Valuation effects of capital inflows: Evidence from emerging market economies

Journal Pre-proofs Valuation Effects of Capital Inflows: Evidence from Emerging Market Economies Dieu Thanh Le, Hail Park PII: DOI: Reference:

S1062-9408(19)30079-8 https://doi.org/10.1016/j.najef.2019.101092 ECOFIN 101092

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North American Journal of Economics & Finance

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12 February 2019 2 September 2019 15 October 2019

Please cite this article as: D. Thanh Le, H. Park, Valuation Effects of Capital Inflows: Evidence from Emerging Market Economies, North American Journal of Economics & Finance (2019), doi: https://doi.org/10.1016/j.najef. 2019.101092

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Valuation Effects of Capital Inflows: Evidence from Emerging Market Economies

Abstract This paper studies valuation changes of capital inflows in 19 emerging market economies (EMEs). In most of the EMEs, we find that there are significant valuation changes and a positive rate of return on external liabilities by foreigners. Furthermore, the nonlinear effects of exchange rate movements on valuation changes are investigated using panel smooth transition regression models. Empirical results show that the transition is centered at approximately -22.3% of exchange rate change, which implies that when the exchange rate appreciates more than this level, foreign investment value gains increase considerably.

Keywords: Valuation Changes, Capital Inflows, Exchange Rate Movements, Panel Smooth Transition Regression JEL Classification: F32, F34

1. Introduction Capital flows can be measured by using balance of payments (BOP) and the international investment position (IIP).1 When measuring financial accounts, the BOP shows economic transactions alone, whereas the IIP shows not only transaction factors but also nontransaction factors. Transaction factors reflect actual capital movements, whereas nontransaction factors involve valuation changes caused by changing prices and exchange rates. Chung et al. (2014) found that the negative net international investment position (NIIP) has increased rapidly in emerging market economies (EMEs) since the 2000s, even though net capital flows based on flow criteria decreased in the 2000s. They also showed that the changes in valuations of nonresidents’ inward investments are much greater than the changes in valuations of residents’ external investments in EMEs. This suggests that valuation gains resulting from changes in (asset) prices and exchange rates are not large in EMEs’ external investment, since most of their foreign reserves are invested in secure assets in advanced countries, such as in US Treasury bonds. The effect of exchange rates on valuation changes is therefore crucial in EMEs, where exchange rate fluctuations are relatively large compared to advanced economies. Several existing studies have addressed the issues of valuation changes and their effects. Lane and Milesi-Ferretti (2001) constructed a stock-based dataset of foreign assets and liabilities, as well as the equity and debt compositions of 67 economies. In the process, they highlighted issues with valuation changes for external assets and liabilities and indicated that these stemmed from price and exchange rate changes. The value of debt-categorized assets and liabilities is merely affected by the movement of exchange rates, whereas equity and FDI investment valuation changes are related to changes in both prices and exchange rates. Pistelli et al. (2008) and Gourinchas and Rey (2007a, 2007b) addressed the strong linkage between valuation effects and exchange rate movements. Tille (2003) showed that changes in dollar value play an important

1

The BOP is a flow statistic that indicates economic transactions between domestic residents and foreigners (nonresidents). Net capital flows can be obtained by deducting (gross) capital outflows from (gross) capital inflows. The IIP is a stock statistic that indicates the outstanding balance of gross external assets (external investment) and gross external liabilities (investment by foreigners) at a certain time. The outstanding net international investment position balance is obtained by deducting the outstanding balance of gross external liabilities from that of gross external assets, with the result indicating the country’s net external assets.

