Have the Stock Connect Programs Improved Information Transmission

Have the Stock Connect Programs Improved Information

Transmission and Price Discovery of Chinese A Shares?

Jing Chen* (corresponding author) Cardiff University, Senghennydd Road, Cardiff CF24 4AG, UK

Qian Guo Birkbeck College, University of London, London WC1E 7HU, UK

Tapas Mishra University of Southampton, Southampton SO14 0DA, UK

Jiali Zhu University of Southampton, Southampton SO14 0DA, UK

Abstract

This study characterizes the dynamics of price discovery of Chinese A shares under the important and classic 2019 Shanghai–London Stock Connect program and wider regulatory impact. We draw comparisons with the Shanghai-Hong Kong Stock Connect program to better understand how the core message from these different regulatory regimes influence both information transmission and price formation. We use intraday open and close prices of the Shanghai Stock Exchange Composite Index (SSEC), Hong Kong Hang Seng Index (HSI), and the FTSE 100 Index (UKX) that are continuously recorded at five-minute intervals from October 12th, 2018, until July 26th, 2019. We then employ Vector Autoregression method and exploit properties of impulse-responses, variance decomposition, Kroner and Ng’s (1998) BEKK-GARCH (allowing for shock asymmetry), and Engle’s (2002) dynamic conditional correlation (DCC) GARCH to understand the causal relations among the series of calculated realized volatility. We find that the process of volatility transmission between Shanghai and London stock markets is bi-directional following the Shanghai-London stock connect. Furthermore, the cross-market response between the SSEC and UKX markets improves measurably under the SLSC program, with Shanghai and London appearing to respond to their past volatilities over large episodes. Overall, our results support the view that the 2019 Shanghai-London Stock Connect has improved the extent of price discovery of Chinese A shares.

Keywords: Volatility Transmission; Shanghai-London Stock Connect; Impulse Response; Variance Decomposition; BEKK-GARCH; DCC-GARCH

1 Introduction

Did the 2019 Shanghai-London Stock Connect improve the extent of price discovery of

Chinese A shares? Theoretically, enforcing regulatory regimes can exert varied influences on both information transmission and price formation. Both dynamics are central to understanding volatility in the asset market and design of counter policy measure to contain them.

Stock Connect is a program based on the mutual market access model. As its first pilot program, the Shanghai–Hong Kong Stock Connect (SHSC) was launched in November

2014, which enables investors in mainland China and Hong Kong markets to trade on each other’s stock markets. For the first time, the program opened up opportunities for overseas investors to gain access to A shares through the Hong Kong Stock Exchange. However, for any investor abroad, many barriers to investing directly in A shares continue to exist. A few years later, on June 17, 2019, the China Securities Regulatory Committee launched the

Shanghai–London Stock Connect (SLSC) program to allow mutual market trade of depository receipts that were exchangeable with domestic shares. This is the first time that overseas investors are allowed to trade A shares directly on the mainland Chinese market, and it represents a strategic breakthrough in the opening up of Chinese domestic share markets.

Regulatory interventions often incorporate significant changes in information flows that would affect the underlying pricing process of financial time series and, subsequently, the price discovery of them.

Taking some studies on price discovery changes post the SHSC program, Huo and

Ahmed (2017) examined the mean and volatility spillover between the Shanghai A shares and

Hong Kong markets and found that A shares lead the price discovery. This is similar to the findings of Hui and Chan (2018), where the trading activities in the mainland affect A-H

premium more significantly than the trading activities in Hong Kong. Sohn and Jiang (2016), however, suggested that the average common factor weight and information share of the

Hong Kong market contribute to more than 50% price discovery. Up to now, there is no literature looking at the SLSC and potential coherence between these two Stock Connect programs. In this paper, we will address the problem: the price discovery of Chinese A shares under the important and classic 2019 Shanghai–London Stock Connect program and wider regulatory impact. We will also try to draw comparisons to the SHSC when appropriate in order to better understand how the core message from these different regulatory regimes influence the information transmission and price formation.

In view of the above objectives, we undertake a number of strategies to characterize the nature of price discovery. We evaluate whether the A shares market has become a dominant vehicle for price formation, under two major Stock Connect programs (SHSC and

SLSC). In particular, we examine the information transmission (proxied by the volatility spillover) among the mainland China, Hong Kong, and London stock markets. Our empirical work involves the use of the intraday open and close prices of the Shanghai Stock Exchange

Composite Index (SSEC), Hong Kong Hang Seng Index (HSI), and the FTSE 100 Index (UKX) that are continuously recorded at five-minute intervals from October 12th, 2018, until July 26th,

2019. These data are used to calculate the realized volatilities in the Shanghai, Hong Kong, and London stock markets. We then employ the Vector Autoregression analyses to understand the causal relations among the series. These include the impulse response, variance decomposition, Kroner and Ng’s (1998) BEKK-GARCH (with the asymmetric effect of shock), and Engle’s (2002) dynamic conditional correlation (DCC) GARCH (to examine the time-varying correlations among the indices’ returns).

The application of BEKK-GARCH does not detect any bi-directional spillover effects across

Shanghai and Hong Kong under the SHSC program. The Impulse Responses analyses, on the other hand, suggest that under the SHSC program, Hong Kong reverts to the market equilibrium, after receiving a shock from Shanghai, but not vice versa. Also, the cross-market reaction of the SSEC / HSI indices to a shock from HSI / SSEC, under the SHSC program appears relatively unresponsive. Furthermore, the DCC-GARCH results suggest that

Shanghai and Hong Kong respond to their past volatilities at small episodes under the SHSC program. The striking change brought by the SLSC program is that the process of volatility transmission between Shanghai and London stock markets becomes bi-directional after the

Shanghai-London stock connect. Each of the three indices can restore equilibrium following a shock from any other markets, and the cross-market response between the SSEC and UKX markets improves a lot under the SLSC program. Also, Shanghai and London appear to respond to their past volatilities at large episodes under the SLSC program. These results might imply that the mainland China market dominates other markets in terms of information transmission (see, for example, Cheung and Mak, 1992; Eun and Shim, 1989; Huang and Kuo,

2015; and Huo and Ahmed, 2017) and that the 2019 Shanghai-London Stock Connect has improved the price discovery of Chinese A shares.

The remainder of the paper is organized as follows: Section 2 critically reviews the existing literature, Section 3 discusses the research methodology for this paper, Section 4 describes the empirical data and preliminary statistics, Section 5 presents the estimated results and findings, and Section 6 concludes with this study. Our research on the key trading mechanisms of SLSC and SHSC is summarized in the Appendix.

2 Literature

Volatility spillover (or volatility transmission) refers to a phenomenon where the price change in a financial market causes a ripple effect on another financial market. Fama (1970) argues that the bi-directional spillover effect exists if the two stock markets are highly connected, and the speed at which the share price of a market reacts to the information from the other market reflects the level of efficiency of that market. Similarly, Ross (1989) found that the spillover effect of the financial market can be used to discover market efficiency as well as the information transmission mechanism. Wei et al. (1995) found that the geographical factor may be one of the reasons for the average conditional spillover between the two markets, especially in the emerging ones.1 Booth et al. (1997) noted that technological advancement, capital market liberalization, and globalization facilitate the rapid response of a national market to new information on the international markets. Among these “volatility transmission” type of literature, many have focused on the volatility transmission between mainland China and other economies, particularly for the time period where the various stock connect programs were put in place following China’s capital account liberalization.

The Shanghai and Hong Kong stock connect (SHSC thereafter), which began in 2014, for example, has been widely regarded as a platform that facilitates the volatility transmission process between the two financial markets. Zhang and Jaffry (2015) investigated the intraday one-minute high-frequency volatility spillover between the Shanghai and Hong Kong stock markets by applying the asymmetric BEKK-GARCH in conjunction with the VAR approach as a robustness check. They found that there was no significant spillover effect during the pre-

1 They assessed how the openness of a market affect stock returns and volatility spillover using the data from the three developed markets (New York, Tokyo, and London) as well as two emerging markets (Taiwan and Hong Kong). 5

connect period; however, there was a strong bi-directional volatility spillover effect during the connect period. Huo and Ahmed (2017) applied the Johansen and Juselius (1990) test and multivariate GARCH model on the high-frequency data of the pre- and post-SHSC stock connect periods. The study revealed a weak cointegration between the Shanghai and Hong

Kong stock market (returns) during the post-connect period. More importantly, a mean and volatile spillover effect extends from Shanghai to the Hong Kong stock market.

