Do Contagion Effects Exist in Capital Flow Volatility?

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Do Contagion Effects Exist in Capital Flow Volatility?

May 2013

Hyun-Hoon LEE (Kangwon National University, Korea)

Cyn-Young PARK (Asian Development Bank)

Hyung-suk Byun (Kangwon National University, Korea)

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The volatility of international capital flows and the limited ability of developing economies to deal with it continue to be a major international policy concern.

The spillover or contagion effects refer to the cross-border transmission of financial shocks, through co-movements of asset prices and capital flows

The contagion effects first caught the attention of the global financial community, as the Mexican devaluation in December 1994 brought an abrupt end to capital flows to many Latin American economies and triggered speculative attacks on their currencies—dubbed as the “tequila effect” afterwards.

The next crisis that hit many Asian economies in 1997 spread beyond the regional boundary.

The spillover or contagion effect culminated in the global financial crisis of 2008/09, which rattled financial markets worldwide and pushed the world economy into the worst recession since World War II.

1. Introduction

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Many studies have investigated the contagion effect by analyzing co-movements of international asset market returns and volatilities.

Very few examined the effect of contagion on capital flow volatility and only recently, some began to focus explicitly on volatility of capital flows.

IMF’s 2007 Global Financial Stability Report find that financial market openness and institutional quality are negatively associated with the volatility of capital flows in both emerging and developed economies.

Neumann, Penl, Tanku (2009) examine how different types of capital flows respond to the opening of financial market. Specifically, they show that a further opening of financial markets tends to increase the volatility of FDI in emerging economies, while it does not lead to any meaningful change in the volatility of portfolio investment flows.

Broto et al. (2011) suggest global conditions have differential impacts on FDI, portfolio investment, and other investment flows and find that global factors have become increasingly significant relative to country specific factors since 2000 .

Mercado and Park (2011) investigate the impact of a set of domestic and global factors on the volatility of different types of capital flows to developing economies, and find that a regional factor plays an important role in determining the volatility of capital inflows to emerging Europe and emerging Latin America.

1. Introduction

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There is rich literature on the volatility spillovers between international asset markets for evidence of contagion.

Only recently, some empirical studies have begun to try to identify the sources of capital flow volatility and examine its determinants.

None of these studies, however, explicitly deal with the spillover phenomenon of capital flow volatilities.

This paper aims to fill the gap in the literature by adopting a new measure of capital flow volatility and assessing the spillover or contagion effect in the volatility of capital flows to developing countries.

1. Introduction

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1. Introduction Contribution of this paper

1. This paper adopts the most commonly used measure of volatility – standard deviation of capital flows in a moving window, but with a more rigorous standardization procedure.

2. This paper examines how volatility of capital flows to individual emerging economy responds to the volatility of capital flows (a) to all developing countries and (b) to the neighboring countries in the same region.

3. Comparison of gross capital inflows and net capital inflows in three different types of capital flows: FDI, equities, and other capital flows (mostly bank lending)

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1. Introduction

Major findings of this paper1. This paper presents evidence for strong and significant

contagion effects from global and regional volatilities on the volatility of capital flows in different types to individual economies.

2. The volatility of FDI flows is the lowest and it is least susceptible to intra-regional contagion, relative to those of portfolio investment or other investment (mostly bank lending).

3. The volatilities of portfolio investment and other investment behave in a roughly similar manner in terms of their magnitudes and exposure to cross-border contagion.

4. It is also found that gross capital inflows and net capital inflows are similar in terms of the size of their volatilities, the volatility of net capital flows responds to intra-regional contagion than that of gross inflows.

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1. Introduction

Contents of this paper

1. Introduction

2. Descriptive Analysis

3. Empirical Specification

4. Results

5. Summary and Conclusions

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Data

Time Period: 1990-2009

Countries: 45 emerging and developing countries.

Types of capital flows: FDI, portfolio investment, and other investment flow (IMF, International Financial Statistics).

Gross inflows vs. net inflows The gross capital inflow represents non-residents’

purchases minus non-residents’ sale of domestic assets.

The net capital inflow is defined as gross inflows minus gross outflows.

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Measurement for volatility of capital flows

The measure of capital flow volatility for country i in year t, σit, is given by the following expression:

Where flowik : capital inflows relative to country i’s GDP to

country i and in period k.

While the standard deviation is widely used to estimate volatility, this measure has some important drawbacks.


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( 1)

1( )

t

it ikk t n

flown

( 1)

1 t

ikk t n

flown

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Measurement for volatility of capital flows

Problem 1: different means provides different dimensions to the standard deviation.

This paper normalizes the size of capital flows in each rolling window by setting the largest capital flows in absolute terms at 100 and adjust the rest of capital flows series accordingly.