2

role in the changes of the US net international investment position. The authors highlighted the substantial influence that a strong US dollar had on the worsening of the US net international investment position in the early 2000s. Specifically, this influence was estimated to account for one-third of the net IIP’s deterioration via the valuation channel. Benetrix et al. (2015) further developed Lane’s and Shambaugh’s (2010a, 2010b) work on international currency exposures, and comprehensively examined the role of exchange rates as valuation adjustments using linear regression models. The value of external liabilities is a linear function of exchange rate movements. However, in some cases, the effect of exchange rate movements on valuation changes may not be linear. If the stock price of a country rises, inflows of foreign equity investment will increase. In the meantime, currency appreciation due to the increase in equity inflows will lead to additional equity investment inflows aimed at foreign exchange gains. Among types of capital inflows, equity inflows have higher price increases than bond investment inflows and other investment inflows, which can cause significant valuation changes due to the rises in stock price and in the value of the currency. The threshold values can thus be different depending on types of capital inflows. As an exchange rate fluctuates, the types of capital inflow might change, and valuation effects due to changes in prices might vary. An exchange rate may therefore have a threshold value beyond which its effects on valuation changes might differ significantly. In regard to this question, this paper decomposes the valuation changes of external liabilities and calculates the rate of return on different types of external liabilities by foreigners in EMEs. The threshold effects of exchange rate movements on valuation changes in EMEs are also investigated, with an exclusive focus on capital inflows, because as mentioned above, valuation changes of capital outflows from EMEs are not significant. Specifically, this study aims to find the threshold level beyond which the effects of exchange rates on valuation changes in capital inflows change significantly. The paper is organized as follows: in section 2, we discuss some stylized facts regarding valuation changes in EMEs; in section 3, we present empirical analyses that estimate the threshold effects; and in section 4, we present our conclusions.

3

2. Valuation Changes of Capital Inflows in Emerging Market Economies Inconsistencies can be found when comparing flow and stock data. It is important to explore where the differences in the results originate. Using Equation (1), we can obtain the NIIP balance at the end of period t+1 by adding the valuation changes to the BOP financial account between periods t and t+1 and to the NIIP balance at the end of period t. The changes in the NIIP between periods t and t+1 can thus be obtained using Equation (2), where FAt+1 denotes transaction factors and VAt+1 denotes nontransaction factors, such as valuation changes: NIIPt + FAt+1 + VAt+1 = NIIPt+1 NIIPt+1 - NIIPt = FAt+1 + VAt+1

(1) (2)

where NIIPt: NIIP balance at the end of period t, FAt+1: change in BOP financial account between t and t+1, VA t+1: valuation changes between periods t and t+1, NIIP t+1: NIIP balance at the end of period t+1. NIIP refers to the stock of residents’ external assets minus the external liabilities outstanding to nonresidents at a certain time, as in Equation (3). The NIIP is composed of direct investment, equity and bond investment, and other investment. In contrast to external liabilities, official foreign reserves are included in external assets, as shown in Equation (4): NIIPt = GFAt – GFLt

(3)

GFAt = FDAt + EAt + DAt + OAt + RESERVEt

(4)

GFLt = FDLt + ELt + DLt + OLt

(5)

where GFAt: Gross External Assets, GFLt: Gross External Liabilities, FDAt: Direct assets, EAt: Equity assets, DAt: Bond assets, OAt: Other assets, RESERVEt: Foreign reserves, FDLt: Direct liabilities, ELt: Equity liabilities, DLt: Bond liabilities, OLt: Other liabilities. As mentioned above, considerable valuation changes in capital inflows to EMEs have been observed since the 2000s, whereas changes in the values of capital outflows from these countries have been relatively insignificant. It is therefore more plausible to focus on and thoroughly examine the case of gross external liabilities. Given the equations above, the relationship between transaction and nontransaction factors affecting gross external liabilities can be represented as follows: 4

GFLt+1 – GFLt = FALt+1 + VALt+1

(6)

where GFLt denotes the value of gross external liabilities at the end of the period t, FALt+1: value of financial account inflows during period (t+1) and VALt+1: valuation changes of external liabilities in period (t+1). The valuation changes can be rewritten as follows: GFLt+1 – GFLt = FALt+1 + IRLt+1GFLt

(7)

where IRLt+1 = VALt+1 /GFLt represents the rate of return on gross external liabilities by foreigners. Next, all the factors were normalized by nominal GDP as follows: gflt+1 – gflt = faLt+1 + irLt+1gflLt gflt+1 = gflt + faLt+1 + irLt+1gflLt

(8) (9)

where gflt = GFLt /GDPt , irLt+1 = IRLt+1/Gt+1 , faLt+1 = FALt+1/GDPt+1 and Gt+1 = GDPt+1/GDPt Figure 1 shows the decomposition of changes in external liabilities in 19 EMEs during the period 2000-2014. As shown in Figure 1, there are significant valuation changes in almost all of the cases during the observation period when compared with actual capital inflows. The results suggest that inconsistencies are found when comparing flow and stock data. For example, in Brazil, while the financial account remained steady at approximately 4-8% of nominal GDP over this period, the valuation changes fluctuated considerably and constantly changed from a positive to a negative sign. Similar patterns were observed in other emerging market economies in Latin America, Central and Eastern Europe, and Asia.