Similarly, Huo and Ahmed (2017) suggested that the price leadership and volatility of the Chinese mainland financial market have increased with an increase in foreign investments in both the Shanghai and Hong Kong stock markets. Like Huo and Ahmed (2017), Lin (2017) analyzed the volatility spillover effect between the Shanghai and Hong Kong markets. The uniqueness of Lin (2017) is that the study took the negative asymmetry of the shock spillover approach (where the negative shock increases the volatility of another market more than the positive shock) and found that the co-movement between the Shanghai and Hong Kong markets was negative when the extreme shock of the same sign existed after the SHSC program. Lin (2017) based his analyses on the ARMA-BEKK-t-AGARCH model and detected a unidirectional spillover effect from Hong Kong to Shanghai, both before and after the SHSC stock connect program. The study argued that the significant change in volatility after the program is attributed to the persistence of volatility transmission and bi-directional causality of volatility in the Shanghai and Hong Kong markets. Ma et al. (2019) used the DCC-GARCH of Engle (2002), ADCC-GARCH (asymmetric DCC-GARCH) of Cappiello et al. (2006), and

GO-GARCH (generalized orthogonal-GARCH) of Van der Weide (2002) to investigate whether the SHSC stock connect program drove the co-movement between the Shanghai and Hong Kong stock markets. The study concluded that the correlation between the two

markets does not appear to increase significantly after the implementation of the stock connect program.

Following Bai and Chaw (2017), the Shanghai-Hong Kong stock connect program resembles a partial liberalization of the Chinese financial market where mutual access to both markets by domestic and foreign investors is possible. In their study, the Hong Kong stock exchange is regarded as a platform for the mainland financial market to gain access to a mature financial market while also serving as a prominent place for offshore CNY transactions. They found that in the short-run, the mainland market reacted positively to the Shanghai-Hong

Kong stock connect while the Hong Kong market showed a negative response. Furthermore, in the medium-term, the market size, liquidity, and exposure to systematic risks of most of the eligible indices increased after the SHSC program. Ma et al. (2019) examined whether the

Shanghai-Hong Kong stock connect drove the co-movement between the Shanghai and Hong

Kong stock markets. The paper controlled for the influential effect as a result of the financial- liberalization-induced market co-movement by comparing the time-varying correlation of the

Shanghai-Hong Kong stock markets with that of the Shenzhen-Hong Kong stock markets. The results show that the market correlation between Hong Kong and the financially liberalized

Shanghai increased much less than the market correlation between Hong Kong and the financially non-liberalized Shenzhen during the market turbulence. This implies that the co- movement between Shanghai and Hong Kong is not mainly driven by the Shanghai-Hong Kong stock connect.

With the implementation of the SHSC program, the Hong Kong stock market became the major and biggest offshore CNY market. Burderkin and Siklos (2018) discussed how the spread between the offshore CNY rate and onshore CNY rate affected the A-H share premium with regards to the control of other sentiments and liquidity effects. They found

that the higher index returns in Shanghai raised the A-H premium while higher index returns in Hong Kong drove the A-H premium down. This implies that the Shanghai stocks become more attractive when the market is buoyant.

3 Methodology

In this paper, we adopt the Vector Autoregression analyses to understand the causal relationships among the series because the VAR embeds theory in its model and provides a handy tool to track the impact of any endogenous variable on other variables in the system.

Among the VAR-type of analyses, we first employ the impulse response and variance decomposition analyses to evaluate the dependency and directional volatility spillover between the indices’ returns of the three markets. We then apply the Kroner and Ng’s (1998) multivariate BEKK-GARCH (with the asymmetric effect of shock), and Engle’s (2002) dynamic conditional correlation (DCC) GARCH to examine the time-varying correlations among the indices’ returns. By doing so, we will be in a position to understand the relative position of each market in the overall information transmission process following Shanghai-London stock connect.

3.1 Impulse Response and Variance Decomposition

We follow undifferenced VAR of the VECM to characterize impulse-responses:

Z = constant + πZ +πZ +⋯+πZ +ε, (1)

where:

푣 휋, 휋, 휋, 푣, 푍 = 푣 ; 휋 = 휋, 휋, 휋, ; 푍 = 푣,; 푣 휋, 휋, 휋, 푣,

휋, 휋, 휋, 푣, 휀 휋 = 휋, 휋, 휋,; 푍 = 푣, ; 휀 = 휀. 휋, 휋, 휋, 푣, 휀

The subscript i of 푣 (i = 1, 2, 3) refers to SSEC, HSI, and UKX, respectively. Then, a corresponding vector moving average representation of the undifferenced VAR can be written as:

Z = constant + ∑ 휑휀. (2)

The moving average coefficient 휑 measures the response of a variable (e.g. 푣) to a unit shock in another variable (for example, 푣) occurring i’th period ago. The shock takes the form of one standard error of each variable. Since the VAR is under identified, the Cholesky decomposition is used to orthogonalize the innovations.

The variance decomposition, on the other hand, takes a different approach to measure the interactions among endogenous variables. It involves a forecast-error variance decomposition technique applicable to a VAR representation of the data (see, for example,

Diebold and Yilmaz, 2009). Specifically, the H-step-ahead error variance in forecasting each variable in the system is decomposed into fractions attributable to the various system shocks

(Zhou et al., 2012). Such a technique involves orthogonal innovations for variance decomposition, achieved by the identification schemes of the Cholesky factorization. In this article, we use the variance decomposition method to evaluate the contributions of volatility

from different markets to the total forecast error variance. We also use this technique to examine the directional volatility spillover received by market i from another market j as well as the directional volatility spillover transmitted by market i to another market j (Zhou et al.,

2012).

3.2.1 Kroner and Ng (1998) BEKK-GARCH

The main feature of the Kroner and Ng’s (1998) BEKK-GARCH model, allowing for shock asymmetry, takes the following form:

H =C C+ A HA+B ΞΞB+D ηηD, (3)

where:

푐 0 0 푐 0 0 푎 푎 푎 푎 푎 푎 퐻 = 푐 푐 0 푐 푐 0 + 푎 푎 푎 퐻 푎 푎 푎 푐 푐 푐 푐 푐 푐 푎 푎 푎 푎 푎 푎

푏 푏 푏 휀, 휀, 휀, 휀, 휀, 푏 푏 푏 + 푏 푏 푏 휀,휀, 휀, 휀,휀, 푏 푏 푏 푏 푏 푏 푏 푏 푏 휀,휀, 휀,휀, 휀,

푑 푑 푑 푑 푑 푑 + 푑 푑 푑 휂η 푑 푑 푑; 푑 푑 푑 푑 푑 푑

max0,−휀,

η = max0,−휀, .

max0,−휀,

Here, the matrices A and B are the channels to convey the shock and volatility spillover effects.

More specifically, the diagonal coefficients (푎′푠) of matrix A represent the effect of market i’s shock on market i’s volatility and the off-diagonal coefficients (푎′푠) represent the cross- market effect of market i’s unexpected event on market j’s volatility. Then, the diagonal elements (b′s) of matrix B measure the impact of market i’s past volatility on market i’s current volatility and the off-diagonal elements (b′s) capture how the lagged volatility of market i affect the current volatility of market j, which is the volatility spillover effect.

Additionally, D is a 3×3 matrix of parameters showing the asymmetric response through

η. While the parameters in D are positive and significant, asymmetric effect exists when bad news causes larger volatility in own/the other market than good news. A negative value of D implies that bad news reduces the volatility in own / the other market.

3.2.2 Engle (2002) Dynamic Conditional Correlation (DCC) GARCH

The Engle’s (2002) Dynamic Conditional Correlation (DCC) GARCH model estimates the time-varying correlations among the markets or, equivalently, indices. The model is

characterized by a diagonal matrix D , with time-varying standard deviations h along the diagonal:

D = diag(h,,…,h,), (4)

where:

h =c+ ∑ 푎k + ∑ bh, 11

c is an n x 1 vector, 푎 and b are the diagonal n x n matrices, and k is the element-wise product.

An unconditional variance matrix Q that resembles the time varying conditional correlation

/ / matrix Γ , where Γ = diag{Q} Qdiag{Q} , is expressed as follows:

Q = (1−θ −θ)Q + θξξ +θQ, (5)

where Q is a k x k positive-definite unconditional variance matrix; ξ is a standardized error that has mean zero and variance one for each series; Q is the unconditional covariance matrix of the standardized errors ξ. The scalars θ,θ must satisfy: θ ≥ 0, θ ≥ 0,and θ +θ <

1. If both θ and θ are statistically significant and their sum is less than 1, we conclude that there is a non-constant conditional correlation in the data.

