information transmission between emerging market american ... · brazil, the underlying market...

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Information transmission between emerging market American Depositary Receipts (ADRs) and their underlying stocks: The case of Brazil, Russia, India and China (BRICs)

Tian Yuan1, Rakesh Gupta2, Eduardo Roca3

Abstract

This paper studies transmission dynamics of information flows between the ADR and its respective underlying stock prices in both the short run and long run. Differing from previous studies, data sets used in this study are collected from the world’s four largest emerging markets, namely Brazil, Russia, India, and China (BRICs) over recent period. By constructing value-weighted portfolios for both ADRs and their underlying stocks on a country-by-country basis, we study the price causal relationship between the two portfolios. Results for cointegration tests suggest that except China there is long-run relationship for all cases. Using a vector autoregressive (VAR) model with error-correction terms, we find that for the case of Brazil, there is bi-directional transmission of information flow between Brazilian ADRs and their underlying stocks for both short run and long run. While worldwide market information dominates the long-run transmission process, home market information prevails over the short-run information transmission. For cases of Russia and India, there exists long-run uni-directional information flow from underlying stocks to ADRs and short-run uni-directional information flow from the ADRs to their underlying stocks. Home market information dominates the long-run transmission process while worldwide market information is the primary force in the short-run transmission. Based on the VAR model, we find that there is uni-directional information flow running from ADRs to their underlying stocks over the short term; worldwide market information plays a bigger role in the information transmission.

Keywords: ADRs; BRICs; Emerging markets; information transmissions; VECM and VAR

1 Department of Accounting, Finance and Economics, Griffith Business School, Griffith University. Email: [email protected] Mobile: 0430821733 2 Department of Accounting, Finance and Economics, Griffith Business School, Griffith University. Email: [email protected] Tele: (07) 37357593 3 Department of Accounting, Finance and Economics, Griffith Business School, Griffith University. Email: [email protected] Tele: (07) 37357583

http://www.griffith.edu.au/cgi-bin/phone_search.pl?format=browse&group=Business&school=Department%20of%20Accounting,%20Finance%20and%20Economics

mailto:[email protected]





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1. Introduction

American Depositary Receipts (hereafter, ADRs) are negotiable certificates issued by one or

more US depositary banks. They represent the ownership of shares in non-U.S. companies.

Specifically, ADRs are denominated in US dollars and traded in the US stock markets in the

same manner as American stocks. Hence, ADR enables US investors to easily invest in foreign

securities without participating directly in foreign exchange and currency translation. On the

other hand, ADRs also provide a cost-efficient way for foreign companies to tap into the

world’s largest capital market.

ADRs are derivatives and have no cash flows of their own. They derive their values from the

expected cash flows of the respective underlying stocks. In theory, ADRs prices ought to be in

line with prices of their underlying stocks. In practice it appears that ADR price may

consistently deviate from the translated price of the underlying stock and market may show a

discount or premium on ADR prices (see Wahab, Lashgari and Cohn, 1992). Consequently, a

great deal of academic attention has been given to the return generating process of ADRs (See

Alaganar and Bhar, 2001; Arquette and Burdekin, 2008; Choi and Kim, 2000; Fang and Loo,

2002; He and Yang, 2011; Kim, Szakmary and Mathur, 2000; Patro, 2000).

However, our study does not attempt to investigate price determinants of ADRs or their

respective underlying stocks. 4 Rather, we focus on transmission dynamics of information flow

between ADRs and their underlying stocks by studying price interrelationship between the two

assets. Understanding information transmission between ADRs and their underlying stocks has

practical implications for asset allocation, hedging strategies (Xu and Fung, 2002), it also sheds

light into asset pricing and investment decisions.

There are two generally accepted hypotheses regarding information transmission between cross-

listed stocks or international equity markets.5 That is, the home bias hypothesis and the global

center hypothesis (Xu and Fung, 2002). According to the home bias hypothesis, domestic

market information plays a primary role in information transmission. Since cross-listed stocks in

4 The return generating process of ADRs returns is one of the major research directions in the ADR literature. There are other study groups including the law of one price and arbitrage benefits (See Grossmann, Ozuna and Simpson, 2007; Kato, Linn, and Schallheim, 1991; Maldonado and Saunders, 1983; Park and Tavakkol, 1994; Wahab and Lashgari, 1992), the effectiveness of international diversification (See Jiang, 1998; Officer and Hoffmeister, 1998; Wahab and Khandwala, 1993), global market integration and segmentation (See Alexander, Eun, and Janakiramanan, 1988; Domowitz, Glen, and Madhavan, 1997; Karolyi, 2004; Webb, Officer, and Boyd, 1995), and transmission dynamics between ADRs and their corresponding underlying stocks (See Alaganar and Bhar, 2001; Jiang, 1998; Kim, Szakmary and Mathur, 2000). 5 ADRs are similar to cross-listed stocks except that ADRs involve the creation of a depositary relationship by a third party whereas cross-listed stocks are common shares which are directly listed on a US (or other) exchange and their domestic exchange. The previous literature has widely used ADRs to study the impact of cross-listing, such as reduction in the cost of capital (see Errunza and Miller, 2000).

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different countries represent the same company, firm-specific information (i.e. earnings and

dividends announcements) is more likely to be dominated by home market factors. Accordingly,

one may expect that such information flows run from the home market to the offshore market.

In contrast, the global center hypothesis argues that international financial markets are affected

by common global factors, therefore the direction of information flows transmission between

different international markets is from a global financial center (i.e. the US market) to other

markets. Given that most ADR firms tend to be globally operated, information transmissions

between ADRs and their underlying stocks may not be intuitively obvious.

This study attempts to answer this research question: what are transmission dynamics of

information flows between emerging market ADRs and their respective underlying shares?

Specifically, there are two major research objectives of this paper. One is to explore whether

there is a long-run equilibrium relationship between ADRs and their respective underlying

stocks. Since ADRs are redundant assets, the value of which relies on expected cash flows of

their underlying stocks, it is expected that prices of ADRs are strongly correlated with those of

their stocks in long run. Based on earlier findings and intuition, we aim to investigate dynamics

of information transmission between ADRs and their underlying stocks, attempting to identify

the direction of the information transmissions between ADRs and their underlying stocks. This

will sheds light into the nature of dominant force or information flows in the transmission

process, namely whether ADRs (worldwide market information) or their respective stocks

(domestic market information) dominate the price adjustment process in cases of returning to

the equilibrium.

Prior studies in the field of price interdependence between ADRs and their corresponding stocks

are mostly conducted in developed markets (See Jiang, 1998; Kim, Szakmary and Mathur, 2000;

Alaganar and Bhar, 2001).6 The general finding is that there is a significant uni-directional

causal transmission from underlying stocks to their ADRs. Yet, as these studies concern the

ADRs originated from developed markets, their findings may not be applicable to emerging

markets. Compared with developed markets, emerging markets are less integrated with the

global markets. US investors may subject to information asymmetry due to market segmentation.

