 11% 13% 10% 14% 15%

AsiaIndia 35% 16% 4% 34% 7% 2% 55% 4%

China,P.R.: Mainland 18% 4% 2% 3% 35% 7% 28% 1%Indonesia 18% 8% 43% 9% 23% 23% 21% 8%

Korea, Republic of 7% 12% 11% 2% 19% 38% 50% 12%Malaysia 18% 4% 16% 6% 22% 38% 39% 45%Pakistan 60% 18% 13% 7% 1% 2% 2% 10%

Philippines 48% 4% 11% 2% 6% 2% 26% 1%Thailand 32% 15% 17% 21% 20% 7% 53% 13%Average 29% 10% 15% 11% 16% 15% 34% 12%

34

FULL VARIANCE DECOMPOSITION RESULTS 2Portfolio Equity Portfolio Bond OI Bank All inflows

Emerging Europe

Belarus 0% 0% 10% 16% 7% 0% 1% 3%Kazakhstan 30% 2% 20% 11% 1% 42% 4% 34%

Bulgaria 16% 62% 1% 2% 5% 38% 6% 60%Russian Federation 8% 2% 15% 3% 23% 37% 36% 17%

Ukraine 3% 0% 11% 2% 10% 68% 8% 36%Czech Republic 1% 10% 17% 10% 21% 6% 1% 12%Slovak Republic 1% 1% 16% 3% 5% 9% 0% 2%

Estonia 1% 91% 5% 2% 1% 53% 6% 39%Latvia 1% 1% 5% 46% 4% 59% 11% 50%

Hungary 1% 0% 14% 3% 1% 25% 1% 35%Lithuania 1% 16% 12% 5% 1% 75% 5% 63%Croatia 3% 0% 1% 5% 15% 2% 2% 36%

Slovenia 35% 1% 4% 2% 5% 64% 17% 40%Poland 1% 3% 22% 9% 1% 24% 32% 11%

Romania 30% 1% 11% 10% 2% 60% 8% 69%Average 9% 13% 11% 9% 7% 38% 10% 43%

Other

Turkey 22% 20% 26% 3% 37% 6% 36% 5%South Africa 17% 14% 30% 22% 22% 10% 41% 18%

Israel 3% 33% 13% 39% 2% 53% 17% 30%Average 14% 22% 23% 10% 20% 23% 24% 29%

35

ANSWER 3: WHO IS MORE SENSITIVE?THE BANK BETAS….