Finally, we estimate the dynamic conditional correlation coefficient, ρ(t), between markets i and j, at time t, which is calculated as follows:

() ρ(t) = . (6) ()()

4 Data and Descriptive Statistics

This paper uses the intraday open and close prices of the Shanghai Stock Exchange Composite

Index (SSEC), Hong Kong Hang Seng Index (HSI), and FTSE 100 Index (UKX) extracted from

Bloomberg at five-minute intervals. The reason for the use of high-frequency 5-minute data for this article is that they contain enriched information about the stock market such that they extract as much information (or volatility) transmission as possible, especially when we use a short period of data after the implementation of the SLSC stock connect program.

Moreover, at some ultra-high frequency, the data may contain microstructure noises. Thus, a

5-minute interval is considered for this study so that the microstructure noise is not overwhelming (Andersen, et al., 2001).

Our untrimmed sample corresponds to those stock indices from October 12th, 2018 until July 26th, 2019, with a total of 13,320 Shanghai Stock Exchange Composite (SSEC), 13,192

Hong Kong Hang Seng (HSI), and 20,400 FTSE100 (UKX) index prices inclusive of the lunch break in Shanghai and Hong Kong. The Shanghai-London stock connect program was launched on June 17th, 2019. Thus, we further classify the sample into two sub-periods. The first subsample corresponds to the pre-SLSC period from October 12th, 2018 to June 17th, 2019, where the SHSC is in effect, and the second subsample corresponds to the SLSC period from

June 17th, 2019 to July 26th, 2019.

In the following sections, we will explain the data construction of index return and its logarithmic realized volatility – the key variable to be used in our empirical analyses. We then present the main characteristics of our calculated logarithmic realized volatility that paves a foundation for our GARCH analyses in Section 5.

4.1 Logarithmic Index Return

The open-to-close return r, , which is continuously recorded every n-minute interval at day t, is defined as the difference between the logarithmic closing price and the logarithmic open price at the same time interval and day:

푟, = 푙표푔푃, − 푙표푔푃, , (7)

where t = 1,2,…,T; n = 1,2,…,N. Here, we use the open-to-close return to avoid the overnight effect and so it captures the changes in return (i.e., volatility) over a trading day well.

4.2 Andersen et al. (2003) Realized Volatility of Stock Index Return

The existing approaches for the modelling of the unobservable volatility are often misspecified

(Andersen et al., 2003). We undertook a more direct measure of volatility—the realized volatility—calculated from the cumulation of squared intraday returns. When the number of times the price (or return) getting sampled becomes sufficiently large, the realized volatility is an unbiased and efficient estimator of volatility (Andersen et al., 2003). Let RV denote the realized volatility of the market index traded on day t. This follows that:

RV = ∑ r,, (8)

where r, is the intraday open-to-close return at day t at time interval n, and n = 1,2,…,N.

Further treatment of Eq. (8) includes taking the logarithm of RV to model VAR as the daily realized volatility RV is shown to be lognormally distributed (Andersen et al., 2003). Denoted

by rv, the logarithmic daily realized volatility of the market index traded on day t is defined as:

rv = log(RV), (9)

where RV denotes the realized volatility of the market index traded on day t.

Our trimmed dataset involves the removal of unmatched data from the three stock indices, which results in 156 and 29 daily price observations being selected for the periods before and after the Shanghai-London stock connect, respectively. Subsequently, 156 daily logarithmic realized volatilities were calculated from the intraday returns for the period before the

Shanghai-London stock connect and 29 daily series were calculated accordingly for the period after the Shanghai-London stock connect.

4.3 Characteristics of the Logarithmic Realized Volatility Data

4.3.1 The Time Series Plot and Descriptive Statistics of the Logarithmic Realized Volatility

Figure 1 provides an overview of the overall price movement of the index prices and the corresponding logarithmic returns at every 5-minute interval during the full sample period.

The market returns exhibit patterns of volatility clustering where large variations are followed by further large variations of either sign, and small variations are followed by small variations of either sign. Such a phenomenon fulfils the pre-requisite condition of the application of the

GARCH model.

Figure 1: Index Price Movement and Logarithmic Returns on SSEC, HSI, and UKX

Stock Price Indices Open-to-Close Logarithmic Returns

SSEC

HSI RHSI

.006

.004

.002

.000

-.002

-.004 2500 5000 7500 10000 12500 UKX

Note: 1) The logarithmic returns on SSEC, HSI, and UKX at every 5-minute interval during the full sample period exhibit patterns of volatility clustering. Such a phenomenon meets the condition of the application of the GARCH model.

The descriptive statistics for the logarithmic realized volatility for the full sample period are outlined in Table 1. In all three stock markets, the distributions of the logarithmic realized volatility approach normal distributions with a skewness close to 0 and kurtosis close to 3.2

Moreover, the average realized volatility of the HSI is higher than that of SSEC and UKX, indicating that the HSI is most volatile.

Table 1: Descriptive Statistics of the Logarithmic Realized Volatilities of Returns on Stock Market Indices SSEC UKX and HSI

Mean Median Maximum Minimum Std Dev. Skewness Kurtosis Jarque- Bera rv -5.581 -5.582 -4.492 -6.339 0.353 0.163 2.685 1.581 rv -5.825 -5.825 -4.842 -6.845 0.350 0.165 3.053 0.858 rv -5.846 -5.872 -4.946 -6.532 0.282 0.428 3.247 6.113 Notes: 1) In all three stock markets, the distributions of the logarithmic realized volatility approach normal distributions with a skewness close to 0 and kurtosis close to 3; 2) We also generated the descriptive statistics for the realized volatility without taking the logarithm. We found that the kurtosis is greater than 3 and the skewness is greater than 1, indicating that the distribution was Leptokurtic. After applying the logarithm on realized volatility, the distribution turned normal.

4.3.2 Pairwise Unconditional Correlation of Logarithmic Realized Volatility Among the Three

Markets

Table 2 summarizes the unconditional correlation between the markets. During the full sample period, the data suggests a strong and positive correlation coefficient (in terms of the logarithmic realized volatility) between London and Hong Kong (0.449) and between Shanghai and Hong Kong (0.412). It is interesting to note, however, that the correlation coefficient

2 We also generated the descriptive statistics for the realized volatility without taking the logarithm. We found that the kurtosis is greater than 3 and the skewness is greater than 1, indicating that the distribution was Leptokurtic. After applying the logarithm on realized volatility, the distribution turned normal.

between the rv and rv was negative (-0.039) before the implementation of the

Shanghai-London stock connect, and such a correlation became positive (0.048) after the stock connect was implemented. This means Shanghai A shares and London market has a closer association because 0.048 > 0.039.

On the other hand, the unconditional correlation between rv and rv is positive

(0.410) before the Shanghai-London stock connect program and becomes weakened (-0.275) after the event. It could be due to the SLSC program, which effectively promotes the role of

Shanghai (and therefore promotes the relation between Shanghai and London) and consequently weakens the original relation between London and Hong Kong in the overall volatility transmission across the three markets. Moreover, the unconditional correlation between rv and rv (0.596) is much stronger under the SLSC period than in the SHSC period (0.253). Clearly, the SLSC program helps strengthen the relationship between Shanghai and Hong Kong.

Table 2: Pairwise Unconditional Correlation of the Logarithmic Realized Volatilities of Returns on Stock Market Indices HSI, UKX, and SSEC

Correlation Probability rv rv rv Panel I: Full Sample

rv 1.000

rv 0.449 1.000

rv 0.412 0.141 1.000 Panel II: Under Shanghai-Hong Kong Stock Connect

rv 1.000

rv 0.410 1.000

rv 0.253 -0.039 1.000 Panel III: Under Shanghai-London Stock Connect

rv 1.000

rv -0.275 1.000

rv 0.596 0.048 1.000

Notes: 1) The correlation coefficient between the 푟푣 and 푟푣 was negative (-0.039) before the implementation of the Shanghai-London stock connect, and such a correlation became positive (0.048) after the stock connect was implemented. This means Shanghai A shares and London market has a closer association after the Shanghai-London stock connect because 0.048 > 0.039; 2) The unconditional correlation between 푟푣 and 푟푣 is positive (0.410) before the Shanghai-London stock connect program and becomes weakened (-0.275) after the event; 3) The unconditional correlation between 푟푣 and 푟푣 (0.596) is much stronger under the SLSC period than in the SHSC period (0.253). Clearly, the SLSC program helps strengthen the relationship between Shanghai and Hong Kong.