This may result in different characteristics of price interrelationship between emerging market

ADRs and their stocks over both the long run and short run. 6 Jiang (1998) studies ADRs originated from Australia, France, Japan, Netherlands, South Africa, Spain, Sweden, and the UK over the period from 1980 to 1994. Using firm level data from 21 Japanese, 21 British, 5 Dutch, 5 Swedish, and 4 Australian ADR issuing companies for the period of 1988 to 1991, Kim, Szakmary and Mathur (2000) investigate the relative importance of, and the speed of adjustment of ADR prices to three underlying factors: the price of the underlying stocks in local currency, the relevant exchange rate, and the US market index. Alaganar and Bhar (2001) study the causal relation between the prices of the Australian and the US markets. Daily prices for 24 Australian ADRs and their underlying Australian stocks over the period of 1988 to 1998 are analysed.

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Recently, there have been a few studies that look into the price interdependence between

emerging market ADRs and their underlying stocks (see Xu and Fung, 2002; Li, 2009; Hansda

and Ray, 2003). Xu and Fung (2002) investigate information flows across Chinese ADRs and

their underlying H-shares over the period of 1994-2000. They find that there is significant

mutual feedback of information between domestic (Hong Kong) and offshore (New York)

markets in terms of pricing and volatility; domestic stocks seems to plays a more significant role

of information transmission in the pricing process, whereas ADRs play a bigger role in volatility

spillover. Li (2009) studies the dynamic interrelations between ADRs and their underlying

stocks by adopting two ADR issuers: UK VOD and Brazil CVDO over the period of January

2004 to December 2004. Their results suggest that for the case of UK VOD, the ADR market

leads the price adjustment process to re-establish the long-run equilibrium; while for the case of

Brazil, the underlying market leads the price adjustment process to restore the long-run

equilibrium. In search of the view of a dominant Nasdaq and satellite Bombay Stock Exchange

(BSE), Hansda and Ray (2003) study the price interdependence of 10 Indian companies which

have floated ADR over the periods from the first day of ADR trading to the February 2002.

They find that there is bi-directional causality between the prices of these Indian ADRs and

their stocks. However, research conducted in emerging markets is relatively scattered and

inadequate. Most of these studies only focus on one market with relatively small sample size

and short period. This may lead to unreliable results.

Given the growing concern about emerging markets, this study re-visits the issue by focusing on

the four most promising emerging markets around the world. These are Brazil, Russia, India,

and China (known as the BRICs).7 Although these four countries are not geographically similar,

the group of BRICs is widely accepted in the sense that the BRICs economies have remarkably

grown during the past decades. According to Goldman Sachs’ 2005 report, ‘How solid are the

BRICs’, the four BRICs countries have continuously contributed about 28% to global economic

growth in U.S. dollar terms and 55% in Purchasing Power Parity (PPP) terms. This trend has

also been reflected by substantial increases in their equity market value: Brazil has risen by

369%, India by 499%, Russia by 630%, and China by 201% (O’Neill, 2007). From investors’

perspective, the BRICs can therefore be viewed as a group to stand for various emerging

markets around the world. To the best of our knowledge, no study has yet investigated the price

dynamics between ADRs and their underlying stocks in the context of the BRICs markets.

Therefore, our paper distinguishes itself by providing a comprehensive analysis of transmission

7 The term ‘BRIC’ was first introduced by Goldman Sachs in its Global Economics Paper, ‘Building Better Global Economic BRICs’, published on November 30, 2001.

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dynamics of information flows between ADRs from emerging markets and their underlying

stocks.

Specifically, our data include 25 Brazilian, 1 Russian, 9 Indian, and 11 Chinese ADR issuing

firms. The periods are from 29/09/2004 to 16/04/2012 for Brazil, from 27/09/2004 to

16/04/2012 for Russia, from 15/10/2003 to 16/04/2012 for India, and from 18/12/2003 to

16/04/2012 for China. Similar to Jiang (1998), we construct value-weighted portfolios for both

ADRs and their underlying stocks on a country-by-country basis. This approach will help to

alleviate the problems of non-trading and noise in individual security returns. Since we use

emerging market data, these issues are of more concern in this study. Our analysis begins with

tests for cointegration between the log-price series of ADRs and their stocks based on Johansen

procedure (Johansen, 1988, 1991; Johansen and Juselius, 1990). Transmission dynamics of

information flows will be further examined within a VAR framework.

Results for cointegration tests show that there is long-run equilibrium relationship between

ADRs and their underlying stocks for cases of Brazil, India and Russia. This is consistent with

our expectation that ADR prices are strongly correlated with prices of their underlying stocks.

There is no evidence for cointegrating relationship for the case of China. This may be because

China’s stock market was segmented in its early year. We then divide the whole sample period

into three sub-periods and test for cointegration.8 It is found that there exists cointegration for

the latter two periods, which supports our conjecture that China’s stock market was isolated and

has recently become more integrated with other international stock markets over time.

Given the presence of cointegration in data from Brazil, Russia, and India, we adopt the Vector-

Error-Correction model to study the price interdependce between the ADR portfolio and its

underlying stock portfolio for the three cases. While for the case of China, we use the Vector

Autoregressive Model (VAR) as there is no cointegrating relationship between the Chinese

ADRs and their underlying stocks. Our findings of transmission dynamics of information flows

for the BRICs are mixed. We find that for the case of Brazil there is bi-directional information

transmissions between Brazilian ADRs and their stocks in both the short run and long run.

While worldwide market information plays a primary role in transmitting information over the

long term, home market information dominates the short-run information transmission. For

cases of Russia and India, only underlying stocks adjust to restore the long-run equilibrium,

suggesting that home market information plays a bigger role in information transmission in long

term. Whereas, the direction of short-run information transmission is from ADRs to underlying

8 The whole sample period is divided into three roughly equal periods. That is, 15/10/2003-1/5/2006, 2/5/2006-1/10/2008, 2/10/2008-16/04/2012.

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stocks, indicating that worldwide market information prevails over the short-run transmission of

information flows. Short-run dynamics for the case of China are similar to those for Russia and

India. We find that there is short-run uni-directional information transmission running from

ADRs to Chinese underlying stocks, which indicates that worldwide market information plays a

primary role in the transmission process.

Our study contributes to the existing literature by providing evidence from the four emerging

markets, namely Brazil, Russia, India, and China (BRICs). To the best knowledge of the author,

this is the first paper in the ADR literature that considers the BRICs as a representative of

various emerging markets around the world. Hence, our findings may be applicable to other

emerging markets. Secondly, our analysis updates previous findings by using the latest data

which generally cover from 2003 to 2012. Thirdly, our analysis offers a better understanding of

transmission dynamics of information flows between emerging market ADRs and their stocks

by identifying the direction of the information transmission and the dominant information forces

in the transmission process. Our results also have practical implications for asset allocation,

hedging strategies, asset pricing and investment decisions for international investors.