Bank Beta on the common EM factor

-0,8

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

0,8

1

36

ACTUAL VS. PREDICTED BETA– BOND

TurkeySouth Africa

Argentina

BrazilChileColombia

Mexico

Peru

Uruguay


Israel

India

Indonesia

Korea, Republic of

Malaysia

Pakistan

Philippines

Thailand

Belarus

Kazakhstan

Bulgaria

Russian Federation


Ukraine

Czech Republic

Slovak Republic

Estonia

Latvia Hungary

Lithuania

CroatiaSlovenia

Poland

Romania

0.1

.2.3

.4.5

Fitte

d va

lues

0 .2 .4 .6 .8Predicted Bond response

37

ACTUAL VS. PREDICTED BETA– BANK

Turkey

South Africa

Argentina

Brazil

Chile

Colombia

Mexico

Peru

Uruguay

Venezuela, Rep. Bol.Israel

India

Indonesia

Korea, Republic of

Malaysia

Pakistan

Philippines

Thailand

Belarus

Kazakhstan

Bulgaria

Russian Federation


Ukraine

Czech Republic

Slovak Republic

EstoniaLatvia

Hungary

Lithuania

Croatia

Slovenia

Poland

Romania

-.20

.2.4

.6Fi

tted

valu

es

-.4 -.2 0 .2 .4 .6Predicted Bank response

38

Turkey

South Africa

Argentina

Brazil

Chile

Colombia

Mexico

Peru

Uruguay


Israel

Indonesia

Korea, Republic ofMalaysia

PakistanPhilippinesThailand

Belarus

Kazakhstan

Bulgaria

Russian Federation


Ukraine


Estonia

Latvia

Hungary

Lithuania

Croatia

Slovenia

Poland

Romania

0.2

.4.6

.8B

ond

Bet

a co

effic

ient

0 1 2 3 4Bond Drop - Crisis

Turkey

South Africa

Argentina

Brazil

Chile

Colombia

Mexico

Peru

Uruguay


Israel

Indonesia

Korea, Republic of

PakistanPhilippines Thailand

Belarus

Kazakhstan

Bulgaria

Russian Federation


Ukraine


Estonia

Latvia

Hungary

Lithuania

Croatia

Slovenia

Poland

Romania

0.2

.4.6

.8B

ond

Bet

a co

effic

ient

-1 -.5 0 .5 1 1.5Bond Drop -Tantrum(as %GDP, std) (as %GDP, std)

The model vs the Crisis (left) vs the Tantrum (right) – Bond flows

ROBUSTNESS 1

39

POLICY IMPLICATIONSVariance of Bank inflows vs Bank Beta

Turkey

South Africa

Argentina

Brazil

ChileColombia

Mexico

Peru

Uruguay


Israel

India

IndonesiaKorea, Republic ofMalaysia

Pakistan

Philippines

Thailand

Belarus

Kazakhstan

Bulgaria

Russian Federation


Ukraine

Czech Republic

Slovak Republic

Estonia

Latvia

HungaryLithuania

Croatia

Slovenia

Poland

Romania

-.4-.2

0.2

.4.6

Pre

dict

ed B

ank

resp

onse

0 5 10 15 20BankBOPvar

Turkey

South Africa

Argentina

Brazil

ChileColombia

Mexico

Peru


Israel

India

Indonesia Korea, Republic ofMalaysia

Pakistan

Philippines

Thailand

Belarus Bulgaria

Russian Federation


Ukraine

Czech Republic

Slov

Hungary Lithuania

Croatia

Slovenia

Poland

Romania

-.4-.2

0.2

.4.6

Pred

icted

Ban

k re

spon

se

0 2 4 6BankBOPvar 40

MACROECONOMIC AND INSTITUTIONAL DATA

Variable Definition Frequency Source Market Characteristics

Foreign Openness Stock of foreign Equity, Bond or Bank claims/GDP Average over 2001-2013 IIP

Stock Market Capitalization Stock Market Cap/GDP Average over 2001-2013World Bank Financial Development Database

Bond Market Capitalization* Bond Market Cap/GDP Average over 2001-2013World Bank Financial Development Database

Private Credit Bank Credit to the Private Sector/GDP Average over 2001-2013

World Bank Financial Development Database

Stock Market Turnover Sum of all shares traded over the period/Stock Market Cap Average over 2001-2013

World Bank Financial Development Database

Share of Funding coming from Advanced Economies

Sum of Bond (Equity) coming from AEs and Financial centers/Total Bond (Equity) Funding

Average over 2001-2013 CPIS

MSCI EM Country listed in the MSCI Emerging index over the sample period Dummy Morgan Stanley

MSCI FMCountry listed in the MSCI Frontier Market index over the sample period

Dummy Morgan Stanley

EMBI EMCountry listed in the EMBI Emerging index over the sample period

Dummy JP Morgan

41

MACROECONOMIC AND INSTITUTIONAL DATA

Macroeconomic and Institutional fundamentals

Trade Openness (X+M)/GDP Average over 2001-2013

World Development Indicators

Public Debt as % GDP Average over 2001-2013


Reserves as % GDP Average over 2001-2013


Real GDP Growth %, annual Average over 2001-2013


Rule of Law Index from 1 to 10 Average over 2001-2013 ICRG

Investor Protection Index from 1 to 10 Average over 2001-2013 ICRG

Political Risk Index from 1 to 10 Average over 2001-2013 ICRG

42

(as %GDP, std) (as %GDP, std)