5 Empirical Results

5.1 Non-stationarity Tests

The unit root tests, including the ADF, PP, and KPSS, are performed on the logarithmic realized volatility rv for the full sample period as well as the periods before and after the

Shanghai-London stock connect (see Appendix 3). The PP tests on rv, rv, and rv suggest that the logarithmic realized volatilities of Shanghai, Hong Kong, and London market indices are (weakly) stationary. However, the ADF and KPSS tests on rv, rv, and rv showed different results, compared with those of the PP test. Perron (1989) suggests that when the generalized unit root tests, including the ADF and KPSS tests, render a result diverse from the PP test, there could be a signal of a structural break in the time series. Hence, it is necessary to re-examine the stationary property using a breakpoint unit root test that incorporates the possible structural break in the time series rv. Table 4 presents the unit root test results with a breakpoint based on the innovation outlier (IO) model during the full sample period, with an intercept both in the basic and breaking specifications.

Table 4: Breakpoint Unit Root Tests on the Logarithmic Realized Volatilities of the Shanghai, Hong Kong, and London Stock Market Index (SSEC, HSI, and UKX) Returns

Breakpoint Unit Root Test (with intercept only) Variables Statistic P-value Break Date Decision

rv -8.476 < 0.01 11/02/2018 Reject

rv -6.799 < 0.01 10/30/2018 Reject

rv -7.599 < 0.01 11/02/2018 Reject Notes: 1) These are the unit root tests with a breakpoint based on the innovation outlier (IO) model during the full sample period, with an intercept both in the basic and breaking specifications; 2) The structural break date for each time series was selected by the Zivot and Andrews (1992) minimum t-statistic; 3) The null hypothesis that the time series is integrated with order one with a break is rejected for all variables.

The structural break date for each time series was selected by the Zivot and Andrews (1992) minimum t-statistic. It follows the null hypothesis that the time series is integrated with order one with a break rejected for all variables. Therefore, we concluded that the logarithmic realized volatilities for the SSEC, HSI, and FTSE 100 indices are all (weakly) stationary.

5.2 Impulse Response and Variance Decomposition Analyses

Figures 2 and 3 present the impulse response analyses using the trivariate VAR modelling of the data of 1) Shanghai-Hong Kong stock connect and 2) Shanghai-London stock connect, respectively. The solid line shows the responsiveness of the one variable resulting from the one-unit shock in another variable of the VAR measured as one standard error, represented by the dashed line. The diagonal graphs in Figures 2 and 3 explain the impulse response of the market indices to their own market shocks. As those in Figure 2 suggest, the responses of the

SSEC, HSI, and UKX indices to their own market shocks during the SHSC period are positive and gradually attenuate after the first lag. The diagonal graphs in Figure 3 suggest that the 20

initial responses of the market indices to their market shocks during the SLSC period are also positive, which then fluctuate and quickly restore the equilibrium.

Figure 2: The Impulse Responses of the Logarithmic Realized Volatilities of the Shanghai, Hong Kong, and London Stock Market Index (SSEC, HSI, and UKX) Returns to the Shocks of Other Indices During the Shanghai-Hong Kong Stock Connect Period

Response to Cholesky One S.D. (d.f. adjusted) Innovations ± 2 S.E.

Response of LRVSSEC to LRVSSEC Response of LRVSSEC to LRVHSI Response of LRVSSEC to LRVUKX .3 .3 .3

.2 .2 .2

.1 .1 .1

.0 .0 .0

-.1 -.1 -.1 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Response of LRVHSI to LRVSSEC Response of LRVHSI to LRVHSI Response of LRVHSI to LRVUKX .3 .3 .3

.2 .2 .2

.1 .1 .1

.0 .0 .0

-.1 -.1 -.1 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Response of LRVUKX to LRVSSEC Response of LRVUKX to LRVHSI Response of LRVUKX to LRVUKX .3 .3 .3

.2 .2 .2

.1 .1 .1

.0 .0 .0

-.1 -.1 -.1 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Notes: 1) The solid line shows the responsiveness of the one variable resulting from the one-unit shock in another variable of the VAR measured as one standard error, represented by the dashed line; 2) The diagonal graphs explain the impulse response of the market indices to their own market shocks; 3) The responses of the SSEC, HSI, and UKX indices to their own market shocks during the SHSC period are positive and gradually attenuate after the first lag; 4) Under the SHSC period, Hong Kong (and London) revert to the equilibrium, after receiving a shock from Shanghai, but not vice versa; 5) The cross-market reaction of the SSEC / HSI indices to one-unit shock in HSI / SSEC, under the SHSC program, is relatively unresponsive.

Figure 3: The Impulse Responses of the Logarithmic Realized Volatilities of the Shanghai, Hong Kong, and London Stock Market Index (SSEC, HSI, and UKX) Returns to the Shocks of Other Indices During the Shanghai-London Stock Connect Period

Response to Cholesky One S.D. (d.f. adjusted) Innovations ± 2 S.E.

Response of LRVSSEC to LRVSSEC Response of LRVSSEC to LRVHSI Response of LRVSSEC to LRVUKX .4 .4 .4

.3 .3 .3

.2 .2 .2

.1 .1 .1

.0 .0 .0

-.1 -.1 -.1

-.2 -.2 -.2 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Response of LRVHSI to LRVSSEC Response of LRVHSI to LRVHSI Response of LRVHSI to LRVUKX .4 .4 .4

.3 .3 .3

.2 .2 .2

.1 .1 .1

.0 .0 .0

-.1 -.1 -.1

-.2 -.2 -.2 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Response of LRVUKX to LRVSSEC Response of LRVUKX to LRVHSI Response of LRVUKX to LRVUKX .4 .4 .4

.3 .3 .3

.2 .2 .2

.1 .1 .1

.0 .0 .0

-.1 -.1 -.1

-.2 -.2 -.2 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Notes: 1) The solid line shows the responsiveness of the one variable resulting from the one-unit shock in another variable of the VAR measured as one standard error, represented by the dashed line; 2) The diagonal graphs explain the impulse response of the market indices to their own market shocks; 3) The initial responses of the market indices to their market shocks during the SLSC period are also positive, which then fluctuate and quickly restore the equilibrium; 4) Each of the three indices restores its own equilibrium (after a couple of lags) after receiving a shock from any other markets. This means London / Shanghai can achieve market equilibrium after receiving a shock from Shanghai / London. Also, Hong Kong / Shanghai will restore its equilibrium after receiving a shock from Shanghai / Hong Kong; 5) The cross-market response of the SSEC and UKX improves a lot under SLSC. In other words, the impact of a shock in SSEC on the London stock market remains stronger; meanwhile, a shock in the UKX causes the return volatilities in the Shanghai stock market to become positive after the Shanghai-London stock connect. This means the connection between Shanghai and London through information transmission increased under the SLSC program.

The cross-market responses of the SSEC, HSI, and UKX indices are shown in the off-diagonal graphs. Under the SHSC period (see Figure 2), Hong Kong (and London) revert to the equilibrium, after receiving a shock from Shanghai, but not vice versa.3 The striking change brought by SLSC is that each of the three indices restores its own equilibrium (after a couple of lags) after receiving a shock from any other markets (see Figure 3). This means London /

Shanghai can achieve market equilibrium after receiving a shock from Shanghai / London. Also,

Hong Kong / Shanghai will restore its equilibrium after receiving a shock from Shanghai / Hong

Kong.

The cross-market reaction of the SSEC / HSI indices to one-unit shock in HSI / SSEC, under the SHSC program, is relatively unresponsive (see Figure 2). It is interesting to note, however, that the cross-market response of the SSEC and UKX improves a lot under SLSC

(see Figure 3). In other words, the impact of a shock in SSEC on the London stock market remains stronger; meanwhile, a shock in the UKX causes the return volatilities in the Shanghai stock market to become positive after the Shanghai-London stock connect. This means the connection between Shanghai and London through information transmission increased with the introduction of the SLSC program.