The rest of the study is organised as follows. Section 2 covers the data and preliminary study of

stationarity and cointegration, while section 3 discusses the empirical model. Section 4 reports

empirical results. Finally, section 5 concludes.

2. Data and Preliminary Analysis

This paper investigates the short-run and long-run dynamic relationships between the BRICs

ADR portfolios and their respective underlying stock portfolios. Data are collected from the

world’s largest four emerging markets, namely Brazil, Russia, India, and China (BRICs).

Specifically, our sample includes 25 Brazilian, 1 Russian, 9 Indian, and 11 Chinese ADRs

issuing firm.9 Daily closing prices for ADRs and their corresponding foreign stocks, as well as

the daily exchange rate for each of the BRICs countries are obtained from Datastream.10 Unlike

prior research that uses firm-specific data, we adopt a portfolio approach. As discussed

previously, portfolio approach can help to mitigate the problems of non-trading and noise

trading for individual ADR. We construct value-weighted portfolios for both ADRs and their

9 Only ADRs that are listed on US exchanges (including the NYSE, NASDAQ, and Amex) are used in this paper. We exclude the ADRs that are traded Over-the-Counter (OTC) as these ADRs generally are relatively illiquid and have minimal disclosures of information to the SEC. For the case of Russia, there are only two ADRs are traded on the US exchange, one is MECHEL OAO SPN.ADR (MTL), the other one is MOBL.TELSMS.OJSC (MBT). Given that the available data for the underlying stocks of MTL has a relatively short period (31/01/2009-16/04/2012), we use MBT as the representative of Russian ADRs. 10 The exchange rate is expressed as local currency to US dollar.

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underlying stocks on a country-by-country basis. 11 To make sure the sample size for each

country group is large enough and the period is not too short, we set our study periods as

follows: the data for Brazil, Russia, India, and China cover from 29/09/2004 to 16/04/2012,

27/09/2004 to 16/04/2012, 15/10/2003 to 16/04/2012, and 18/12/2003 to 16/04/2012,

respectively. Table 1 summarizes the periods for each of the BRIC countries.

[Insert Table 1 here]

Before introducing formal econometric models for testing the price dynamics between ADRs

and their respective underlying stocks, we perform preliminary study to justify the appropriate

model used in this study. These are stationarity and cointegration tests on the log-prices of ADR

and the respective underlying stock portfolios.

Firstly, this study employs Augmented Dickey-Fuller (ADF) test (Dickey and Fuller, 1979) to

examine whether the log-price series of ADRs and their corresponding stock portfolio (denoted

by UND) are stationary. Two forms of the test are performed, namely the model with intercept

and the model with intercept and trend. If the log-price series themselves are found to be non-

stationary, then we will re-test stationarity at the first difference level (price return). If the price

returns are stationary, then we can conclude that the log-price series are integrated of order one I

(1). And this is the prerequisite of co-integration test. This will tell us whether there exists a

long-run relationship between the two series. Results of ADF tests for stationarity are reported

in Table 2 and 3 below. While Table 2 presents results of ADF tests at the price level, Table 3

displays results of the tests at the first difference level.


As Table 2 shows, for all of the four countries, we do not reject the null hypothesis and

conclude that both the log-prices of ADR and the underlying stock portfolios are non-stationary

for all cases. We then perform ADF tests at the first difference level to see whether the price

returns of ADR and the underlying stock portfolios are stationary. Results are displayed in

Table 3.


We find that the price return series of both ADR and the underlying stock are stationary.

Stating alternatively, both the log-prices of ADR and underlying stock portfolios are integrated 11 The ADR portfolio for each of the BRICs simply is a value-weighted portfolio of individual ADRs from the corresponding BRIC countries. To construct the underlying stock portfolio (UND), we adjust each individual underlying stocks to its respective conversion ratio. Then we weight the underlying stocks based on its daily market value. For comparison purpose, all of the four underlying stock portfolios are expressed in US dollars.

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of order one I (1). Based on these results, we now proceed to the co-integration test. That is, we

examine if there is a long-run equilibrium relationship between the two series.

We first use the graphical approach by plotting the pair of log-price series of ADR portfolio and

its underlying stock portfolio for each of the BRICs countries. Graphs are displayed in Figure 1

below. It can be seen from Figure 1 (a) that the log-price series of Brazilian ADR portfolio and

its underlying stock portfolio nearly overlap over the sample period. For the case of Russia,

shown in Figure 1 (b), the two series overlap over the period before 2006; there exhibits ADR

premium afterwards, however, the patterns of the two series are similar. Figure 1 (c) shows that

there are consistent Indian ADR premium over the sample period; the overall patterns of the two

series are nearly the same. Finally, Figure 1(d) shows that there are Chinese ADR premium up

to 2007; afterwards, the log-price series of Chinese ADR portfolio and its underlying stock

portfolio overlap. Based on the graphical analysis, we expect that there is a long-run

relationship between the log-price series of ADR and its underlying stock for all cases.

[Insert Figure 1 here]

Next we implement formal cointegration tests based on the Johansen procedure (Johansen, 1988,

1991; Johansen and Juselius, 1990).12 Both the trace statistic and the maximal eigenvalue are

used to detect the presence of cointegration. The null hypothesis that there is no cointegration

between the two series is tested against the alternative that there exists coinegration between the

two series. If a cointegration relationship is found for our case, it implies that there exists a

long-run equilibrium relationship between the log-price series of ADR portfolio and its

respective underlying stock portfolio. In this case, arbitrage opportunities may not exist in these

markets. Then the price relationship between the ADR and its respective underlying stock can

be described by a Vector Autoregressive Model (VAR) with error-correction terms, namely

Vector-Error-Correction Model (VECM) which ties the short-run dynamics to the long-run

equilibrium. If the null hypothesis of no cointegration is not rejected, then we proceed to the

VAR model without considering such cointegrating constraints. Table 4 reports the results for

cointegration tests.


Trace statistics and its p-value presented in Table 4 suggest that except for China the null

hypothesis that there is no cointegration can be rejected at 10% significance level for Brazil and 12 Studies argue that the Johansen procedure (Johansen, 1988, 1991; Johansen and Juselius, 1990) for testing the cointegration is preferred over the Engle-Granger method (Engle and Granger, 1987) as the test allows more than one cointegrating relationship. Therefore, this study uses the Johansen method to test whether there is a cointegrating relationship in our data.

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India while at 5% for Russia. Therefore, we reject the null hypothesis and conclude that there

exists a cointegration relationship between the log-price series of ADR portfolio and its

underlying stock portfolio for these three cases. The eigenvalue confirm the presence of

cointegration in the three cases. The evidence of cointegration justifies the use of VECM to

estimate price transmissions between ADRs and their respective underlying stocks. Since we

did not find any cointegration for the case of China, we use VAR model without considering

cointegrating constraints to examine the price transmissions between the Chinese ADR portfolio

and its respective underlying stock portfolio. 13 The following subsection demonstrates the

model.