Problem 2: the choice of the length of a rolling window is arbitrary.

Following other studies, this study also uses the five-year moving window, but the three-year moving window will be provided as a robustness check.

Problem 3: standard deviation of a rolling window is strongly persistent, and when it is used as the dependent variable in a regression analysis, the error term is serially correlated by construction.

This study utilizes the system GMM estimator for dynamic panel data models of Blundell and Bond (1998).


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Volatilities of Different Types of Capital inflows to All Developing Countries

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Volatilities of Different Types of Capital inflows to East Asian Countries

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Note: FDI, FPI, and IOI refer to the standard deviation of foreign direct investment, portfolio investment, and other flows (mostly bank lending) in % of GDP, respectively. NFED_LT and NFED_ST refer to the standard deviation of net flows on long-term external debt and on short-term external debt in % of GDP, respectively.

0

10

20

30

40

50

60

70

80

90

3 Year average_World

VIFDI VIFPI VIOI VNFED_LT VNFED_ST

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The Model

VMit = β0 + EVit β1+PVit-1 β2+ CVit-1 β3+ εit. Where

VM is a volatility measure of the five different types of capital inflows,

EV is a vector of external variables,

PV is a vector of policy variables, and

CV is a vector of control variables.


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Dependent variable: VM

VFDI : 5-year standard deviation of foreign direct investment in % of GDP

VFPI: 5-year standard deviation of portfolio investment in % of GDP

VOI: 5-year standard deviation of other investment in % of GDP

Note: measured with gross inflows or net inflows.


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Explanatory variables

External variables

ALL: Average of the world-wide volatility of capital flows into all emerging countries

East_Asia: Average volatility of capital flows into other neighboring countries in East Asia

Latin_America: Average volatility of capital flows into other neighboring countries in Latin America

East_Europe: Average volatility of capital flows into other neighboring countries in Eastern Europe


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Explanatory variables

Policy variables

KOPEN: Chin and Ito’s financial openness index INSTITUTION: Institutional quality INF: CPI-based inflation rate RESIMP: Stock of foreign reserves in months of imports

Control variables

GDP_PC: log of GDP per capita (constant in 2000US$) PGDP: Change in GDP_PC (i.e. growth rate):


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Benchmark result Table 1: Determinants of Volatility of Capital Inflows to

Emerging

Table 2: Regional Contagion Effects on Volatility of Gross Capital Inflows

Table 3: Regional Contagion Effects on Volatility of Net Capital Inflows

Table 4: Determinants of Volatility of Capital Inflows to Emerging Countries: With the world average level of lows included

Table 5: Summary of Global and Regional Spillover Effects of Capital Flows

4. Results

Table 1: Determinants of Volatility of Capital Inflows to Emerging Countries

VFDI VFPI VOI VFDI VFPI VOI

(1) (2) (3) (4) (5) (6)

Lag of Dep Variable 0.917*** 0.834*** 0.835*** 0.754*** 0.860*** 0.863***

(0.011) (0.011) (0.007) (0.018) (0.012) (0.014)

Volatility of World Average 0.831*** 0.742*** 0.483*** 0.747*** 0.490*** 0.601***

(0.058) (0.043) (0.038) (0.046) (0.019) (0.043)

KOPEN 0.205 -0.854*** 1.296*** -0.914*** 0.544 0.856***

(0.172) (0.203) (0.283) (0.231) (0.339) (0.326)

INSTITUTION -1.735*** -10.916*** -6.262*** 2.606** -10.120*** 2.437

(0.539) (1.745) (1.351) (1.255) (1.065) (1.482)

INF -0.004*** -0.001** 0.003*** -0.002*** -0.000 0.006***

(0.000) (0.001) (0.001) (0.000) (0.001) (0.001)

RESIMP 0.280*** -0.285** 0.063 0.255** -0.550*** -0.212

(0.102) (0.114) (0.082) (0.121) (0.112) (0.242)

GDP_PC 1.129*** 4.798*** 0.165 2.959*** 2.220*** 3.217***

(0.319) (0.411) (0.664) (0.828) (0.450) (1.161)

PGDP -0.938*** -0.351*** -0.995*** -1.118*** 0.275*** 0.317***

(0.036) (0.067) (0.045) (0.083) (0.062) (0.075)

Constant -29.267*** -60.166*** -13.098*** -37.517*** -32.544*** -45.991***

(2.018) (3.939) (4.360) (6.769) (3.643) (9.809)

Number of observations 955 718 986 970 804 975

Number of countries 49 45 49 49 48 49

Arellano-Bond test

AR 1 (p-value) 0.000 0.000 0.000 0.000 0.000 0.000

AR 2 (p-value) 0.519 0.052 0.122 0.951 0.171 0.900

Sargen test (p-value) 0.571 0.807 0.651 0.695 0.746 0.732

Notes: Estimates are made with Blundell-Bond (1998)'s two-step system GMM. Shown in parentheses are standard errors.***, **, and * denote one, five, and ten percent level of significance, respectively.