Table 5 summarizes the variance decomposition analyses on the return volatilities across the three financial markets for both Panel II (Shanghai-Hong Kong stock connect) and

Panel III (Shanghai-London stock connect) time periods. Under both two stock connect programs, the Shanghai stock market dominates the other market in terms of information transmission (see, for example, Cheung and Mak, 1992; Eun and Shim, 1989; Huang and Kuo,

2015; and Huo and Ahmed, 2017). For example, under the SHSC program, Shanghai / Hong

Kong contributes to an average of 18.17% / 1.61% to the volatility transmission of Hong Kong

3 Hong Kong would barely restore to the equilibrium, after receiving a shock from London, but not vice versa. 23

/ Shanghai. Also, under the SLSC program, Shanghai / London contributes to an average of

3.8% / 2.67% to the volatility transmission of London / Shanghai. Furthermore, under the SLSC,

the volatility contribution from Shanghai to the variance of the Hong Kong market increases

remarkably by 15.86%, which is very close to the 17.1% increase in the volatility contribution

from London to Hong Kong.4 The short-run shock from the Shanghai stock market (due to

the SLSC) appears to be increasingly influential on the international market under the SLSC.

On the other hand, the SLSC seems to boost the information contribution from Hong

Kong to London as Hong Kong contribute 12.22% to the forecast error variance of London,

1.53% higher than the case of the SHSC period.

Table 5: Variance Decompositions

Panel II: Shanghai-Hong Kong Stock Connect

Variance Decomposition of rv Variance Decomposition of rv Variance Decomposition of rv

Period S.E. rv rv rv S.E. rv rv rv S.E. rv rv rv 1 0.23 100.00 0.00 0.00 0.22 18.88 81.12 0.00 0.27 2.83 2.45 94.72 2 0.26 99.37 0.10 0.62 0.24 19.67 79.31 1.02 0.28 3.35 2.55 94.10 3 0.28 98.48 0.33 1.20 0.25 18.84 79.98 1.18 0.30 3.37 7.66 88.98 4 0.29 97.39 0.59 2.02 0.26 18.48 79.83 1.69 0.31 3.49 9.17 87.34 5 0.30 96.12 1.12 2.76 0.26 18.10 79.92 1.98 0.32 3.38 11.59 85.02 6 0.31 94.86 1.66 3.48 0.26 17.83 79.89 2.28 0.33 3.31 12.93 83.75 7 0.31 93.65 2.26 4.09 0.26 17.63 79.86 2.50 0.33 3.24 14.12 82.64 8 0.32 92.54 2.83 4.63 0.26 17.49 79.83 2.68 0.33 3.19 14.92 81.90 9 0.32 91.55 3.37 5.08 0.26 17.40 79.77 2.82 0.33 3.16 15.53 81.31 10 0.32 90.68 3.85 5.46 0.27 17.33 79.73 2.93 0.34 3.14 15.97 80.89 Panel III: Shanghai-London Stock Connect

Variance Decomposition of rv Variance Decomposition of rv Variance Decomposition of rv

Period S.E. rv rv rv S.E. rv rv rv S.E. rv rv rv 1 0.34 100.00 0.00 0.00 0.27 41.25 58.75 0.00 0.21 2.41 11.38 86.21 2 0.35 98.31 0.58 1.11 0.27 41.39 58.36 0.25 0.21 2.52 12.03 85.45 3 0.35 96.12 1.15 2.72 0.29 31.91 44.34 23.75 0.25 4.08 12.33 83.58

4 The average volatility contribution from Shanghai to the variance of the Hong Kong market increases remarkably from 18.17% to 34.03%; whereas the average volatility contribution from London to Hong Kong increases from 1.91% to 19.01%. 24

4 0.35 95.81 1.16 3.03 0.29 31.89 44.35 23.75 0.25 4.07 12.32 83.60 5 0.35 95.53 1.18 3.28 0.29 32.40 44.17 23.44 0.25 4.12 12.34 83.54 6 0.35 95.50 1.22 3.28 0.29 32.39 44.08 23.53 0.25 4.14 12.36 83.49 7 0.35 95.45 1.22 3.32 0.29 32.26 43.88 23.86 0.25 4.16 12.36 83.47 8 0.35 95.45 1.22 3.33 0.29 32.26 43.89 23.85 0.25 4.16 12.36 83.47 9 0.35 95.44 1.23 3.33 0.29 32.27 43.88 23.85 0.25 4.17 12.36 83.47 10 0.35 95.44 1.23 3.33 0.29 32.27 43.88 23.85 0.25 4.17 12.36 83.47 Notes: 1) Under both two stock connect programs, the Shanghai stock market dominates the other market in terms of information transmission. For example, under the SHSC program, Shanghai / Hong Kong contributes to an average of 18.17% / 1.61% to the volatility transmission of Hong Kong / Shanghai. Also, under the SLSC program, Shanghai / London contributes to an average of 3.8% / 2.67% to the volatility transmission of London / Shanghai; 2) Under the SLSC, the volatility contribution from Shanghai to the variance of the Hong Kong market increases remarkably by 15.86%, which is very close to the 17.1% increase in the volatility contribution from London to Hong Kong. This implies that the short-run shock from the Shanghai stock market (due to the SLSC) appears to be increasingly influential on the international market under the SLSC; 3) The SLSC seems to boost the information contribution from Hong Kong to London as Hong Kong contribute 12.22% to the forecast error variance of London, 1.53% higher than the case of the SHSC period.

5.3 Kroner and Ng (1998) BEKK-GARCH (1,1) Estimation Results with Asymmetric Specifications

Table 6 reports the parameter estimates of the variance-covariance of the BEKK-GARCH

(1,1) model. The subscript i (i = 1, 2, 3) refers to the SSE, HSI, and UKX, respectively. The parameters a, b correspond to the elements in matrices A, B in Eq. (3) respectively. Specifically, the diagonal coefficients (푎′푠) of matrix A represent the effect of market i’s shock on market i’s volatility and the off-diagonal coefficients (푎′푠) represent the cross-market effect of market i’s unexpected event on market j’s volatility. Then, the diagonal elements (b′s) of matrix B measure the impact of market i’s past volatility on market i’s current volatility, and the off-diagonal elements (b′s) capture how the lagged volatility of market i affect the current volatility of market j, which is the volatility spillover effect.

Under the SHSC program (i.e., Panel II), the diagonal coefficients of matrices A and B, for example, 푎, 푎, 푎, 푏, 푏, 푎푛푑 푏 of Shanghai, Hong Kong and London are all

statistically significant at 1% level, whereas all the off-diagonal coefficients are statistically insignificant for these economies. Therefore, each market only just responds to its own market’s shock and past volatility, and there is no significant spillover effect as the cross- market influences of unexpected news or past volatility are insignificant across Shanghai, Hong

Kong, and London under the SHSC program.

Furthermore, the conditional variance of the return volatility in Shanghai, Hong Kong, and London becomes highly linked to their past conditional variance in Panel III (i.e., the SLSC period) than in Panel II (i.e., the SHSC period), in particular, 푏 =1.228 > 0.938; 푏 =1.024 >

0.905. This means that, in general, the return volatility in Shanghai, Hong Kong, and London exhibits higher persistence after the Shanghai-London stock connect.

Moreover, all diagonal coefficients in matrix A (푎’s) are statistically significant in Panel

II but insignificant in Panel III. This means that the current return volatilities in Shanghai, Hong

Kong, and London are unaffected by news from their own market after the SLSC program, but instead, are highly affected by past volatility.

As for the asymmetric specification captured by matrix D of Eq. (3), the diagonal coefficients (d′s) of matrix D represent the effect of market i’s bad news on market i’s return volatility and the off-diagonal coefficients (d′s) represent the cross-market effect of market i’s bad news on market j’s return volatility. In Table 6, the bad news from Shanghai / London was found to have a significant (but small) impact on the return volatility of the London /

Shanghai stock market in Panel III when the Shanghai-London stock connect was being

5 implemented (i.e. d = -0.001; d = -0.0001). Echoing Yang et al. (2019), who discovered

5 The small value could be due to the short trading cycles following the SLSC stock connect program lead to a limited dataset, making it difficult to detect large volatility spillover across markets. In addition, due to the use of the trivariate BEKK-GARCH model, it is possible to dilute some of the volatility spillovers between pairwise markets and not achieve the overall volatility overflow. 26

that the downside risk spillover between China and London were strengthened after the

Shanghai-Hong Kong stock connect, our empirical evidence suggests that the process of volatility transmission due to bad news between Shanghai and London stock markets becomes bi-directional after the Shanghai-London stock connect.