3. Empirical Model

Given the presence of cointegration relationship in data from Brazil, Russia, and India, this

paper adopts the Vector Autoregressive Model (VAR) with a cointegrating constraint to

examine the price dynamics between the ADR and its respective underlying stock for these

cases. As Enders and Siklos (2000) point out, standard models of cointegrated variables assume

linearity and symmetric adjustments. According to Engle and Granger (1987), these

cointegrated variables can be estimated under a vector error correction model (VECM). Put it

another way, if there exists cointegration between two variables, and then an error correction

model should also exit between the two variables.

Let 𝑋𝑡 = [𝑃𝑡𝑠𝑡𝑜𝑐𝑘,𝑃𝑡𝐴𝐷𝑅]′ represent a 2-dimensional I(1) time series, with one 2×1 cointegrating

vector β. Let 𝑍𝑡(𝛽 ) = 𝛽′𝑋𝑡 denotes the I(0) error correction term. A linear VECM of order L+1

can be written as

𝛥𝑋𝑡 = 𝐴′𝑋𝑡−1(𝛽 ) + 𝑢𝑡

(1)

where: 𝑋𝑡−1(𝛽 ) = [𝑍𝑡−1(𝛽 ), 1,𝛥𝑋𝑡−1,𝛥𝑋𝑡−2, … .𝛥𝑋𝑡−𝐿 ]′, which represents regressor. 𝛥 is the

first-order difference operator. A is a matrix of coefficients. The error term 𝑢𝑡 is assumed to be a

vector martingale difference sequence (MDS) with a finite covariance matrix ∑(𝑢𝑡 ,𝑢𝑡 ′ ).

𝑍𝑡−1(𝛽) = 𝛽′𝑋𝑡−1 is an I(0) error correction term. The parameters (𝛽,𝐴) are estimated by

maximum likelihood under the assumption that errors 𝑢𝑡 are iid Gaussian.

13 According to the theory, prices of ADRs and their respective underlying stocks should converge over long run. However, our findings suggest that there is no cointegration between Chinese ADRs and their respective underlying stocks over the whole sample period of 2003 to 2012. We suspect that this may be because Chinese stock market was segmented and has only recently become more integrated into the global market. We further split our sample into three sub-periods, namely 15/10/2003-1/5/2006, 2/5/2006-1/10/2008, 2/10/2008-16/04/2012 and re-test for cointegration. We find no cointegration over the 15/10/2003-1/5/2006, whereas there is cointegration over the latter two periods. The findings support our conjecture.

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To examine the price relationship between ADRs and their underlying stocks, the VECM model

is specified as follows

𝛥𝐴𝐷𝑅𝑡 = 𝜗1𝑍𝑡−1 + 𝑐1 + �𝛼1,𝑖

𝑝

𝑖=1

𝛥𝐴𝐷𝑅𝑡−1 +�𝑏1,𝑖

𝑝

𝑖=1

𝛥𝑈𝑁𝐷𝑡−1 + 𝑢1,𝑡 (2)

𝛥𝑈𝑁𝐷𝑡 = 𝜗2𝑍𝑡−1 + 𝑐2 + �𝛼2,𝑖

𝑝

𝑖=1

𝛥𝐴𝐷𝑅𝑡−1 + �𝑏2,𝑖

𝑝

𝑖=1

𝛥𝑈𝑁𝐷𝑡−1 + 𝑢2,𝑡 (3)

According to Eq. (2) and (3), ADR returns (𝛥𝐴𝐷𝑅𝑡) and its underlying stock returns (𝛥𝑈𝑁𝐷𝑡)

are simultaneously explained by the error correction term 𝑍𝑡−1 which stands for deviations from

the long-run equilibrium, the constant terms, and lagged short-term reactions to previous ADR

returns (𝛥𝐴𝐷𝑅𝑡−𝑖 ) and underlying stock returns ( 𝛥𝑈𝑁𝐷𝑡−𝑖 ). 𝜗1 and 𝜗2 represent coefficients

for the error correction term (ECT) in Eq. (2) and (3), respectively. They measure the speed of

price convergence to the long-run equilibrium. 𝑐1 and 𝑐2 are the constant terms. 𝛼1,𝑖,𝑏1,𝑖,𝛼2,𝑖,

and 𝑏2,𝑖 are short-run coefficients. Finally, 𝑢1 and 𝑢2 are the error terms. It can be seen that the

VECM model estimates both the long-term and short-term price dynamics.

For Eq. (2), there is uni-directional information flow from underlying stocks to ADRs if the set

of estimated coefficients on the lagged underlying stock returns ( 𝑏1,𝑖 ) are

statistically significantly different from zero (short run causality). And the error correction

coefficient (𝜗1) of 𝑍𝑡−1 is significant (long run causality). Similarly, uni-directional information

flow from ADRs to underlying stocks (ADRs Granger causes underlying stocks) will occur in

Eq. (3) if the set of estimated coefficients on the lagged ADRs (𝛼2,𝑖) are non-zero (short run

causality), while the error correction coefficient (𝜗2) of 𝑍𝑡−2 is significant (long run causality).

If both variables Granger cause each other, then it is said that there is a bi-directional or a two-

way feedback relationship between ADRs and their underlying stocks.

As mentioned earlier we find that there is no cointegration for the case of China. Hence, we

employ the VAR model which is specified similar as in (2) and (3) except the error correction

term 𝑍𝑡−1 = 𝑃𝑡𝐴𝐷𝑅 − 𝑃𝑡𝑠𝑡𝑜𝑐𝑘 is left out.

4. Empirical Results

This section reports the results of transmission dynamics of information flows between ADRs

and their underlying stocks for each of the BRICs countries. Our analysis is applied within a

VAR framework. According to this model, returns on ADRs and their respective underlying

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stocks are estimated simultaneously. Both short-run and long-run relationships between the two

variables are tested. While the error correction term (ECT) measures the speed at which ADR/

underlying stock returns to long run equilibrium after a deviation has occurred, coefficients of

lagged returns of ADR and underlying stock measure the short run dynamics. The empirical

results for Brazil are shown in Table 5.


As Table 5 shows, the coefficient of ECT (𝜗) is statistically significant for both ADR and

underlying stock equations, which indicates that if the price relationship deviates from the long

run equilibrium both ADRs and their respective underlying stocks will respond to the

disequilibrium. More specifically, the ECT coefficient 𝜗 is significant at 5% level in ADR

equation (Eq. (2)) while at 10% level in UND equation (Eq. (3)); the absolute value of the

coefficient 𝜗 is 0.0072 and 0.0052 for Eq. (2) and Eq. (3), respectively. This suggests that the

price adjustment process of Brazilian ADR is quicker than its respective underlying stock. As

expected, the sign of the coefficient for ECT (𝜗) is negative for both Eq. (2) and Eq. (3),

implying that returns of ADRs and their underlying stocks will be negative to restore the

equilibrium. That is, if ADRs/underlying stocks are above their equilibrium value, their prices

will be falling in the following period to return to the equilibrium.