The model vs the Crisis (left) vs the Tantrum (right) – Bank flows

ROBUSTNESS 1

Turkey

South Africa

Argentina

Brazil

ChileColombia

Mexico

Peru

Uruguay


Israel

India

Indonesia Korea, Republic ofMalaysia

Pakistan

Philippines

Thailand

Belarus

Kazakhstan

Bulgaria

Russian Federation


Ukraine

Czech Republic

Slovak Republic

Estonia

Latvia

Hungary Lithuania

Croatia

Slovenia

Poland

Romania

-.4-.2

0.2

.4.6

Ban

k B

eta

coef

ficie

nt

-2 0 2 4 6Bank Drop - Crisis

Turkey

South Africa

Argentina

Brazil

ChileColombia

Mexico

Peru

Uruguay


Israel

India

Korea, Republic of

Philippines

Thailand

Belarus

Kazakhstan

Bulgaria

Russian Federation


Ukraine

Czech Republic

Slovak Republic

Estonia

Latvia

HungaryLithuania

Croatia

Slovenia

Poland

Romania

-.4-.2

0.2

.4.6

Bank

Bet

a co

effic

ient

-1 0 1 2Bank Drop - Tantrum

43

WHY USE CORRELATION WITH EPFR?

44

MORE DATA ON FOREIGN INVESTOR BASE

Turkey

South Africa

Argentina

Brazil

Chile

Colombia

Mexico

Peru

Uruguay

India

Indonesia

Malaysia

PhilippinesThailand

Bulgaria

Russian Federation


Ukraine

Latvia

Hungary

Lithuania

Poland

Romania

0.2

.4.6

.8Pr

edic

ted

Bond

resp

onse

.2 .4 .6 .8 1bondforeign_official

Share of country debt owned by (foreign) officials vs Bond Beta

45

BAYESIAN AVERAGING EXERCISE

Equity- Bayesian Averaging Bond- Bayesian Averaging Bank - Bayesian Averaging Coef. t-Stat PIP Coef. t-Stat PIP Coef. t-Stat PIP

Trade/GDP 0.000 -0.17 0.09 Trade/GDP 0.000 0.3 0.15 Trade/GDP -0.041 -0.79 0.47Debt/GDP 0.000 0.04 0.07 Debt/GDP 0.000 0.24 0.12 Debt/GDP -0.024 -0.4 0.21Reserves/GDP 0.000 -0.02 0.07 Reserves/GDP -0.001 -0.41 0.21 Reserves/GDP 0.023 0.25 0.14FX Regime 0.002 0.29 0.13 FX Regime 0.019 1.34 0.73 FX Regime 0.048 0.19 0.12Average Growth 0.008 0.43 0.22 Average Growth -0.001 -0.14 0.1 Average Growth -0.017 -0.05 0.1Investor Protection -0.003 -0.27 0.13 Investor Protection 0.000 -0.05 0.09 Investor Protection -0.129 -0.2 0.12Law and Order -0.009 -0.34 0.17 Law and Order -0.001 -0.13 0.09 Law and Order 0.105 0.16 0.1

Foreign Equity Stock/GDP 0.000 0.17 0.09 Foreign Bond Stock/GDP 0.001 0.27 0.14 Foreign OI stock -0.004 -0.12 0.1Local Equity Size 0.000 0.17 0.09 Private credit/GDP 0.027 0.52 0.29Relative Equity Size 0.000 0.07 0.08 Relative Market Size 0.000 0.13 0.1MSCI Benchmark (dummy) 0.000 -0.01 0.09 EMBI Benchmark (dummy) 0.007 0.25 0.12MSCI Frontries Benchmark (dummy) 0.184 1.48 0.77Turnover Ratio 0.001 1.45 0.77Share of Funding from AE 0.000 0.24 0.11 Share of Funding from AE 0.000 0.26 0.13Correlation with EPFR Equity flows 0.325 1.39 0.74

Correlation with EPFR Bond flows 0.102 0.7 0.41 Correlation w/ BIS flows 37.545 4.09 0.99

46

push factors and capital flows to ems: why knowing your ...€¦ · why knowing your lender matters...

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