Table 6: Multivariate BEKK-GARCH (1,1) Estimation with Asymmetric Specifications

Conditional Mean Estimates Panel II: Shanghai-Hong Kong Stock Connect Panel III: Shanghai-London Stock Connect BEKK-GARCH BEKK-GARCH Mean (1) -5.49 (0.000) Mean (1) -5.87 (0.000) Mean (2) -5.76 (0.000) Mean (2) -5.72 (0.000) Mean (3) -5.72 (0.000) Mean (3) -6.22 (0.000) Conditional Variance-Covariance Estimates BEKK-GARCH BEKK-GARCH

c 0.144 (0.678) c -0.009 (0.898)

c 0.115 (0.781) c -0.019 (0.425)

c 0.015 (0.994) c -0.009 (0.836)

c 0.095 (0.884) c -0.060 (0.612)

c -0.115 (0.995) c -0.001 (0.941)

c 0.032 (0.999) c 0.000 (0.995)

풂ퟏퟏ 0.543 (0.002) *** 풂ퟏퟏ 0.224 (0.367)

푎 0.037 (0.218) 푎 -0.002 (0.896)

푎 -0.102 (0.858) 푎 -0.013 (0.974)

푎 0.188 (0.777) 푎 0.077 (0.930)

풂ퟐퟐ 0.441 (0.000) *** 풂ퟐퟐ 0.840 (0.173)

푎 0.009 (0.591) 푎 0.003 (0.897)

푎 -0.054 (0.927) 푎 0.140 (0.999)

푎 0.079 (0.849) 푎 -0.107 (0.948)

풂ퟑퟑ 0.364 (0.002) *** 풂ퟑퟑ 0.189 (0.999)

퐛ퟏퟏ 0.929 (0.000) *** 퐛ퟏퟏ 0.892 (0.086) *

b 0.060 (0.766) b -0.001 (0.936)

b 0.073 (0.719) b -0.006 (0.623)

b -0.185 (0.653) b -0.001 (0.999)

퐛ퟐퟐ 0.938 (0.000) *** 퐛ퟐퟐ 1.228 (0.000) ***

b 0.151 (0.664) b -0.001 (0.769)

b -0.075 (0.812) b 0.041 (0.637)

b -0.121 (0.610) b 0.038 (0.699)

퐛ퟑퟑ 0.905 (0.000) *** 퐛ퟑퟑ 1.024 (0.000) ***

퐝ퟏퟏ -0.362 (0.719) 퐝ퟏퟏ 0.417 (0.799)

d -0.025 (0.977) d 0.913 (0.569) 27

d -0.117 (0.896) 퐝ퟏퟑ -0.001 (0.000) ***

d 0.383 (0.800) d 0.0001 (0.798)

퐝ퟐퟐ -0.047 (0.973) 퐝ퟐퟐ -0.0001 (0.000) ***

d -0.036 (0.982) d -0.0001 (0.999)

d -0.121 (0.914) 퐝ퟑퟏ -0.0001 (0.000) ***

d 0.214 (0.766) 퐝ퟑퟐ -0.0003 (0.000) ***

퐝ퟑퟑ 0.199 (0.838) 퐝ퟑퟑ -0.0001 (0.999) Notes: 1) The variance-covariance matrix of the trivariate BEKK-GARCH (1,1) model is estimated using an iterative maximum likelihood estimation of the Berndt, Hall, Hall, and Hausman (BHHH) algorithm; 2) The number in parenthesis denotes the p-value; 3) *, ** and *** signify the statistical significance at the 10%, 5%, and 1% levels, respectively; 4) The parameters c, a, b, and d represent the elements in the matrices of C, A, B, and D in Eq. (3); 5) Under Panel II, the diagonal coefficients of matrices A and B, for example, 푎, 푎, 푎, 푏, 푏, 푎푛푑 푏 of Shanghai, Hong Kong and London are all significant at 1% level, whereas all the off-diagonal coefficients are insignificant for these markets. Therefore, each market only just responds to its own market’s shock and past volatility, and there is no significant spillover effect under the SHSC program; 6) The conditional variance of the return volatility in Shanghai, Hong Kong, and London becomes highly linked to their past conditional variance in Panel III than in Panel II, in particular, 푏 =1.228 > 0.938; 푏 =1.024 > 0.905. This means that the return volatility in Shanghai, Hong Kong, and London exhibits higher persistence after the

Shanghai-London stock connect; 7) All diagonal coefficients in matrix A (푎’s) are significant in Panel II but insignificant in Panel III. This means that the current return volatilities in Shanghai, Hong Kong, and London are unaffected by news from their own market after the SLSC program, but instead, are highly affected by past volatility; 8) Bad news from Shanghai / London was found to have a significant (but small) impact on the return volatility of the London / Shanghai market in Panel III (i.e. 푑 = -0.001; 푑 = -0.0001), which suggests that the process of volatility transmission due to bad news between Shanghai and London markets is bi-directional after the Shanghai-London stock connect.

Also, the volatility of Hong Kong is affected by the bad news from its own market and London

(as d = -0.0001, d = -0.0003). On comparing the influence of bad news on the return volatility of Hong Kong, we find: |d| > |d|. This suggests that the bad news from London, as opposed to those from the Hong Kong market itself, has a significant but relatively more negative impact on Hong Kong’s return volatility under the SLSC.

Furthermore, the statistically significant coefficients d, d, and d (d= -0.001, d = -0.0001, and d = -0.0003) are small when compared with their diagonal counterparts in matrix B (b = 1.024, b = 0.892, and b = 1.228). This means that while the bad news has a significant but small and negative impact, each market appears more responsive to its own past volatility during the SLSC period.

5.4 Engle (2002) DCC-GARCH (1,1) Estimation Results

Table 7 reports the parameter estimates of the DCC-GARCH (1,1) model. The subscript i (i

= 1, 2, 3) refers to SSE, HSI, and UKX, respectively. The parameters a, b correspond to the elements in Eq. (4)—they represent the effect of one market’s past shock and past conditional variance on the market’s current conditional variance. Under Panel III where the SLSC is being implemented, the estimated parameters 푎 of SSEC and UKX, that is, 푎 and 푎, have small values. This implies that the influence of shock on the current volatility of Shanghai and London stock markets is small under SLSC. So, the variation in the conditional variance in these two markets may come from the market’s own past conditional variance. Not surprisingly, the estimated parameters b of SSEC and UKX under Panel III (i.e., 푏 and 푏) are highly significant, with the return volatilities exhibiting much higher persistence under SLSC across the Shanghai and London stock markets (as 푏 =1.135 > 0.518, 푏 =1.232 > 0.863). In contrast, the estimated coefficients b of Shanghai and Hong Kong (i.e. 푏, 푏 ) are small during the period when the SHSC is being implemented, which suggests that each market responds to their past volatility at small episode (as 푏 = 0.518<1.135, 푏 = 0.523<1.344).

In addition, the estimated coefficients θ and θ are statistically significant both in the

SHSC and SLSC periods. Also, in both samples, if we add up θ and θ, their sum value is less than 1. This means there is a dynamic conditional correlation among the three markets over time. Thus, if there is a market shock, the conditional correlation will change across all three markets.

Table 7: Multivariate DCC-GARCH (1,1) Estimation

Conditional Mean Estimates Panel II: Shanghai-Hong Kong Stock Connect Panel III: Shanghai-London Stock Connect DCC-GARCH DCC-GARCH Mean (1) -5.500 (0.000) Mean (1) -5.86 (0.000) Mean (2) -5.789 (0.000) Mean (2) -6.07 (0.000) Mean (3) -5.742 (0.000) Mean (3) -6.19 (0.000) Conditional Variance-Covariance Estimates DCC-GARCH DCC-GARCH

c 0.003 (0.056) c -0.032 (0.223)

c 0.006 (0.203) c -0.012 (0.049)

c 0.001 (0.403) c 0.011 (0.681)

푎 0.279 (0.015) *** 푎 -0.181 (0.065) *

푎 0.233 (0.132) 푎 -0.345 (0.216)

