Do Contagion Effects Exist in Capital Flow Volatility?

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) 1

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 2

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 3

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 4

1. Introduction Contribution of this paper 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. 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. 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) 5

1. Introduction Major findings of this paper 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. 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). 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. 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. 6

1. Introduction Contents of this paper 1. Introduction 2. Descriptive Analysis 3. Empirical Specification 4. Results 5. Summary and Conclusions 7

2. Descriptive Analysis 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. 8

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. 2. Descriptive Analysis 9

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). 2. Descriptive Analysis 10

Volatilities of Different Types of Capital inflows to All Developing Countries 11

Volatilities of Different Types of Capital inflows to East Asian Countries 12

Volatilities of Different Types of Capital inflows to All Developing Countries 15

Volatilities of Different Types of Capital inflows to All Developing Countries 2. Descriptive Analysis 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. 16

The Model VMit = β0 + EVitβ1+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. 3. Empirical Specification 17

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. 3. Empirical Specification 18

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 3. Empirical Specification 19

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): 3. Empirical Specification 20

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 21

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

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

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

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

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

5. Summary and Conclusions 27

Hyun-Hoon Lee hhlee@kangwon.ac.kr

Do Contagion Effects Exist in Capital Flow Volatility?