Dependency on FDI Inflows and Stock Market Linkages

Dependency on FDI Inflows and Stock Market Linkages

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Dependency on FDI Inflows and Stock Market Linkages Dinh-Tri Vo PII: DOI: Reference:

S1544-6123(19)31368-6 https://doi.org/10.1016/j.frl.2020.101463 FRL 101463

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Finance Research Letters

Received date: Revised date: Accepted date:

3 December 2019 10 February 2020 12 February 2020

Please cite this article as: Dinh-Tri Vo, Dependency on FDI Inflows and Stock Market Linkages, Finance Research Letters (2020), doi: https://doi.org/10.1016/j.frl.2020.101463

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Highlights • This paper discovers the dependence of aheavily net FDI receiver to her major investors regarding the stock index

• Copulas approaches are used to detect the dynamic linkages • The linkage strength depends on the position of FDI inflows and the changes of FDI inflows. • The dependenceto FDI investor could be only in positive extreme, only negative extreme, or both.

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Dependency on FDI Inflows and Stock Market Linkages Dinh-Tri Vo IPAG Business School Paris, France & University of Economics Hochiminh City, Vietnam

Abstract For many emerging economies, FDI inflows are important sources of development though the spillover effects are heterogeneous. The interdependence between stocks markets both regional and global are found more and more evidently. This paper aims to discover the dependence of a heavily net FDI receiver, Vietnam, to her major investors regarding the stock index. To detect the dynamic linkages between indices, several copulas were employed. The findings figure out that the linkages depends on the position of FDI inflows and the changes of FDI inflows. The dependence to FDI investor could be only in positive extreme, only negative extreme, or both. Keywords: FDI inflow, Stock market, Dynamic linkages 1. Introduction For many developing countries, FDI represents a crucial source of growth and development. In the sense that FDI enhances growth, Hansen and Rand (2006) find that FDI has both lasting and long-run effects on GDP. However, the spillovers effect of FDI in recipient countries are highly heterogeneous (Amendolagine et al., 2019). This could be a consequence that literature on FDI is extensive and still attracting scholars. There is a vast number of papers focusing on the determinants of FDI flows and the causality between FDI and economic growth. Meanwhile, studies on the nexus between FDI and financial markets, especially stock market account just a tiny proportion in this research stream. Among few, Soumaré and Tchana Tchana (2015) document that there is a bidirectional causality between FDI and financial market development, notably the case of African countries. According to Anagnostopoulos et al. (2019), cross-country stock market correlations have increased significantly in the past three decades. In fact, numerous of papers conclude that interdependence between financial markets exhibits at both regional and global scope (Chien et al., 2015, Mensi et al., 2017, Chevallier et al., 2018, Caporale et al., 2019, Mohti et al., 2019). Previous

Preprint submitted to Financial Research Letters

February 17, 2020

studies point out that trade intensity drives the linkage between stock markets of trade partners (Balli et al., 2015, Paramati et al., 2018). If trade linkages matter for stock market interdependence, one could also question whether FDI dependency has the same pattern. However, answering this question is not easy. The fact is that countries depending much on FDI inflows are poor or small ones. Consequently, empirical data on stock markets are not available or very limited. Vietnam is one of the economies that has highest ratio of FDI net inflows over GDP. Figure 1 shows that since 1999, Vietnam always has this ratio higher than average of other groups. After joining WTO in 2007 and several FTAs, net FDI inflows over GDP ratio surged to 10% in 2008, and keep over 5% until now. In term of value, accumulated invested FDI capital at the end of 2019Q1 is about 196 bil. USD or 80% of the current GDP. The dependency of Vietnam on FDI exposes in both total investments and export of Vietnam which accounts for 70%. According to McLaren and Yoo (2017), this country is an extremely interesting one for measuring the effects of trade and foreign investment because of its rapid transition from a relatively closed centrally-planned economy to a very open market-based economy.

Figure 1: Global FDI net inflows, %GDP

Figure 2: FDI inflows to Vietnam

Among the top FDI investors to Vietnam, there are six countries/jurisdictions from Asia: China, Hong Kong, Korea, Japan, Singapore, and Taiwan which is presented in Figure 2. In recent years, Japan and Korean are the top FDI investors in Vietnam. But the aggregated FDI inflows from China, Hong Kong and Singapore are remarkable. Following the recent work of Anagnostopoulos et al. (2019) who establish the relationship between FDI positions and cross-country stock market correlations both theoretically and empirically, this paper aims to explore the linkages between the Vietnamese stock market and her important FDI investors. The rest of the paper is organized as follows: In the next section, I describe data and statistical method; then, empirical results are discussed. The final section is reserved for conclusions and implications. 3