In the meantime, the rate of return on different types of external liabilities by foreigners in EMEs can be calculated. As mentioned, gross external liabilities are composed of several types of investment, namely: direct, equity, bond and other investments. Similarly, to the gross external liabilities, the value of the j type of investment liabilities can be written as follows: GFLjt+1 = GFLjt + FALjt+1 + IRLjt+1GFLjt

(10)

Following that, the value of gross external liabilities at the end of period (t+1) can be rewritten as follows: 5

GFLt+1 = ∑jGFLjt+1 = ∑j GFLjt + ∑j GFLjt ∑j (GFLjt/∑jGFLjt)IRLjt+1 + ∑j FALjt+1 = ∑j GFLjt + ∑j GFLjt∑j §LjtIRLjt+1 + ∑j FALjt+1

(11)

With §Ljt = GFLjt /∑j GFLjt and ∑j §Ljt = 1 where §Ljt denotes the proportion of individual types of investment in the total nonresident investment, and where IRLjt+1 is the rate of return on each type of investment. Table 1 shows the composition of each investment made by foreigners and their rate of return in 20 EMEs during 2000-2014. In most of the observed countries, a positive rate of return on total external liabilities can be seen. Korea takes first place, followed by Hungary, the Slovak Republic and Poland. In the case of Korea, equity investment liabilities made up the largest part of total external liabilities; given that finding, the high positive rate of return on equity investments by foreigners in Korea results in the high positive rate of return on total external liabilities. Meanwhile, in the other economies that are at the top of the list in terms of the total rate of return, direct investment is the main type of investment among the 4 types; thus, the rate of return on gross external liabilities follows the pattern of the rate of return on direct investment. Ukraine and Venezuela are the two countries in which the highest negative rate of return on total liabilities is observed. In the case of Venezuela, the negative rate of return on direct investment liabilities (which make up almost half of the total investment), is attributable to the high negative total rate of return. On the other hand, in the case of Ukraine, this is the result of the negative rate of return on both direct and other investments.

6

3. Nonlinear Effects of Exchange Rate Movements In this section, the panel smooth transition regression (PSTR) model developed by González et al. (2005) is applied to investigate the nonlinear effects of exchange rate movements on valuation changes in capital inflows. 𝑣𝑎_𝑖𝑛𝑖𝑡 = 𝛼𝑖+ 0Xit + 1Xit g(erit; , c) + it

(12)

The dependent variable is the valuation changes in capital inflows va_init, which is calculated by finding the difference between the stock value of capital inflows, and the sum of the flow value and previous year’s stock value, which is normalized by the economy’s GDP. Xit is a set of independent variables that refer to existing studies (Curcuru et al., 2010; Forbes, 2010; Hausmann and Sturzenegger, 2006), and the variables consist of the ratio of external liabilities to external assets liab_assetit, the proportion of FDI and equity inflows in total capital inflows fdiequityit, the exchange rate change erit, the domestic stock price index growth stock_dit and the global stock price index growth stock_gt. The transition variable is the exchange rate change erit. We also use valuation changes in direct investment va_fdiit, equity investment va_equityit, bond investment va_bondit and other investment inflows va_otherit variables as dependent variables to examine the nonlinear effects of exchange rates on valuation changes in these 4 types of capital inflows. The transition function g(erit; , c) is a continuous function of the transition variable erit (exchange rate change) and is normalized to fall between 0 and 1. The transition function is defined as follows: g(erit; , c) = 1/{1+exp[- (erit - c)]}

(13)

where  (>0) is the slope parameter that determines the smoothness of the transition, and where c is the location parameter2, at which the panel is divided into two “extreme regimes” associated with low and high values of the exchange rate change erit. As erit increases, there is a single monotonic transition of coefficients from 0 to 0 + 1, where the change in coefficients is centered around the location parameter c. At c, the coefficients of independent variables will take a value of (0 + 0.51). 2

Both the slope parameter and the location parameter are estimated using a two-dimensional grid. More precisely, it is a conditional - iterative procedure. Thus, the standard errors for the other parameter estimates are somewhat lower than they should be since the estimates are conditional on the values of  and c.