Gross Inflow Net Inflow

Sample period: 1980-2009


(1) (2) (3) (4) (5) (6)


(0.022) (0.024) (0.024) (0.012) (0.015) (0.012)

East_Asia_Intra 0.092** 0.220*** 0.147***

(0.041) (0.025) (0.045)

Latin_America_Intra -0.113* 0.143*** 0.235***

(0.062) (0.032) (0.029)

East_Europe_Intra 0.042 0.157*** 0.301***

(0.026) (0.034) (0.038)

East_Asia_Extra 0.032 0.124*** -0.052

(0.043) (0.036) (0.054)

Latin_America_Extra -0.240*** 0.093*** 0.059

(0.038) (0.025) (0.051)

East_Europe_Extra -0.017 -0.045 0.103***

(0.046) (0.031) (0.040)

KOPEN -0.393 -0.070 -0.928*** -0.700*** 1.685*** 1.211***

(0.405) (0.163) (0.287) (0.207) (0.279) (0.388)

INSTITUTION -1.299*** -1.316 -10.682*** -11.338*** -2.864** -5.836***

(0.454) (1.099) (1.114) (2.294) (1.354) (1.147)

INF -0.002*** -0.001 0.000 -0.003*** 0.002*** 0.002*

(0.001) (0.001) (0.002) (0.001) (0.001) (0.001)

RESIMP 0.060 0.020 -0.337 -0.076 0.306* 0.192*

(0.125) (0.107) (0.311) (0.193) (0.175) (0.108)

GDP_PC 2.278*** 2.378*** 3.359*** 3.802*** -0.652 1.014

(0.778) (0.413) (0.746) (0.903) (1.206) (1.099)

PGDP -0.813*** -0.827*** -0.570*** -0.562*** -0.982*** -1.097***

(0.069) (0.069) (0.163) (0.114) (0.074) (0.066)

Constant -15.290*** -14.473*** -20.272*** -21.245*** 8.273 0.648

(4.702) (2.884) (6.079) (6.597) (8.690) (9.015)



Arellano-Bond test

AR 1 (p-value) 0.000 0.000 0.000 0.000 0.000 0.000

AR 2 (p-value) 0.473 0.450 0.059 0.062 0.089 0.116


VFDI VFPI VOI

Notes: Estimates are made with Blundell-Bond (1998)'s two-step system GMM. Shown in parentheses are standarderrors. ***, **, and * denote one, five, and ten percent level of significance, respectively.



(1) (2) (3) (4) (5) (6)


(0.026) (0.017) (0.019) (0.014) (0.037) (0.028)

East_Asia_Intra 0.343*** 0.327*** 0.643***

(0.041) (0.043) (0.049)

Latin_America_Intra 0.357*** 0.295*** 0.496***

(0.031) (0.034) (0.067)

East_Europe_Intra 0.237*** 0.450*** 0.818***

(0.054) (0.075) (0.101)

East_Asia_Extra 0.148*** -0.013 0.063

(0.046) (0.030) (0.039)

Latin_America_Extra 0.171*** 0.106* 0.103*

(0.027) (0.056) (0.059)

East_Europe_Extra 0.040 0.165*** -0.073

(0.057) (0.063) (0.087)

KOPEN -1.442*** -1.018*** 1.008 1.443*** 2.688*** 0.526

(0.418) (0.236) (0.904) (0.424) (0.445) (0.703)

INSTITUTION 0.585 2.103** -15.376*** -9.585*** -4.528 2.137

(1.353) (1.051) (2.568) (1.176) (3.641) (2.781)

INF -0.001** -0.002*** -0.003*** -0.002 0.002* 0.005***

(0.001) (0.000) (0.001) (0.002) (0.001) (0.001)

RESIMP 0.298 0.360* -0.281 -0.298*** -0.889 0.195

(0.270) (0.208) (0.272) (0.102) (0.751) (0.551)

GDP_PC 4.089*** 4.329*** 1.931 0.770 -3.727 3.991

(0.642) (0.896) (1.574) (0.734) (3.449) (2.599)

PGDP -0.673*** -0.860*** -0.105 0.150* 0.658*** 0.137

(0.093) (0.068) (0.167) (0.080) (0.235) (0.135)

Constant -30.097*** -27.860*** -17.309 -0.586 22.014 -27.499

(4.672) (6.542) (12.494) (6.085) (23.648) (17.807)



Arellano-Bond test

AR 1 (p-value) 0.000 0.000 0.000 0.000 0.000 0.000

AR 2 (p-value) 0.851 0.884 0.162 0.162 0.867 0.618



VFDI VFPI VOI

Notes: Estimates are made with Blundell-Bond (1998)'s two-step system GMM. Shown in parentheses are standarderrors. ***, **, and * denote one, five, and ten percent level of significance, respectively.