Table 1: Descriptive statistics

Index

n

mean

sd

min

max

skew

kurtosis

se

Vietnam

4959

0.0005

0.0144

-0.0766

0.0666

-0.2910

3.5941

0.0002

Korea

4959

0.0002

0.0138

-0.1280

0.1128

-0.5324

7.4808

0.0002

Japan

4959

0.0001

0.0145

-0.1211

0.1323

-0.4051

7.0415

0.0002

Singapore

4959

0.0001

0.0108

-0.0870

0.0753

-0.1868

5.7665

0.0002

Taiwan

4959

0.0001

0.0128

-0.0994

0.0652

-0.3204

4.3184

0.0002

Hong Kong

4959

0.0001

0.0140

-0.1358

0.1341

-0.0442

9.1549

0.0002

China

4959

0.0001

0.0151

-0.0926

0.0940

-0.3935

5.4001

0.0002

2. Data and Statistical method 2.1. Data To discover the linkages between the Vietnamese stock index and top FDI investors to Vientnam, the study gets the stock price index of each economy from Eikon Thomson Reuteurs as follows: .N225 for Japan, .KS11 for Korean, .STI for Singapore, .TWII for Taiwan, .HSI for Hong Kong, .SSEC for China, and .VNI for Vietnam. Take into account the fact that the Vietnamese stock indices started in 20/07/2010, the time-frame of the sample is from 20/07/2010 to 01/08/2019. The descriptive statistics from Table 1 and Figure 3 show that all of the indices have left tail skewness and peak kurtosis during the studied period. These results suggest the possibility of extreme value dependencies. 2.2. Copula Approach In order to investigate the possible contagion effect and interdependence of other stock market on Vietnamese stock market, we use Copulas to model average and tail dependence and examine the transmission and contagion in term of values. An effect could be defined as a couple that is negatively correlated (safe hedging property) or positively correlated. A copula is a function which links univariate marginal to the multivariate distribution for which the marginal distribution of each variable is uniform. Let X, Y are continuous random variables follow a distribution function F (X, Y ) and FX (X), FY (Y ) are their marginal distributions respectively. Therefore, U = FX (X) ∈ [0, 1] and V = FY (Y ) ∈ [0, 1]. Thus the copula C is the

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Figure 3: Distribution of Indices

distribution function of (U, V ) as bellow: C(u, v) = P (U ≤ u, V ≤ v) = P (X ≤ FX−1 (u), Y ≤ FY−1 (v))

(1)

Let F be a bivariate distribution function with F1 and F2 are the margins. Thus there exists a copula C such that: F (X, Y ) = C(F1 (X) , F 2 (Y ))

(2)

Furthermore, C is unique if the margins F1 and F2 are continuous. Likewise, if C is a copula, then for any u1 [0, 1], the partial derivative

0≤

∂C ∂u

exists for all u2 [0, 1] . For such u1 and u2 there are:

∂C(u1 , u2 ) C(u1 , u2 ) ≤ 1 and 0 ≤ ≤1 ∂u1 ∂u2

(3)

Moreover, if the copula is sufficiently differentiate, then the copula density may be as follows:

c(u1 , u2 ) =

∂C (u1 , u2 ) ∂u1 ∂u2

(4)

In case of an absolutely continuity of bivariate distribution, the copula density can be repre-

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sented as follows:

c(u1 , u2 ) =

f (F1−1 (u1 ) , F2−1 (u2 ))
f1 F1−1 (u1 ) × f2 (F2−1 (u2 ))

(5)

where f1 and f2 denote the marginal densities of variables x and y, respectively. An appealing feature of the copula is tail dependence, which measures the probability that two variables are in the lower or upper joint tails of their bivariate distribution. The coefficient of upper (right) and lower (left) tail dependence for two random variables X and Y is obtained from the copula as: λU = LimU →1 P r(Y > F2−1 (u)/X > F1−1 (u)) = LimU →1

1 − 2u + C(u, u) 1−u

λL = LimU →0 P r(Y ≤F2−1 (u)/X≤F1−1 (u)) = LimU →0

C(u, u) u

(6)

(7)

Where λU , λL ∈ [0, 1]. Lower (upper) tail dependence means that λL > 0 (λU > 0) indicates a

non-zero probability of observing an extremely small (large) value for one series together with on extremely small (large) value for another series. 1. The Gaussian (Normal) copula is given by: CG (u1 , u2 , ρ) = ΦN (Φ−1 (u1 ), Φ−1 (u2 ))

(8)

With Φ−1 is the inverse cumulative distribution of N (0, 1), Φ is the cumulative distribution function and ρ is the covariance matrix. 2. The Student copula is a copula associated to the bivariate t-distribution. It’s defined as follows: −1 Ct (u1 , u2 , ρ, ϑ) = tρ,ϑ (t−1 ϑ (u1 ), tϑ (u2 )

(9)

where t−1 ρ,ϑ is the standard distribution with ϑ degrees of freedom; ρ is the correlation and t−1 ϑ is the inverse of the univariate standard student t-distribution function. However, when ϑ → ∞ : the student copula tends to the normal copula which doesn’t authorize the tail dependence.