7

A quarterly panel dataset covering data from 19 EMEs during the period from 2000Q1 to 2014Q3 is used. Most of the data are extracted from the IMF, while the stock price index data are taken from Bloomberg. Detailed descriptions of the variables used are presented in Table 2 and the list of EMEs is shown in Table 3. According to González et al. (2005), the parameter estimation in the PSTR model can be defined as the application of fixed effects estimation and nonlinear least squares (NLS). Specifically, it consists of eliminating the individual effects, by removing individual-specific means and then applying NLS to the transformed data. The first step in the testing procedure is to test the linearity against the PSTR model. The null hypothesis of the linearity test of equation (12) can be written as: H0:  = 0

(14)

The transition function g(erit; , c) will be replaced by its first-order Taylor expansion around  = 0, leading to the following auxiliary regression: 𝑣𝑎_𝑖𝑛𝑖𝑡 = 𝛼𝑖 + 𝛽0∗ 𝑋𝑖𝑡 + 𝛽1∗ 𝑋𝑖𝑡𝑒𝑟𝑖𝑡 + 𝑖𝑡∗

(15)

In this case, testing H0:  = 0 in equation (12) turns into testing H0∗ : 𝛽1∗ = 0 in equation (15). Then, the likelihood ratio (LR) test is determined. The next step in the PSTR model specifications test consists of testing the number of transition functions. It can be regarded as the test of no remaining nonlinearity in the model. The logic for this test follows a similar procedure to the linearity test against the PSTR model. For example, the null hypothesis of the linearity test was rejected. In the next step, we need to test whether there is one (r = 1) or two transition functions (r = 2) in the model. We can thus obtain the following equation: 𝑣𝑎_𝑖𝑛𝑖𝑡 = 𝛼′𝑖 + 𝛽′0𝑋𝑖𝑡 + 𝛽′1𝑋𝑖𝑡 g1(erit; 1, c1) + 𝛽′2𝑋𝑖𝑡 g2(erit; 2, c2)+ ′𝑖𝑡

8

(16)

The null hypothesis of this no remaining nonlinearity test can be written as: H0: 2 = 0. Applying the same method, g2(erit; 2, c2) is replaced with a first-order Taylor expansion around 2 = 0, and the null hypothesis is tested based on the following auxiliary regression: 𝑣𝑎_𝑖𝑛𝑖𝑡 = 𝛼′𝑖 + 𝛽′0∗ 𝑋𝑖𝑡 + 𝛽′1∗ 𝑋𝑖𝑡 g1(erit; 1, c1) + 𝛽′2∗ 𝑋𝑖𝑡 erit+ ′𝑖𝑡∗ (17) The null hypothesis of the no remaining nonlinearity can be rewritten as: H0∗ : 𝛽′2∗ = 0. Then, the LR test is computed. The testing procedure can be summarized as follows: 1) Estimate a linear model and test the linearity against the PSTR model. 2) If the linearity hypothesis is rejected, estimate a PSTR model with one transition (r =1). 3) Test the null hypothesis of the no remaining nonlinearity. If it is rejected, estimate a PSTR model with two transitions (r = 2). 4) Perform continuous testing until the first acceptance of the null hypothesis of no remaining nonlinearity is reached. At each step, reduce the significance level by a factor , 0<<1 to avoid excessively large models.

Table 4 shows the results of the panel smooth transition regression models with total capital inflows (sum of the 4 types of capital inflows) and the 4 types of capital inflows as dependent variables, respectively, over the sample period from 2000Q1 to 2014Q3. In the model with total capital inflows as the dependent variable, the transition is estimated to be slow and smooth (=0.0017) and to center around a -22.3% exchange rate change. In the cases of the ratio of external liabilities to external assets (liab_assetit) and the proportion of FDI and equity inflows in total capital inflows (fdiequityit), the coefficients of 0 are significantly positive and those of 1 significantly negative. This implies that the effects of the two variables on valuation changes are positive, but they decrease gradually. Meanwhile, both domestic and global stock prices are statistically insignificant. In the case of the 4 types of capital inflows as dependent variables, the models with direct investment, equity investment and other investment are estimated to have one transition, while there are two transitions in the model that has bond investment inflows as dependent variables. The change is estimated to center around -23.7% in the case of FDI and -10.0% in the case of 9