푎 0.096 (0.089) * 푎 -0.472 (0.149)

b 0.518 (0.001) *** b 1.135 (0.029) **

b 0.523 (0.078) * b 1.344 (0.105)

b 0.863 (0.000) *** b 1.232 (0.050) **

θ 0.196 (0.000) *** θ 0.444 (0.003) ***

θ 0.699 (0.000) *** θ 0.364 (0.065) * Notes: 1) The variance-covariance matrix of the trivariate DCC-GARCH (1, 1) model is estimated using an iterative maximum likelihood estimation of the Berndt, Hall, Hall, and Hausman (BHHH) algorithm; 2) The number in parenthesis denotes the p-value; 3) *, ** and *** signify the statistical significance at the 10%, 5%, and 1% levels, respectively; 4) The parameters c, a, b, and 휃 represent the corresponding parameters

of Eq. (4) and (5); 5) In Panel III, the estimated parameters 푎 of SSEC and UKX, that is, 푎 and 푎, have small values. This implies that the influence of shock on the current volatility of Shanghai and London markets is small, and thus the variation in the conditional variance in these two markets may come from the market’s own past conditional variance. 6) This point is supported by the fact that the estimated parameters b of

SSEC and UKX under Panel III (i.e., 푏 and 푏) are highly significant, with the return volatilities exhibiting

much higher persistence under SLSC across the Shanghai and London stock markets (as 푏 =1.135 > 0.518,

푏 =1.232 > 0.863). 7) In contrast, the estimated coefficients b of Shanghai and Hong Kong (i.e., 푏, 푏 ) are small under Panel II, which suggests that each market responds to their past volatility at small episode

(as 푏 =0.518<1.135, 푏 =0.523<1.344). 8) The estimated coefficients 휃 and 휃 are significant both in

the SHSC and SLSC periods. Also, in both samples, if we add up 휃 and 휃, their sum value is less than 1. This means there is a dynamic conditional correlation among the three markets over time.

Furthermore, we plot the pairwise conditional correlation generated from Eq. (6) of the DCC model in Figure 4. The conditional correlation between the return volatilities of

SSEC and HSI varies between -0.35 and 0.90 (top panel) while the return volatilities of HSI and UKX remain positively correlated between 0 and 0.80 (bottom panel). The means of 30

these two pairs of conditional correlations are consistent with the corresponding unconditional correlations at about 0.4 in Table 2 (full sample).

Figure 4: Pairwise Conditional Correlation of the Logarithmic Realized Volatilities of the Shanghai, Hong Kong, and London Stock Market Index (SSEC, HSI, and UKX) Returns in the Full Sample Period

Notes: 1) We plot the pairwise conditional correlation generated from Eq. (6) of the DCC model in Figure 4; 2) The conditional correlation between the return volatilities of SSEC and HSI varies between -0.35 and 0.90 (top panel) while the return volatilities of HSI and UKX remain positively correlated between 0 and 0.80 (bottom panel); 3) The dynamics of the conditional correlation between the Shanghai and London stock markets, as captured by the graph in the middle, exhibit two peaks during the overall sample period; 4) As time passed to the 160th unit, the conditional correlation between

Shanghai and London exhibited an upward trend. The possible reason for such an enhanced correlation is the issuance of the well-known Huatai securities, and investors tend to respond positively to this.

The dynamics of the conditional correlation between the Shanghai and London stock markets, as captured by the graph in the middle, exhibit two peaks during the overall sample period.

The first peak is found at the beginning of the full sample period. This is right after the Chinese government launched the trial vision of the regulatory policy of SLSC, which sends out a positive signal about the SLSC to investors across both sides of the market. As time passed to the 160th unit, the correlation between Shanghai and London reaches their second high.6

Since then, we observe an upward trend in the conditional correlation between the two markets. The possible reason for such an enhanced correlation is the issuance of the well- known Huatai securities, and investors tend to respond positively to this. Huatai securities, an SSEC-listed company, issued its first general depositary receipt on the London Stock

Exchange on June 17th, 2019. The issued GDR represented ten shares of the Huatai’s A share and raised US$1.692 billion on the first day of the Shanghai-London Stock Connect program, following the report of the Huatai Securities Co., Ltd.

6 Conclusions

Changes in regulatory conditions impact price discovery and variations of dynamics within information transmission channel, eventually forming a major building block in volatility transmission in the asset market. Characterizing the exact nature of volatility transmission and the inter-market dynamics is very important given that these help investors towards earning a significant leverage in profit-earnings. Predictability is a great virtue and if it relates

6 It is worth noticing that the SLSC program was launched at the 157th unit of time. 32

to certain characterization of an information transmission channel following changes in regulatory conditions in the market, it becomes relatively easier for investors to leverage on profits. Exploiting various degrees of informational inefficiency in the market serves investors well towards prediction of a likely change in the prospective profitability. This paper focuses on the Shanghai-London stock connect (that commenced on June 19th, 2019) and has effectively combined London—the world’s leading capital market—with Shanghai—one of the world’s largest domestic capital markets—through the trading of depository receipts. It presents, for the first time, a system that any foreign companies can list in China and for the first time the Shanghai exchange listed company can raise capital abroad through instruments exchangeable with their domestic shares. 7 Our research presents, to the best of our knowledge, the first rigorous study that evaluates the role of the 2019 Shanghai–London Stock

Connect in improving the process of price discovery—the crucial economic function of the stock exchange for the Chinese A shares. The usefulness of this research is that it improves the understanding of the information transmission processes across multiple markets from a regulatory perspective.

We use the intraday open and close prices of the Shanghai Stock Exchange Composite

Index (SSEC), Hong Kong Hang Seng Index (HSI), and the FTSE 100 Index (UKX) that are continuously recorded at five-minute intervals from October 12th, 2018, until July 26th, 2019.

These data are used to calculate the realized volatilities in the Shanghai, Hong Kong, and

London stock markets. We then apply these time series on various econometrics modelling specific to the VAR-type of representation. These mainly include: 1) impulse response and variance decomposition; 2) BEKK-GARCH with asymmetric specifications; and 3) DCC-

GARCH, to examine the shifts of the volatility transmission process across the three return

7 https://www.lseg.com/markets-products-and-services/our-markets/Shanghai-london-stock-connect 33

volatilities characterizing the VAR system. The main point we draw from our empirical analyses is that the 2019 Shanghai-London Stock Connect has improved the price discovery of Chinese A shares. Our primary evidence based on pairwise unconditional correlation suggests that the Shanghai A shares and London market has a stronger association after the

SLSC, however, the unconditional correlation between London and Hong Kong is weakened after the event. It could be due to the SLSC, which effectively promotes the role of Shanghai

(and therefore promotes the relation between Shanghai and London) and consequently weakens the original relation between London and Hong Kong in the overall volatility transmission across the three markets. In addition, the unconditional correlation between

Shanghai and Hong Kong is much stronger under the SLSC period than in the SHSC period.

Clearly, the SLSC program helps strengthen the relationship between Shanghai and Hong

Kong.

Our dynamic analyses using VAR suggest that under the SHSC program, Hong Kong (and

London) revert to the equilibrium, after receiving a shock from Shanghai, but not vice versa.

The striking change brought by SLSC is that each of the three indices restores its own equilibrium (after a couple of lags) after receiving a shock from any other markets. This means

London / Shanghai can achieve market equilibrium after receiving a shock from Shanghai /

London. Also, Hong Kong / Shanghai will restore its equilibrium after receiving a shock from

Shanghai / Hong Kong. Furthermore, the Impulse Responses analyses also suggest that the cross-market reaction of the SSEC / HSI indices to one-unit shock in HSI / SSEC, under the

SHSC is relatively unresponsive. However, the cross-market response between the SSEC and

UKX markets improves a lot under the SLSC. In other words, the impact of a shock in

Shanghai on the London stock market remains stronger; meanwhile, a shock in London causes the return volatilities in the Shanghai stock market to become positive. This means the

connection between Shanghai and London through information transmission has increased with the introduction of the SLSC program. Our variance decomposition analyses suggest that under the two stock connect programs, the Shanghai stock market dominates the other market in terms of information transmission. On the other hand, the SLSC seems to boost the information contribution from Hong Kong to London.

With regard to direction of spillover effects, the BEKK-GARCH results do not detect any bi-directional spillover as the cross-market influence of unexpected news, or past volatility is insignificant across Shanghai and Hong Kong under the SHSC program. However, the return volatility in these two markets exhibits higher persistence after the SLSC program, meaning that the SLSC program has caused the Shanghai and Hong Kong’s volatility to become highly affected by their past volatilities. The process of volatility transmission (due to bad news) between Shanghai and London stock markets becomes bi-directional after the Shanghai-

London stock connect. Additionally, the bad news from London, as opposed to those from the Hong Kong market itself, has a significant and more negative impact on Hong Kong’s return volatility under the SLSC. Finally, the dynamic component volatility model (DCC-

GARCH) suggest that the influence of shock on the current volatility of Shanghai and London stock markets is small under SLSC, and the variation in the conditional variance in these two markets comes from the market’s own past conditional variance. In contrast, Shanghai and

Hong Kong respond to their past volatility at small episodes under SHSC. Furthermore, there is a dynamic conditional correlation among the three markets over time.