3. The Gumbel copula was proposed by Gumbel (1960) it is called too Gumbel-Hougaard copula. Its expression defined by: Cθ (u1 , u2 ) = exp((−ln (u1 ))θ + (−ln (u1 ) )θ )1/θ , 1≤θ < ∞ 6

(10)

where the copula parameter θ ≥ 1. It is asymmetric with upper tail dependence: λU = 2- 21/θ and lower tail independence (λL = 0).

4. The Clayton copula was introduced by Clayton (1978) and takes the following copula form: −1/θ

Cθ (u1 , u2 ) = (u1 −θ + u2 −θ − 1)

, θ[0, ∞]

(11)

It is asymmetric with lower tail dependence: λL = 2−1/θ and upper tail independence (λU =0). The study uses the Canonical Maximum likelihood (CML) to estimate the parameter of the copula. We follow the methodology stated in Bedoui et al. (2018). Firstly, we estimate the margins using empirical distribution: T 1X 1I {Xi x} Fbi (x) = T t=1

∀I = 1, ...., p

where I is the indicator function. Then we apply the CML method. The procedure of this in two steps: 1. Transformation of the initial sample set into uniform variables, using the empirical marginal distribution: c t c t ubt = (c u1 , ...., uc N ) = (F1 (x1 ), ..., FN (xN ))

(12)

2. Estimation of the copula parameters via the following relation:

3. Empirical results

α bCLM = argmax

T X t=1

LnC(c u1 , .... , uc N , α)

(13)

In this section, we present the patterns of dependency between the Vietnamese stock index and others. We firstly recall the main differences between each kind of copulas. Normal and Student copulas are symmetric where Gumbel and Clayton are asymmetric. This means that the dependencies could be non-homogeneous in term of positive and negative extreme values for the Clayton and Gumbel copulas. The Student copula allows higher heavy tails than Normal copula. Gumbel Copula allows more dependence in positive extreme values and Clayton in negative extreme values. But interestingly, all of the employed copulas present a positive dependence between Vietnam and other countries. When we look at the dependencies in further details, the linkages between Vietnamese stock index and others have not only a strong link in common but also have specific relation regarding 7

Figure 5: Taiwan-Vietnam Figure 4: Korea-Vietnam

Figure 6: Hong Kong-Vietnam Figure 7: Singapore-Vietnam

extreme values. These differences can be categorized into 3 groups: Korea-Taiwan-Hong Kong; Singapore; and China-Japan. In the first group, Figure 4, 5, and 6 show a high level of dependency regarding the Normal copula parameter and T copula which argue in favor of a symmetric dependency. Regarding the pair Korea-Vietnam, we observe that this dependency increased since 2016. A fall in this dependency in March-May 2007 is also visible. This could be understandable with the fluctuation of FDI inflows from Korean during these period. More interesting, results from Joe-Clayton copula indicate that the dependence appear in both positive and negative extreme most of the time. This means that an extreme increase or decrease in Korean stock market will have lead a similar consequence in Vietnamese market.

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Figure 8: Japan-Vietnam Figure 9: China-Vietnam

In case of Taiwan, we observe from Figure 5 a significant level of dependency regarding the Normal copula parameter with an average of 0.8 which argues in favor of a symmetric dependency between Vietnam and Taiwan. Furthermore, the results from Joe-Clayton copula is quite similar with the case of Korean. Interestingly, this is the only case that extreme positive dominate extreme negative in most of the time. This means that extreme increase in Taiwanese market have more influence to Vietnamese market. With the case of Hong Kong-Vietnam, Figure 6 shows a significant level of dependency regarding the Normal copula parameter which argues in favor of a symmetric dependency. Moreover, we observe that this dependency increased since 2015. A fall in this dependency in March-May 2007 is also visible. Moreover, the Joe-Clayton copula illustrate the dominance of dependence in negative values. This pattern suggests that extreme negative in Hong Kong leads significantly downturn of Vietnamese market. The positive dependence was found mostly during 2002-2006, 2008-2010, and since 2016. The second group includes only Singapore. We can see from Figure 7 a significant level of dependency regarding the Normal copula parameter with an average of 0.8 which argues in favor of a symmetric dependency between Vietnam and Singapore. The results obtained from Joe-Clayton copula shows a dependence in extreme positive between the two markets. However, this dependency via Joe-Clayton is not clear. The third group includes two opposite cases: China and Japan. For the case of Japan-Vietnam, we observe from Figure 8 a significant level of dependency regarding the Normal copula parameter 0.7 and T-copula parameter between 0.7 − 0.85 which argues in favor of a positive dependency of 9