equity investment. However, in the model with other investment inflows as the dependent variable, the transition happens when the exchange rate depreciates near 10%. In these 3 models, the slope parameter is estimated to be small, which means that the transitions are relatively slow. The two transitions in the model with the bond investment inflows are estimated to center approximately 0.003% and 16.8%. Notably, the slope parameter in the first transition is very large (=58,339.0), implying that the PSTR model in this case can be seen as the panel threshold regression model (Hansen, 1999). The transitions in five models with total capital inflows, FDI investment, equity investment inflows, bond investment inflows and other investment inflows are plotted in Figures 2-6. In the cases of the direct, equity and other investments, the coefficients 1 of fdiequity_init are estimated to be significantly negative, signifying negative effects on valuation changes when the exchange rate change goes beyond the threshold values. The coefficient 1 of stockprice_dit is significantly positive, which means that the domestic stock price index growth has a positive effect on valuation changes, even when the exchange rate change exceeds the threshold. In addition, we can see that the results of the model with the total capital inflow are fairly similar to the results in the cases of direct investment and equity investment, and these findings are consistent with previous studies. This is understandable, as FDI and equity investment are the two main types of capital inflow into emerging countries.

4. Conclusion 10

This paper decomposes valuation changes in external liabilities and calculates the rate of return on different types of external liabilities by foreigners in emerging market economies (EMEs). We find that there are significant valuation changes and a positive rate of return on external liabilities by foreigners in most of the EMEs. In addition, the nonlinear effects of exchange rate movements on valuation changes in capital inflows to EMEs are examined. The estimation results confirm the negative effects of exchange rate movements on valuation changes. The transition is found to center at approximately -22.3% of the exchange rate change, which implies that when the exchange rate appreciates more than this level, foreign investors’ investment value gains increase considerably. It is important to note that the results of the model with total capital inflows as a dependent variable are fairly similar to those of the models with direct investment and equity investment, because these two types of investment make up most of the capital inflow for EMEs.

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Table 1. Composition and rate of return: by countries Unit: % Total

Direct investment

Equity investment

IRLt

IRL1t

§L1(t-1)

IRL2t

Belarus

-1.31

-1.90

29.64

Brazil1)

-0.43

-0.67

42.62

Chile

-0.66

-0.75

Colombia

0.18

Croatia Czech Republic

Bond investment

§L2(t-1)

IRL3t

3.08

0.10

2.52

25.56

61.50

-2.92

0.43

49.99

-0.69

-2.18

1.04

0.95

Hungary

1.47

Israel Kazakhstan

Other investment

§L3(t-1)

IRL4t

§L4(t-1)

-0.02

2.49

-1.11

67.77

-0.28

17.54

-5.22

14.28

6.61

0.16

8.79

-0.08

23.10

0.02

2.42

-0.24

16.97

0.02

30.62

34.90

-5.61

1.23

0.36

12.54

0.19

51.33

59.31

3.92

5.27

-0.16

10.28

1.14

25.14

2.03

62.51

3.70

3.66

0.57

13.15

-0.05

20.68

0.22

-4.56

25.46

5.54

26.27

-0.47

14.70

-0.01

33.57

0.55

2.07

55.56

1.75

2.84

-3.10

6.70

-1.25

34.90

Korea

2.82

-0.58

18.28

8.30

36.97

-2.44

19.51

1.33

25.24

Latvia

0.26

0.87

26.92

4.35

0.65

1.51

4.50

-0.10

67.93

Lithuania

0.53

0.73

35.81

3.46

1.11

1.95

18.17

-0.28

44.91

Pakistan2)

0.11

-1.45

25.96

6.05

3.42

1.55

2.45

0.35

68.17

Peru

0.62

-1.68

36.97

8.17

15.55

-6.81

12.51

2.36

34.97

Poland

1.31

1.79

43.54

4.21

6.14

1.56

19.49

-0.12

30.83

Romania

0.05

-0.56

41.84

-1.79

1.48

-0.87

5.58

0.71

51.10

Slovak Republic

1.35

1.84

52.07

-10.91

0.84

-1.05

14.71

1.99

32.38

Ukraine3)

-2.82

-5.04

31.28

0.20

1.90

0.36

11.85

-2.35

54.97

Venezuela -2.36 -5.72 43.11 -6.44 1.15 -1.81 24.08 1.96 31.66 Notes: 1) 3) During 2002-2014; 2) During 2004-2014, §Lj(t-1): the proportion of j investment in the total investment in period (t-1), IRLjt: rate of return of j investment in period t.