As stated earlier, 𝛼1,𝑖, 𝑏1,𝑖, 𝛼2,𝑖 𝑎𝑛𝑑 𝑏2,𝑖 are short-run coefficients. 𝛼1,𝑖 (in Eq. (2)) and 𝛼2,𝑖 (in

Eq. (3)) are coefficients for lagged returns on ADR, while 𝑏1,𝑖 (in Eq. (2)) and 𝑏2,𝑖 (in Eq. (3))

are coefficients for lagged returns on underlying stocks. Results suggest that ADRs (underlying

stocks) returns are correlated with their own past performance and lagged returns of underlying

stocks (ADRs). This suggests that there exists bi-directional information flow running from

Brazilian ADRs to their underlying stocks with feedback over the short run. However, it appears

that the impact of ADRs on their underlying stocks is not as strong as that of underlying stocks

on their ADRs.

Our findings are different from those of Li (2009). The estimated results from the VECM model

suggest that there is bi-directional information flow between Brazilian ADRs and their

respective underlying stocks for both the short run and long run. ADRs show quicker reaction

than underlying stocks in cases of returning to equilibrium over the long term. This suggests

that worldwide market information plays a bigger role in the long-run information transmission.

The influence of short-run price changes of underlying stocks on short-run variations in ADRs

prices is found to be stronger than the other way around. This indicates that home market

information prevails over the short-run information transmission process.

12

For the case of Russia, the estimated results are reported in Table 6. It shows that the coefficient

for the ECT (𝜗 ) is statistically significant in the equation of the UND (Eq. (3)) while

insignificant in the equation of the ADR (Eq. (2)). This implies that only the underlying stocks

responds to the disequilibrium, whereas the ADR market does not adjust to discrepancies from

the long-run equilibrium. The value of the coefficient 𝜗 indicates if the price relationship

deviates from the equilibrium, the Russian stocks will adjust by 0.0073 of the ECT value in the

following period.

The estimated results from the ADR equation (Eq. (2)) show that the returns on Russian ADR

have weak correlation with its own lagged return at lag 1 to 3. However, the lagged ADR

returns are significant at lag four, five, six, and eight. The findings are similar to those of Jiang

(1998). These results indicate that the ADR portfolio tends to have long memory characteristics.

The coefficients (𝑏1,𝑖 ) for lagged returns of the underlying stocks suggest that short-run

fluctuations in prices of Russian stock have little impact on the short-run variations in its ADR

prices. However, the results for the UND equation (Eq. (3)) are quite different from those for

the ADR equation (Eq. (2)). We find that the short-run variations in the underlying stock are not

only affected by its own past performance but also by lagged returns of ADRs.

To conclude, the adjustment to the long-run equilibrium is only driven by the underlying stock

returns in the VECM. This implies that there is uni-directional information flow running from

underlying stocks to ADRs in long run; home market information plays a bigger role in the

long-run information transmission process. Additionally, we find that there is uni-directional

information flow running from ADRs to their underlying stocks over the short term. This

suggests that worldwide market information plays a dominant role in the short-run information

transmission.


Table 7 below reports the results for the case of India. Similar to the case of Russia, the

coefficient for the ECT (𝜗) appears significant only for the UND equation (Eq. (3)). This

suggests that only the respective underlying stocks (Indian stocks) adjust to discrepancies from

the long-run equilibrium. The value of the ECT coefficient (𝜗) indicates that in the next period

the corresponding Indian stocks adjust by 0.0092.

The short-run coefficients in the equation of ADR (Eq. (2)) show that there is a weak correlation

between the short-run changes in ADR prices and its own lagged returns, whereas the short-run

variations in the underlying stock prices have no impact on the ADR returns. Therefore, we

13

conclude that short-run price changes in Indian ADRs are not affected by their respective

underlying stocks. Results of UND equation (Eq. (3)) show that the short-run coefficients (𝛼2,𝑖)

for lagged ADR returns are highly significant at all lags, suggesting that the short-run changes

in the prices of the underlying stocks are significantly affected by the previous returns of ADR.

Additionally, short-run fluctuations in underlying stock prices are also correlated with its own

past performance.

Our results of Indian case are different from previous findings of Hansda and Ray (2003). We

observe uni-directional long-run transmission of information flows from underlying stocks to

their ADRs, suggesting that domestic market information plays a primary role in the

information transmission over the long term; while over the short run, there is uni-directional

information flows running from ADRs to their underlying stocks, indicating that worldwide

information dominates the short-run information transmission process.


Since we do not find cointegration between the log-price series of Chinese ADRs and their

underlying stocks over the whole sample period of 18/12/2003 to 16/04/2012, we use VAR

model to estimate transmission dynamics between the two series. Results are presented in Table

8. It shows that the Chinese ADR portfolio has weak correlation with its own past performance,

and variations in their respective underlying stock portfolio. On the other hand, the underlying

stock portfolio is significantly affected by both its own lagged returns and those of the Chinese

ADR. Based on these results, we conclude that there is short-run uni-directional information

flow running from ADRs to their Chinese stocks. This indicates that worldwide market

information plays a primary role in the short-run transmission of information flows. Our

findings are different from those of Xu and Fung (2002).


Compared with relatively consistent findings in developed markets, our results from the BRICs

are mixed. Firstly, the direction of information transmission for each of the BRICs appears

different. While we observe a two-way feedback transmission for the case of Brazil in both the

short run and long run, there is uni-directional information flows transmission for other three

cases.14 Secondly, the dominant information force in the transmission process is different in

different cases. For Brazil, we document that worldwide market information plays a dominant

role over the long term while home market information play a bigger role over the short term. In 14 Since we do not find long-run relationship between Chinese ADRs and their underlying stocks, there is no transmission of information flows between Chinese ADRs and their underlying stocks over the long term.

14

contrast, cases of Russia and India we find that for home market information is the dominant

force of the information transmission in long run whereas worldwide market information plays

more significant role in the short-run. Similarly, for the case of China home market information

prevails over the transmission over the long term while worldwide market information

dominates the short-run transmission process. Moreover, our findings for each of the BRICs are

different from those for the corresponding individual studies in the literature. This may be

because our analysis is market wide whereas previous research has focused on price

interrelation for individual data.

5. Conclusions

American Depositary Receipts (ADRs) are dollar-denominated securities issued by a US

depositary bank, which represents foreign shares held by the institution. ADRs are essentially

redundant assets which derive their value from future cash flows of their underlying stocks.

Theoretically, prices of ADRs and exchange-rate adjusted prices of underlying stocks should

converge in long run and there are no persistent price differentials between ADRs and their

stocks because of arbitrage activities. However, empirical evidence of consistent deviations of

ADR prices from their underlying stock prices seems to violate the theory. This has prompted

various empirical studies to investigate the return generating process of ADRs. Yet, the current

paper does not focus on the price discovery of ADRs or their underlying stocks. Instead, this

study lies in the field of transmission dynamics of information flows between ADRs and

underlying stocks by examining price interrelationship between the two assets.