Appendices:

1. The Shanghai-London Stock Connect Program

On June 17th, 2019, the China Securities Regulatory Commission (CSRC), together with the

UK Financial Conduct Authority, made a joint announcement about the birth of the Shanghai-

London stock connect (SLSC) program.8 The program is the first stock connect program where overseas investors can trade A shares directly on the mainland Chinese market, which represents a strategic breakthrough in the opening up of Chinese domestic share markets.

The main attraction of the SLSC program is that it allows mutual market trade of depository receipts that were exchangeable (i.e., redeemed) with Chinese A shares. Such a form of trade is because there is no overlapping trading hour between Shanghai and London markets due to the 7-hour time difference between the two markets (or 8-hour difference if the daylight saving time is applied in the UK). Under the SLSC scheme, therefore, regular cross-border trading involves the trading of the depository receipts which starts with the creation of the receipt, for example, at Shanghai Stock Exchange, followed by the redemption of the receipt in exchange with the underlying A shares, for example, at LSE, which usually takes place the following day.

8 The stock connect program between Shanghai and London was initially proposed by Guangshao Tu, the executive vice mayor of Shanghai, to the China Securities Regulatory Commission (CSRC) on December 29th, 2015. Three years later on October 10th, 2018, the CSRS officially released the “Regulations on the Depository receipts of Shanghai-London Stock connect (trial)”, a revised version of the initial proposal, which took on board the opinions and suggestions from both sides of the market. The revision includes the restriction of the redemption period after the domestic-listed companies issued global depositary receipts (GDRs) abroad and the restriction of the redemption period after the overseas-listed companies issued depositary receipts (CDRs) in China. 36

The Trading Process of the Shanghai-London Stock Connect

The trading process of the Shanghai-London stock connect program may be illustrated using a diagram, as shown in Figure 5. Westbound trading refers to the trading of the global depository receipts (GDRs) issued by the companies listed in the Shanghai Stock Exchange on the London Stock Exchange.9 The issuers of the GDRs in the westbound trading must be those listed companies on the A-share market of the Shanghai Stock Exchange with a market capitalization of over 20 billion CNY. All trading of the GDRs in London follows the rules and practices of the LSE. Stock connect GDRs traded on the Shanghai segment must go through the International Order Book (IOB). Designated brokers act in both Shanghai and London and ensure the exchangeability between the GDRs and the underlying A-share. Regular cross- border trading of GDRs involves the creation of GDRs on T+2 and the redemption taking place on T+3.

9 According to the joint announcement, the total quota of the westward business is 300 billion CNY.

Figure 5: The Trading Mechanism of the Shanghai-London Stock Connect

Westbound Trading Eastbound Trading

Underlying stocks listed Underlying stocks listed on SHSE on LSE

Cross-border conversion Cross-border conversion mechanism mechanism

GDR CDR (Available financing in (Direct financing not the UK market ) allowed temporarily)

LSE SHSE

Foreign investors A shares investors (Outside China investors) (Chineses investors)

Source: wallstreetcn.com

Eastbound trading refers to the trading of the Chinese depository receipts (CDRs) issued by the companies listed on the London Stock Exchange on the main board of the Shanghai Stock

Exchange.10 The issuers of the CDRs of the eastbound trading must be those high-quality companies listed on the LSE for at least three years with a market capitalization of over 20 billion CNY. 11 Designated brokers acting in both Shanghai and London ensure the

10 According to the joint announcement, the total quota of the eastward business is 250 billion CNY. 11 As the HM Treasury announced, over 260 of the SSE-listed companies are potentially eligible to take part in the SLSC program and list on the London Stock Exchange. 38

exchangeability between the CDRs and the underlying securities. Investors trade the CDRs of London-listed companies in Shanghai, following all the rules and practices of the SSE.

Regular trading of CDRs involves the creation of CDRs on T+2 and the redemption that takes place on T+3. Qualified investors wishing to trade the CDRs need to have no less than 3 million cash and securities in their accounts.

2. The Shanghai-Hong Kong Stock Connect Program

Officially launched on November 17th, 2014, the Shanghai-Hong Kong stock connect is a mutual order-routing program jointly created by the Hong Kong Exchanges and Clearing

Limited (HKEX), Shanghai Stock Exchange (SSE) and China Securities Depository, and the

Clearing Corporation Limited (ChinaClear). It enables investors of their respective markets to trade designated securities in each other’s markets through the trading and clearing facilities of their home exchange. 12 Under the program, the Hong Kong investors trade all the constituent stocks from the Shanghai 180 Index, the Shanghai 380 Index, and the eligible SSE- listed A shares, in the SSE market (i.e., northbound trading). Also, mainland investors trade the constituent stocks of the Hang Seng Composite Large Cap Index, the Hang Seng Index

Composite Mid-Cap Index, and the eligible H shares, in the Hong Kong Stock Exchange (i.e., southbound trading). The Hong Kong Securities Clearing Company Limited (HKSCC) and

China Securities Depository and Clearing Corporation Limited (ChinaClear) are responsible for the clearing, settlement, and other related services of the trades executed by their respective market’s participants and investors.13 The clearing and settlement cycle ensures a stock settlement on the same day (T) while the cash settlement for SSE trades takes place

12 https://www.hkex.com.hk/Mutual-Market/Stock-Connect?sc_lang=en 13 https://www.hkex.com.hk/-/media/hkex-market/news/news-release/2014/141214news/investor_faq_en 39

one day later (T+1). Trading under the Shanghai-Hong Kong stock connect is subject to the investment quota, monitored by the SEHK and SSE collectively. The northbound aggregate quota is set at 300 billion CNY. The southbound aggregate quota is set at RMB 250 billion

CNY.

Undoubtedly, the Shanghai-Hong Kong stock connect provides investors a trading window for investing in the Chinese A shares, but for any investor overseas, barriers to investing directly in the Chinese A shares still exist because they must trade SSE securities via Hong

Kong. Hence, when comparing with the new Shanghai-London stock connect program, the

Shanghai-Hong Kong stock connect inevitably provides less direct access to the mainland stock market for global investors. Finally, under the SHSC program, only eligible Mainland institutional investors and those individual investors14 can trade SEHK Securities through the stock connect.

3. Unit Root Tests (ADF, PP, and KPSS)

The PP tests on rv, rv, and rv suggest that the null hypothesis of non-stationarity is rejected when the p-value is less than 5% significance level in all three sample periods. Thus, the logarithmic realized volatilities of Shanghai, Hong Kong, and London market indices are

(weakly) stationary. However, the ADF and KPSS tests on rv, rv, and rv show different results compared with those of the PP test. For example, the ADF test shows stationarity properties only for rv and rv during the full sample period and for rv during the post-stock connect period.

14 The criterion for eligible individual investors is those who hold an aggregate balance of not less than RMB 500,000 in their securities and cash accounts. 40

Table 3: Unit Root Tests on the Logarithmic Realized Volatilities of the Shanghai, Hong Kong, and London Stock Market Index (SSEC, HSI, and UKX) Returns

Variable ADF Prob PP test Prob KPSS LM-Stat Panel I: Full sample

rv -1.635 0.463 -6.632 0.000 0.251

rv -1.920 0.322 -8.103 0.000 1.268

rv -3.078 0.030 -8.453 0.000 0.866 Panel II: Under Shanghai-Hong Kong Stock Connect

rv -2.794 0.062 -5.991 0.000 0.190

rv -8.369 0.014 -7.832 0.000 1.025

rv -3.124 0.027 -7.880 0.000 0.953 Panel III: Under Shanghai-London Stock Connect

rv -5.635 0.000 -5.636 0.000 0.235

rv -5.551 0.000 -5.551 0.000 0.290

rv -2.334 0.169 -5.299 0.000 0.152

Notes: 1) The PP tests on 푟푣, 푟푣, and 푟푣 suggest that the null hypothesis of non-stationarity is rejected, given that the p-value is less than 5% significance level in all three sample periods; 2) The ADF and KPSS tests on

푟푣 , 푟푣, and 푟푣 show different results compared with those of the PP test.

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