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Table 2. Descriptive statistics Variable

Definition Valuation changes in capital

va_init

inflows to GDP (%)

Obs.

Average

877

2.04

877

Standard

Min

Max

32.60

-172.12

337.58

0.56

19.68

-162.59

332.58

877

0.37

5.74

-37.88

22.26

877

0.14

7.23

-65.92

47.76

877

0.97

12.65

-119.15

93.86

933

192.79

74.87

6.02

497.65

973

45.95

16.12

-8.58

74.94

1,106

1.13

9.23

-51.86

95.87

996

3.98

15.79

-45.44

166.75

1,081

0.62

11.01

-193.50

34.94

Deviation

Valuation changes in foreign va_fdiit

direct investment inflows to GDP (%)

va_equityit va_bondit va_otherit liab_assetit

Valuation changes in equity investment inflows to GDP (%) Valuation changes in bond investment inflows to GDP (%) Valuation changes in other investment inflows to GDP (%) External Liabilities/External Assets (%) The proportion of FDI and equity

fdiequityit

inflows to total capital inflows (%)

erit stock_dit stock_gt

Exchange rate change (%) Domestic stock price index growth (%) Global stock price index growth (%)

Table 3. List of emerging market economies Belarus Brazil Chile Colombia Croatia

Czech Republic Hungary Israel Kazakhstan

Korea

Poland

Latvia

Romania

Lithuania

Slovak Republic

Pakistan

Ukraine

Peru

Venezuela

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Table 4. Results of panel smooth transition regression Transition variable: Exchange rate change Dependent Variables liab_assetit fdiequityit stock_dit stock_gt Transition Function No. of transition functions Slope parameters (1) Location parameters (1) Slope parameters (2) Location parameters (2) Linearity Test LR Test: H0: r=0 vs H1: r=1 p-value LR Test: H0: r=1 vs H1: r=2 p-value LR Test: H0: r=2 vs H1: r=3 p-value

Capital inflows

Direct investment inflows

Equity investment inflows

Bond investment inflows

Other investment inflows

0

1

0

1

0

1

0

1

2

0

1

0.1373** (0.0663) 0.8410*** (0.2438) -0.2121 (0.2573) 0.1607 (0.3272)

-0.1405** (0.0702) -1.1066*** (0.3566) 0.6911 (0.4444) 0.1097 (0.5655)

0.1273*** (0.0395) 0.3286** (0.1668) -0.2122 (0.1928) 0.1789 (0.2140)

-0.1181** (0.0478) -0.3221 (0.2026) 0.5917* (0.3267) 0.0695 (0.3695)

-0.0045 (0.0051) 0.2133*** (0.0287) 0.0566 (0.0414) 0.1052* (0.0529)

0.0492*** (0.0078) -0.2928*** (0.0333) 0.1949** (0.0874) -0.0180 (0.1086)

-0.0654 (0.0868) -0.3838* (0.2128) 0.4319 (0.3115) 2.7946*** (0.5606)

0.0337 (0.0871) 0.3741* (0.2091) -0.3905 (0.3080) -2.7825*** (0.5574)

0.0658*** (0.0164) -0.0474 (0.0674) -0.1042 (0.1222) 0.1426 (0.1396)

-0.0971*** (0.0198) 0.3755*** (0.0809) -0.0409 (0.1380) 0.1646 (0.1526)

0.1946*** (0.0268) -0.3940*** (0.0724) 0.0397 (0.1748) -0.0873 (0.2087)

1

1

1

2

1

0.0017 -22.3

0.0016 -23.7

0.0047 -10.0

58338.9 0.0026 0.0019 16.752

0.01 10.3

247.69 0.0000 1.6364 0.8022

64.05 0.0000 0.9723 0.9140

117.03 0.0000 6.0788 0.1933

17.89 0.0000 52.974 0.0000 7.8776 0.0962

80.05 0.0000 4.7000 0.3195

Notes: 1) The values in parentheses are standard errors corrected for heteroskedasticity. 2) *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

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Figure 1. Decomposition of changes in external liabilities of EMEs (2000-2014)

15

Note: 1), 2), 3) Data from different periods are presented due to the unavailability of data. Source: IFS, IMF.