Table 4: Determinants of Volatility of Capital Inflows to Emerging Countries:With the world average level of flows included


(1) (2) (3) (1) (2) (3)


(0.013) (0.015) (0.008) (0.020) (0.012) (0.013)

Volatility of World Average 1.087*** 0.860*** 0.454*** 0.760*** 0.701*** 0.554***

(0.065) (0.047) (0.036) (0.047) (0.061) (0.046)

Level of World Average 1.449*** 2.971*** -0.080 -0.203 -1.012*** 0.673***

(0.135) (0.460) (0.065) (0.192) (0.317) (0.233)

KOPEN 0.017 -1.130*** 1.189*** -0.851*** 1.301** 0.490

(0.155) (0.169) (0.249) (0.264) (0.518) (0.341)

INSTITUTION -1.369** -11.758*** -4.916*** 2.455* -11.934*** 5.773***

(0.695) (2.073) (1.497) (1.448) (1.266) (1.850)

INF -0.005*** -0.001 0.002** -0.002*** 0.002 0.006***

(0.001) (0.001) (0.001) (0.000) (0.001) (0.001)

RESIMP -0.045 -0.676** 0.015 0.297 -0.366*** -0.410**

(0.155) (0.269) (0.123) (0.182) (0.140) (0.209)

GDP_PC 0.242 4.573*** 0.456 3.273*** 3.198*** 1.968*

(0.441) (0.668) (0.757) (0.841) (0.608) (1.096)

PGDP -1.107*** -0.528*** -0.822*** -1.110*** 0.555*** 0.275***

(0.062) (0.108) (0.103) (0.084) (0.118) (0.075)

Constant -33.939*** -65.569*** -14.089*** -39.680*** -49.910*** -35.007***

(3.312) (6.581) (5.433) (6.983) (6.832) (9.591)



Arellano-Bond test

AR 1 (p-value) 0.000 0.000 0.000 0.000 0.000 0.000

AR 2 (p-value) 0.520 0.058 0.123 0.954 0.177 0.925


Notes: Estimates are made with Blundell-Bond (1998)'s two-step system GMM. Shown in parentheses are standard errors.***, **, and * denote one, five, and ten percent level of significance, respectively.

Net Inflow


Gross Inflow

Table 5: Summary of Global and Regional Spillover Effects of Capital Flows


(1) (2) (3) (4) (5) (6)

Average of World Volatilities 0.831*** 0.742*** 0.483*** 0.747*** 0.490*** 0.601***

(0.058) (0.043) (0.038) (0.046) (0.019) (0.043)

East_Asia_Intra 0.092** 0.220*** 0.147*** 0.343*** 0.327*** 0.643***

(0.041) (0.025) (0.045) (0.041) (0.043) (0.049)

Latin_America_Intra -0.113* 0.143*** 0.235*** 0.357*** 0.295*** 0.496***

(0.062) (0.032) (0.029) (0.031) (0.034) (0.067)

East_Europe_Intra 0.042 0.157*** 0.301*** 0.237*** 0.450*** 0.818***

(0.026) (0.034) (0.038) (0.054) (0.075) (0.101)

Average of World Volatilities 0.863*** 0.420*** 0.429*** 0.890*** 0.397*** 0.467***

(0.043) (0.023) (0.036) (0.049) (0.018) (0.029)

East_Asia_Intra 0.058* 0.147*** 0.055 0.321*** 0.392*** 0.510***

(0.031) (0.020) (0.059) (0.024) (0.066) (0.025)

Latin_America_Intra -0.091** -0.067 0.178*** 0.378*** 0.172*** 0.560***

(0.037) (0.050) (0.037) (0.014) (0.043) (0.042)

East_Europe_Intra -0.002 0.135*** 0.191*** 0.169*** 0.668*** 0.582***

(0.037) (0.038) (0.032) (0.059) (0.114) (0.073)

1980-2009

1980-2007

Notes: Estimates are made with Blundell-Bond (1998)'s two-step system GMM. Shown in parentheses are standard errors. ***, **, and *denote one, five, and ten percent level of significance, respectively. The results for the period 1980-2009 are taken from Tables 1, 2, and 3,while those for the period 1980-2007 are the corresponding results when the global crisis years of 2008 and 2009 are excluded from thesample.

Gross Inflow Net InflowSampleperiod

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5. Summary and Conclusions

Hyun-Hoon Lee

[email protected]

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Do Contagion Effects Exist in Capital Flow Volatility?