Our study is motivated by the following reasons. Firstly, understanding information

transmissions between ADRs and their underlying stocks is important. This has significant

practical implications for asset allocation, hedging strategies, asset pricing and investment

decisions. Secondly, prior studies have focused on developed markets whereas little attention

has been given to emerging market ADRs. Since emerging markets are less integrated with the

global market, price interrelationship between emerging market ADRs and their stocks may be

different from those for developed market ADRs. In this sense, findings in developed markets

may not be applicable to other emerging markets.

There are two major research objectives of this study. One is to identify whether there exists

long run equilibrium relationship between prices of ADRs and their underlying stocks. Based on

these findings, we further investigate dynamics of information transmission between ADRs and

their underlying stocks, attempting to identify the direction of the transmission of information

15

flows between ADRs and their underlying stocks and the dominant information force in the

transmission process.

This paper re-examines the price interdependence between ADRs and their underlying stocks by

using recent data from the four largest emerging markets around the world, namely Brazil,

Russia, India, and China (BRICs). To the author’s best knowledge, this study is the first

research in the ADR literature that considers the BRICs as a representative of worldwide

emerging markets. In this sense, our findings provide recent evidence which may be applicable

to other emerging markets. Our analysis also offers a better understanding of the nature of

information transmission between emerging market ADRs and their stocks by not only

identifying the direction of transmission but also the dominant information force in the

transmission process. Finally, our findings are important with respect to asset allocation,

hedging strategies, asset pricing and investment decisions for investors.

Results of cointegration tests suggest that except China there is a long-run relationship between

ADRs and their respective underlying stocks for all cases. This is consistent with the

expectation that prices of ADRs and their underlying stocks are strongly correlated. Based on

these results, the Vector-Error-Correction Model (VECM) is adopted to investigate the

transmission dynamics of information flows between ADRs and their underlying stocks for

these cases. There is no evidence for cointegrating relationship between Chinese ADRs and

their underlying stocks. After splitting the whole sample period into three sub-periods and

retesting for cointegration, we find that there is cointegration relationship over the latter two

periods. This supports our conjecture that Chinese stock market was segmented and has recently

become more interacted with the global market. Since we did not find any cointegration for the

case of China, a Vector Autoregressive (VAR) model is used.

Our results for information transmission dynamics are mixed among the BRICs. Firstly, the

direction of information transmission is different for different cases. We observe that there is bi-

directional information flows between Brazilian ADRs and their underlying stocks for both

short run and long run, while for Russia and India there is long-run uni-directional information

flow running from underlying stocks to ADRs and short-run uni-directional information flow

running from ADRs to their stocks. Similarly, we find short-run uni-directional information

flow running from ADRs to their stocks for China.

Secondly, the dominant information force in the information transmission process appears

differently. For Brazil while worldwide market information plays a dominant role in the long-

run information transmission, home market information plays a bigger role in the short-run

16

transmission of information. Findings for cases of Russia and India are similar. We find that

home market information is the dominant force in the long-run transmission process, whereas

worldwide market information plays a primary role in the short-run information transmission.

Similarly, for China worldwide market information dominates the short-run transmission of

information.

The different results among the four countries, especially between the Brazilian and the other

three cases, may be attributed to different trading periods of their equity markets. As Bacidore

and Sofinaos (2002) report, there are less information asymmetries and higher market quality

for markets with greater overlapping trading time with the US market than those having no

overlapping trading period with the US market. Accordingly, it is reasonable to observe a two-

way transmission between ADRs and the underlying stocks for firms listed in the Brazilian

stock market since the Brazilian stock market, among other BRIC markets, is the only market

that has an overlap in trading hours with the US market.

The opening and closing prices in different markets are non-synchronous and our results may

have been influenced by the non-synchronisty of market opening and closing times. For

robustness we are working on adjusting market opening times for stock market to estimate with

synchronous price information. Despite this weakness, results of this study are valid because we

have tested the relationship between the lagged prices of underlying assets with that of the ADR

prices (and vice versa). Using lagged variable should take care of the effect of non-synchronous

data.

Transmission dynamics of information between securities can be analysed in terms of both

returns and volatilities. This study focuses on price returns as proxy to study the transmission of

information flow between emerging market ADRs and their underlying stocks. Future research

could focus on volatility spillover effect between the ADRs and their underlying stocks, which

may better characterize linkages between ADRs and their underlying stocks.

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19

Figures

Figure 1: Log-prices of ADR and the underlying stock

(a) Brazil (b) Russia

(c) India (d) China

1.6

2.0

2.4

2.8

3.2

3.6

4.0

2004 2005 2006 2007 2008 2009 2010 2011 2012

LADR LUND

1.6

2.0

2.4

2.8

3.2

3.6

4.0

2004 2005 2006 2007 2008 2009 2010 2011 2012

LADR LUND

1.6

2.0

2.4

2.8

3.2

3.6

4.0

2004 2005 2006 2007 2008 2009 2010 2011 2012

LADR LUND

3.2

3.6

4.0

4.4

4.8

5.2

2004 2005 2006 2007 2008 2009 2010 2011 2012

LADR LUND

20

Tables

Table 1: Summary of sample

Countries Sample periods The number of stocks/ADRs

Brazil 29/09/2004 to 16/04/2012 25

Russia 27/09/2004 to 16/04/2012 1

India 15/10/2003 to 16/04/2012 9

China 18/12/2003 to 16/04/2012 11

Table 2 Augmented Dickey-Fuller (ADF) unit root tests at price level Countries ADR/UND Augmented Dickey-Fulller (ADF) Tests

Intercept Intercept and trend

Brazil ADR -2.176602

(0.2151)

-2.030711

(0.5835)

UND -2.193224

(0.2090)

-2.002317

(0.5992)

Russia ADR -2.373992

(0.1493)

-2.110326

(0.5392)

UND -2.373877

(0.1494)

-2.282142

(0.4430)

India ADR -1.875750

(0.3441)

-1.951514

(0.6268)

UND -2.169296

(0.2179)

-2.266150

(0.4518)

China ADR -1.903946

(0.3307)

-2.171822

( 0.5046)

UND -1.625212

( 0.4695)

-2.292801

(0.4372)

Note: Values in parentheses are p-values. The tested null hypothesis is that the log-prices of ADRs/underlying stocks

(denoted by UND) are non-stationary. If the p-value is less than the selected level of significance, then we will reject

the null of non-stationary and conclude that the log-prices of ADRs/underlying stocks are stationary. ***/**/*

indicates that the null hypothesis of non-stationarity is rejected at 1%, 5%, and 10% level of significance, respectively.