stock market between Vietnam and Japan. Especially, there are two periods that show interesting patterns from Clayton and Joe-Clayton copula. During 2009-2013, the positive dependence found in both positive and negative values. Meanwhile, the dependence is more pronounced in cases of extreme negative. This finding implies that a drawdown in Japanese index would lead to a similar phenomena in Vietnam. Since 2013, the dependency in confirmed in negative values with Clayton copula. Taking into account the FDI inflows from Japan to Vietnam, we can see that the flows are only increasing during the period 2009-2013. The FDI from Japan then decreased in 2014, 2015 and then increased strongly since then. Meanwhile, patterns from Figure 9 show interesting results. As for Japan, the dependency between Vietnam and China is visible with a Normal copula parameter equal in average to 0.75. But now this dependency follows a Gumbel copula density which argues in favor of more dependence in positive extreme values. This behavior appears occasionally in 2007, 2009, 2010, 2013 and on all time between 2015 and 2017. This interesting pattern advocates that the Vietnamese index is more heuristic with an strong increase in Chinese market. As the evidence of stock market linkages in a region is clear, the paper checks the strength of dependency with also the ASX200 and the Nifty50. Results show that the dependency exists, but it is not clear, as the case of Singapore. Furthermore, obtained results reveal a structural break in the conditional copula following the 2007-2008 crisis as well as the important changes in FDI positions. 4. Concluding remarks Vietnam has experienced tremendous inflows of FDI recently from Taiwan, China, Japan, Hong Kong, Singapore, and Korea Ni et al. (2017). The impacts of FDI on Vietnamese economy during the last two decades are very strong and significant (McCaig and Pavcnik, 2013, 2018). Not only changing the structure of the economy and the evolution of local firms, FDI inflows also create linkages between these stock markets. In fact, the fluctuation of FDI investors’ stock price would have a impact of the local listed firms. Consequently, there would be linkages between Vietnam stock index and others. In this paper, we apply different copulas to detect the dependency of Vietnamese Index with the top 6 FDI investors in Vietnam. The obtained results demonstrate that there is a strong dependency between Vietnam and these economies. Furthermore, the linkages in details reveal different types of dependency. In general, there are three group of dependency: both extreme 10

positive and negative in case of Taiwan, Korea, and Hong Kong; not clear in extreme value regarding the case of Singapore; and dominated extreme positive with China whereas extreme negative with Japan. The difference in dependency can be extended as a further research. However, from the economic sense, we can hypothesize that it depends on the trading partnership. For Vietnam, Taiwan, Korea and Hongkong are less important than Japan and China in terms of exporting and importing. Regarding Singapore, though this is an important FDI investors of Vietnam, the fact is that most of Singapore companies are offshore ones. The findings of this paper contribute to the new research topic on FDI positions and crosscountry stock market correlation Anagnostopoulos et al. (2019) with a special case that highly depends on FDI inflows. Furthermore, the implications of this study can benefit international investors investing in Vietnam stock market. Moreover, the local policy makers must be aware of the FDI inflow positions and their changes. The specific relation with FDI partner will create a different dependency between stock market movements. There could be only extreme positive, only extreme negative, or both. The policy makers must pay attention to the linkage with economy that has larger upside and downside risk, i.e. China. Moreover, the attention also kept at important FDI-trading partners. For a further research, this question could be looked at the specific sectors of FDI investors and the host country. References Amendolagine, V., Presbitero, A. F., Rabellotti, R., and Sanfilippo, M. (2019). Local sourcing in developing countries: The role of foreign direct investments and global value chains. World Development, 113:73–88. Anagnostopoulos, A., Atesagaoglu, O., Faraglia, E., and Giannitsarou, C. (2019). Foreign direct investment as a determinant of cross-country stock market comovement. Balli, F., Balli, H. O., Louis, R. J., and Vo, T. K. (2015). The transmission of market shocks and bilateral linkages: Evidence from emerging economies. International Review of Financial Analysis, 42:349–357. Bedoui, R., Braeik, S., Goutte, S., and Guesmi, K. (2018). On the study of conditional dependence structure between oil, gold and usd exchange rates. International Review of Financial Analysis, 59:134 – 146.

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