16

Figure 2: Sensitivity of valuation changes in total capital inflows to fdiequity

Coefficient of fdiequity

0= 0.8401

c = -22.2759 ( = 0.0017) 0.3019 0.2314

-60

-40

-20

0

20

40

60

80

100

Exchange rate change (%)

0 + 1= -0.2656

Figure 3: Sensitivity of valuation changes in FDI inflows to fdiequity

Coefficient of fdiequity

0= 0.3286

c = -23.7279 ( = 0.0016) 0.1711 0.1524

-60 0 + 1= 0.0065

-40

-20

0 20 40 Exchange rate change (%)

17

60

80

100

Figure 4: Sensitivity of valuation changes in equity investment inflows to fdiequity

Coefficient of fdiequity

0= 0.2133

c = -10.0216 ( = 0.0047) 0.0813

0.0313

-60

-40

-20

0

0 + 1= -0.0794

20

40

60

80

100

Exchange rate change (%)

Figure 5: Sensitivity of valuation changes in bond investment inflows to fdiequity 0 + 1=-0.0097 0 + 1 + 2 =-0.0571 Coefficient of fdiequity

c2 = 16.7519 (2 = 0.0019)

c1 = 0.0026 (1 = 58339)

0= -0.3838 -60

-40

-20

0

20

40

Exchange rate change (%)

18

60

80

100

Figure 6: Sensitivity of valuation changes in other investment inflows to fdiequity

0.2390

Coefficient of fdiequity

0= 0.3755

c = 10.3093 ( = 0.0102)

0.0915

-60 0 + 1= -0.0185

-40

-20

0

20

40

Exchange rate change (%)

19

60

80

100

References Bénétrix, A. S., Lane, P. R., & Shambaugh, J. C. (2015), “International currency exposures, valuation effects and the global financial crisis”, Journal of International Economics, 96, S98S109. Chung, K., Kim, S., Park, H., Choi, C., & Shin, H. S. (2014), “Volatile capital flows in Korea: Current policies and future responses”, Palgrave Macmillan. Curcuru, S. E., Dvorak, T., & Warnock, F. E. (2010), “Decomposing the US external returns differential, Journal of international Economics, 80, 22-32. Forbes, K. J. (2010), “Why Do Foreigners Invest in the United States?”, Journal of International Economics, 80, 3-21. González, A., Teräsvirta, T., & van Dijk, D. (2005), “Panel smooth transition regression models”, SSE/EFI Working Paper Series in Economics and Finance. Gourinchas, P. O., & Rey, H. (2007a), “From world banker to world venture capitalist: US external adjustment and the exorbitant privilege”, G7 Current Account Imbalances: Sustainability and Adjustment, 11-66. Gourinchas, P. O., & Rey, H. (2007b), “International financial adjustment”, Journal of Political Economy, 115, 665–703. Hansen, B. E. (1999). “Threshold effects in non-dynamic panels: Estimation, testing and inference”, Journal of Econometrics, 93, 345-368.

Hausmann, R., & F. Sturzenegger (2006), “Global Imbalances or Bad Accounting? The Missing Dark Matter in the Wealth of Nations”, CID Working Paper No. 124. 20

Lane, P. R., & G. M. Milesi-Ferretti (2001), “The external wealth of nations: Measures of foreign assets and liabilities for industrial and developing countries”, Journal of International Economics, 55, 263-294. Lane, P. R., & Shambaugh, J. C. (2010a), “Financial exchange rates and international currency exposures”, American Economic Review, 100(1), 518-40. Lane, P. R., & Shambaugh, J. C. (2010b), “The long or short of it: Determinants of foreign currency exposure in external balance sheets”, Journal of International Economics, 80(1), 33-44. Pistelli, A., Selaive, J., & Valdés, R. O. (2008), “Stocks, flows, and valuation effects of foreign assets and liabilities: Do they matter?”, Central Banking, Analysis, and Economic Policies Book Series, 12, 237-277. Tille, C. (2003), “The impact of exchange rate movements on U.S. foreign debt,” Federal Reserve Bank of New York Current Issues in Economics and Finance, 9.

Title: Valuation Effects of Capital Inflows: Evidence from Emerging Market Economies

First author: Dieu Thanh Le Address: Economic Division, Embassy of the Republic of Korea, Hanoi, Vietnam Email: [email protected]

Second author (Corresponding author): Hail Park 21

Address: Department of International Business & Trade, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul, Republic of Korea Email: [email protected] Phone number: +82-2-961-2170 Fax number: +82-2-961-0622

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