21

Table 3 Augmented Dickey-Fuller (ADF) unit root tests at first difference level Countries ADR/UND Augmented Dickey-Fulller (ADF) Tests

Intercept Intercept and trend

Brazil ADR -35.3523***

(0.0000)***

-35.3712***

(0.0000)***

UND -41.5894***

(0.0000)***

-41.6079***

(0.0000)***

Russia ADR -14.3398***

(0.0000)***

-14.3818***

(0.0000)***

UND -44.8741***

(0.0001)***

-44.8729***

(0.0000)***

India ADR -34.3931***

(0.0000)***

-34.3938***

(0.0000)***

UND -41.3118***

(0.0000)***

-41.3204***

(0.0000)***

China ADR -51.7856***

(0.0001)***

-51.7803***

(0.0000)***

UND -47.4985***

(0.0001)***

-47.4926***

(0.0000)***

Note: Values in parentheses are p-values. The null hypothesis of non-stationary is tested at the first difference level.

If the p-value is less than the selected level of significance, then we will reject the null of non-stationary and conclude

that the price returns of ADRs/underlying stocks (denoted by UND) are stationary. ***/**/* indicates that the null

hypothesis of non-stationary is rejected at 1%, 5%, and 10% level of significance, respectively.

Table 4 Johansen cointegration tests for the log-price series of ADR and its underlying stock Countries Trace Statistic Eigenvalue

Brazil 3.4308*

(0.0640)*

0.0017*

(0.0640)*

Russia 5.8205**

(0.0158) **

0.0026**

(0.0158)**

India 3.2857*

(0.0699)*

0.0016*

(0.0699)*

China 1.2102

(0.2713)

0.0005

(0.2713)

Note: ***/**/* indicates that the rejection of the null hypothesis that there is no cointegrating relationship among the varaible at the 1%, 5%, and 10% level of significance, respectively.

22

Table 5 Brazil: VECM estimations for log-prices of ADR and UND portfolios The linear VECM model is given as follows


𝑝

𝑖=1

𝛥𝐴𝐷𝑅𝑡−𝑖 + �𝑏1,𝑖

𝑞

𝑖=1

𝛥𝑈𝑁𝐷𝑡−𝑖 + 𝑢1,𝑡 (2)


𝑟

𝑖=1


𝑠

𝑖=1


As can be seen, returns are a function of the error correction term (ECT) which is denoted by 𝑍𝑡−1, the constant term

(c), lagged returns of ADR and its underlying stock (𝛥𝐴𝐷𝑅𝑡−𝑖 and 𝛥𝑈𝑁𝐷𝑡−𝑖), and the error term (𝑢). The absolute

value of the coefficient for ECT describes how quickly the equilibrium is restored. The sign of the coefficient

indicates the direction of the price adjustment. Short-run dynamics are measured by coefficients for lagged returns.

The estimated results are reported as follows. The first column presents the coefficient for ECT (𝜗), the constant term

(c), and coefficients for lagged returns of ADR (𝛼𝑖) and UND (𝑏𝑖). The second and third columns display the

estimated results for Eq. (2) and (3), respectively.

Dependable variable 𝛥𝐴𝐷𝑅𝑡 𝛥𝑈𝑁𝐷𝑡

ECT (𝜗) -0.0072**

(0.0277)**

-0.0052*

(0.0613)*

Constant (c) 0.0007

(0.2192)

0.0005

(0.3217)

𝛥𝐴𝐷𝑅𝑡−1 -0.2749***

(0.0000)***

0.4560***

(0.0000)***

𝛥𝐴𝐷𝑅𝑡−2 -0.5015***

(0.0000)***

0.0719

(0.1919)

𝛥𝐴𝐷𝑅𝑡−3 -0.1516**

(0.0313)**

0.1728***

(0.0037)***

𝛥𝐴𝐷𝑅𝑡−4 -0.1912***

(0.0065)***

0.0906

(0.1269)

𝛥𝐴𝐷𝑅𝑡−5 -0.2324***

(0.0009)***

0.1218**

(0.0395)**

𝛥𝐴𝐷𝑅𝑡−6 -0.2214***

(0.0017)***

0.0374

(0.5296)

𝛥𝐴𝐷𝑅𝑡−7 -0.1406**

(0.0309)**

-0.0038

(0.9442)

𝛥𝐴𝐷𝑅𝑡−8 0.0188

(0.7203)

0.0125

(0.7781)

𝛥𝑈𝑁𝐷𝑡−1 0.3198***

(0.0000)***

-0.3769***

(0.0000)***

𝛥𝑈𝑁𝐷𝑡−2 0.4498***

(0.0000)***

-0.0929

(0.1237)

𝛥𝑈𝑁𝐷𝑡−3 0.1288*

(0.0882)*

-0.2026***

(0.0015)***

23

𝛥𝑈𝑁𝐷𝑡−4 0.2067***

(0.0060)***

-0.0611

(0.3360)

𝛥𝑈𝑁𝐷𝑡−5 0.2287***

(0.0022)***

-0.1240**

(0.0499)**

𝛥𝑈𝑁𝐷𝑡−6 0.1872**

(0.0126)**

-0.0594

(0.3488)

𝛥𝑈𝑁𝐷𝑡−7 0.0868

(0.2138)

0.0045

(0.9390)

𝛥𝑈𝑁𝐷𝑡−8 -0.0474

(0.3796)

-0.0361

(0.4287)

Note: Values in parentheses are p-value. ***/**/* represent significant at 1%, 5%, and 10% level, respectively.

24

Table 6 Russia: VECM estimations for log-prices of ADR and UND portfolios The linear VECM model is given as follows


𝑝

𝑖=1


𝑞

𝑖=1



𝑟

𝑖=1


𝑠

𝑖=1



(c), lagged returns of ADR and its underlying stock (𝛥𝐴𝐷𝑅𝑡−𝑖 and 𝛥𝑈𝑁𝐷𝑡−𝑖), and the error term (𝑢). The absolute

value of the coefficient for ECT describes how quickly the equilibrium is restored. The sign of the coefficient

indicates the direction of the price adjustment. Short-run dynamics are measured by coefficients for lagged returns.

The estimated results are reported as follows. The first column presents the coefficient for ECT (𝜗), the constant term

(c), and coefficients for lagged returns of ADR (𝛼𝑖) and UND (𝑏𝑖). The second and third columns display the

estimated results for Eq. (2) and (3), respectively.


ECT (𝜗) 0.0002

(0.9620)

0.0073**

(0.0386)**

Constant (c) 0.0004

(0.4783)

0.0001

(0.6841)

𝛥𝐴𝐷𝑅𝑡−1 -0.0105

(0.6895)

0.3982***

(0.0000)***

𝛥𝐴𝐷𝑅𝑡−2 -0.0243

(0.4231)

0.1836***

(0.0000)***

𝛥𝐴𝐷𝑅𝑡−3 0.0330

(0.2880)

0.1003***

(0.0000)***

𝛥𝐴𝐷𝑅𝑡−4 0.0684**

(0.0267)**

0.1292***

(0.0000)***

𝛥𝐴𝐷𝑅𝑡−5 -0.1094***

(0.0004)***

0.0314

(0.1414)

𝛥𝐴𝐷𝑅𝑡−6 -0.0713**

(0.0218)**

0.0607***

(0.0046)***

𝛥𝐴𝐷𝑅𝑡−7 -0.0485

(0.1134)

-0.0083

(0.6921)

𝛥𝐴𝐷𝑅𝑡−8 -0.1234***

(0.0000)***

-0.0300

(0.1260)

𝛥𝑈𝑁𝐷𝑡−1 -0.0399

(0.2943)

-0.3099***

(0.0000)***

𝛥𝑈𝑁𝐷𝑡−2 -0.0465

(0.2411)

-0.0956***

(0.0005)***

𝛥𝑈𝑁𝐷𝑡−3 -0.1032***

(0.0091)***

-0.1045***

(0.0001)***

25

𝛥𝑈𝑁𝐷𝑡−4 -0.0260

(0.5080)

-0.1226***

(0.0000)***

𝛥𝑈𝑁𝐷𝑡−5 0.0433

(0.2704)

0.0121

(0.6541)

𝛥𝑈𝑁𝐷𝑡−6 0.0674*

(0.0847)*

-0.0087

(0.7447)

𝛥𝑈𝑁𝐷𝑡−7 -0.0146

(0.6980)

0.0307

(0.2366)

𝛥𝑈𝑁𝐷𝑡−8 0.0449

(0.1710)

-0.0060

(0.7892)


26

Table 7 India: VECM estimations for log-prices of ADR and UND portfolios

The linear VECM model is given as follows


𝑝

𝑖=1


𝑞

𝑖=1



𝑟

𝑖=1


𝑠

𝑖=1



(c), lagged returns of ADR ADR and its underlying stock (𝛥𝐴𝐷𝑅𝑡−𝑖 and 𝛥𝑈𝑁𝐷𝑡−𝑖), and the error term (𝑢). The

absolute value of the coefficient for ECT describes how quickly the equilibrium is restored. The sign of the

coefficient indicates the direction of the price adjustment. Short-run dynamics are measured by coefficients for

lagged returns. The estimated results are reported as follows. The first column presents the coefficient for ECT (𝜗),

the constant term (c), and coefficients for lagged returns of ADR (𝛼𝑖) and UND (𝑏𝑖). The second and third columns

display the estimated results for Eq. (2) and (3), respectively.


ECT (𝜗) 0.0029

(0.4806)

0.0093***

(0.0034)***

Constant (c) 0.0005

(0.2989)

0.0007*

(0.0558)*

𝛥𝐴𝐷𝑅𝑡−1 -0.0645**

(0.0142)** 0.3378***

(0.0000)*** 𝛥𝐴𝐷𝑅𝑡−2 -0.0503*

(0.0837)* 0.1674***

(0.0000)*** 𝛥𝐴𝐷𝑅𝑡−3 0.0159

(0.5912) 0.1169***

(0.0000)*** 𝛥𝐴𝐷𝑅𝑡−4 0.0483

(0.1070) 0.0865***

(0.0002)*** 𝛥𝐴𝐷𝑅𝑡−5 -0.0033

(0.9101) 0.0580**

(0.0117)** 𝛥𝐴𝐷𝑅𝑡−6 -0.0123

(0.6766) 0.0546**

(0.0160)** 𝛥𝐴𝐷𝑅𝑡−7 -0.0062

(0.8248) 0.0423**

(0.0493)** 𝛥𝑈𝑁𝐷𝑡−1 -0.0285

(0.3985) -0.1839***

(0.0000)*** 𝛥𝑈𝑁𝐷𝑡−2 -0.0179

(0.5999) -0.1347***

(0.0000)*** 𝛥𝑈𝑁𝐷𝑡−3 -0.0529

(0.1236) -0.1167***

(0.0000)*** 𝛥𝑈𝑁𝐷𝑡−4 -0.0384

(0.2637) -0.0310 (0.2405)

27

𝛥𝑈𝑁𝐷𝑡−5 -0.022

(0.5131) -0.0920***

(0.0005)***

𝛥𝑈𝑁𝐷𝑡−6 0.0004

(0.9892) -0.0955***

(0.0002)*** 𝛥𝑈𝑁𝐷𝑡−7 -0.0018

(0.9505)

-0.0465**

(0.0463)**


28

Table 8 China: VAR estimations for log-prices of ADR and UND portfolios The VAR model is given as follows

𝛥𝐴𝐷𝑅𝑡 = 𝑐1 + �𝛼1,𝑖

𝑝

𝑖=1


𝑞

𝑖=1


𝛥𝑈𝑁𝐷𝑡 = 𝑐2 + �𝛼2,𝑖

𝑟

𝑖=1


𝑠

𝑖=1


As can be seen, returns are a function of the constant term (c), lagged returns of ADR and its underlying stock

(𝛥𝐴𝐷𝑅𝑡−𝑖 and 𝛥𝑈𝑁𝐷𝑡−𝑖), and the error term (𝑢). Short-run dynamics are measured by coefficients for lagged returns.

The estimated results are reported as follows. The first column presents the constant term (c), and coefficients for

lagged returns of ADR (𝛼𝑖) and UND (𝑏𝑖). The second and third columns display the estimated results for Eq. (2) and

(3), respectively.

Dependable variable ΔADRt ΔUNDt

Constant (c) 0.0004

(0.3599)

0.0005

(0.1518)

𝛥𝐴𝐷𝑅𝑡−1 -0.1400*** (0.0000)***

0.4775*** (0.0000)***

𝛥𝐴𝐷𝑅𝑡−2 -0.0550 (0.1140)

0.2865*** (0.0000)***

𝛥𝐴𝐷𝑅𝑡−3 0.020631 (0.5734)

0.1996*** (0.0000)***

𝛥𝐴𝐷𝑅𝑡−4 0.0167 (0.6521)

0.1307*** (0.0000)***

𝛥𝐴𝐷𝑅𝑡−5 -0.0531 (0.1512)

0.0330 (0.2345)

𝛥𝐴𝐷𝑅𝑡−6 0.0116 (0.7446)

0.0609** (0.0229)**

𝛥𝐴𝐷𝑅𝑡−7 -0.0352 (0.2600)

-0.0172 (0.4618)

𝛥𝑈𝑁𝐷𝑡−1 0.0715* (0.0559)*

-0.4342*** (0.0000)***

𝛥𝑈𝑁𝐷𝑡−2 -0.0006 (0.9870)

-0.2730*** (0.0000)***

𝛥𝑈𝑁𝐷𝑡−3 -0.0206 (0.6271)

-0.2115*** (0.0000)***

𝛥𝑈𝑁𝐷𝑡−4 0.0107 (0.8018)

-0.1163*** (0.0003)***

𝛥𝑈𝑁𝐷𝑡−5 -0.0153 (0.7161)

-0.0658** (0.0374)**

𝛥𝑈𝑁𝐷𝑡−6 -0.0550 (0.1693)

-0.0729** (0.0153)**

𝛥𝑈𝑁𝐷𝑡−7 0.0645** (0.0480)**

0.0812*** (0.0009)***


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