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Comovements between Chinese and global stock markets: evidence from aggregate and sectoral data

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Abstract

This paper investigates the dynamic correlations between Chinese stock returns and global markets at both the market and sectoral levels. Statistics suggest that stock-return correlations across markets are time-varying and display structural breaks. An upward shift in stock return correlations is associated with China’s adoption of a higher degree of financial liberalization. The evidence indicates that the stock returns of the financial sector exhibit the highest correlation across countries among 10 sectors. Low correlations are present in the Health Care, Telecommunications, and Utilities sectors. The correlations are closely tied to geographic location: the correlation with Hong Kong is the highest, followed by South Korea, Japan, Europe and the US. The time-series stock-return correlations are positively correlated with the conditional variance and variance premiums.

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Notes

  1. This big market swing may occur for many reasons. One key reason is that the Chinese market has been riding a rapid upward swing since 2005, when a price bubble, along with investors’ herding behavior, intensified the rise in prices (Tan et al. 2008; Chiang and Zheng 2010). The fear of a contagion effect triggered by the US credit crisis led to a large price reversal, resulting in big losses to investors in the Chinese market. Obviously, there are asymmetric effects on price movements in relation to the source of a shock. A shock that originates in the US markets has a more profound impact than a shock from the Chinese market. The US market is characterized by sound banking systems and deep capital markets (Rojas-Suarez 2014), and the immediate liquidity provision by the Fed alleviates the pressure of liquidity and the fear; the market’s efficiency and resiliency under an adequate regulatory and supervisory structure help to restore market stability, leading to fewer losses than in China’s market. On the other hand, the investors in Chinese markets are made up of a large number of inexperienced small investors, and their investment decisions tend to herd with those of financial gurus, leading to large movements in market prices (Tan et al. 2008; Chiang and Zheng 2010).

  2. For this reason, we do not use a dummy variable approach to capture a discrete change due to an external shock or policy innovations. Lin and Teräsvirta (1994) provide a testing procedure to tackle continuous structural change.

  3. Dungey et al. (2005) provide a review of methodologies in this line of approach.

  4. Yu et al. (2010) provide a good summary of methodologies for testing financial market integration.

  5. Bekaert et al. (2009) introduce a market factor, size factor, and value factor and other style factors as exogenous variables in their factor models. In addition, Colacito et al. (2011) recently developed a component model for modeling dynamic correlations. It combines the ingredients of Engle’s (2002) DCC model and the Engle and Lee (1999) component GARCH model. This GARCH-MIDAS specification allows researchers to extract a long-run correlation component via mixed data sampling. The shortage of high-frequency observations for the sectoral data, especially in the Chinese market, prevents us from having the MIDAS touch with mixed frequencies in estimations.

  6. A typical element of Q t is given by \(q_{ij,t} = (1 - a - b)\overline{\rho }_{ij} + au_{i,t - 1} u_{j,t - 1} + bq_{ij,t - 1} ,\) where \(\overline{\rho }_{ij}\) is the unconditional correlation of \(u_{i,t} u_{j,t} .\)

  7. Let θ denote the parameters in D t , and ϕ the parameters in P t , then the log-likelihood function is:

    \(l_{t} (\theta ,\varphi ) = \left[ { - \frac{1}{2}\sum\limits_{t = 1}^{T} {\left( {n\log (2\pi ) + \log |D_{t} |^{2} + \varepsilon_{t}^{{\prime }} D_{t}^{ - 2} \varepsilon_{t} } \right)} } \right] + \left[ { - \frac{1}{2}\sum\limits_{t = 1}^{T} {\left( {\log |P_{t} | + u_{t}^{{\prime }} P_{t}^{ - 1} u_{t} - u_{t}^{{\prime }} u_{t} } \right)} } \right].\)

    The first part of the likelihood function in the above equation is volatility, which is the sum of individual GARCH likelihoods. The log-likelihood function can be maximized in the first stage over the parameters in \(D_{t}\). Given the estimated parameters in the first stage, the correlation component of the likelihood function in the second stage (the second part of the above equation) can be maximized to estimate correlation coefficients.

  8. Both A and B shares are listed on the Shanghai (SH) Stock Exchange (SHSE) and the Shenzhen (SZ) Stock Exchange (SZSE), namely, SHA, SHB, SZA, and SZB in China. This study focuses on SHA, rather than SZA or SZB, since the SHA are dominant in media and public information. Most previous studies used SHA. The evidence suggests that the correlation between Shanghai and Shenzhen stock returns is rather high. Chiang et al. (2007b) find the correlation of stock returns between SHA and SZA for the period 1/1/1999- 6/30/2003 is 0.965. To save space, we feel the analysis of SHA is sufficient to demonstrate our empirical evidence.

  9. For instance, on May 18, 1995, Shanghai security composite index was up 28 % in one trading day as the Chinese authorities shut down the Treasury bond futures market (China Security Journal, May 18. 1995). On May 23, 1995, the market was reversed and fell by 18.42 %. The failure of China’s first financial futures market was attributed to weak supervision from both the government and the stock exchanges, as argued by Lan (2013). These outliers were excluded from our sample.

  10. In revising this paper, the authors calculated the unconditional correlation of stock market returns between China’s and Hong Kong’s markets for the sample period November 17, 2014 to May 19, 2015. The correlation value is 0.90 (t = 23.29), partly because of the policy promotion of “the Shanghai stock exchange and Hong Kong stock exchange connect” program launched on November 17, 2014.

  11. Health-care data are unavailable for the Hong Kong market. Thus, we have only 9 correlation series between China and Hong Kong.

  12. The current model is relevant to analyze the dynamic relations between different sectors within a particular country. For instance, China is in the process of transitioning from a manufacturing economy to one based more on services. The dynamic correlations between commodities and services can be traced out by following the procedure outlined in this paper.

  13. In the absence of health-care data for Hong Kong, the Japanese market shows the highest correlation with China for this sector.

  14. An anonymous referee pointed us toward this reasoning.

  15. Horejs (2012) demonstrates that long-short equity funds lost much less than the market during the 2000–02 and 2007–09 financial crises and notes that this strategy may explain why long-short equity funds outperformed the S&P 500 over the period 2001–2011.

  16. For the sample period between 1/03/1994 and 11/16/2014, the correlation of stock returns is 0.80 (t = 94.93). After the Shanghai and Hong Kong connection of 11/17/2014, the correlation of stock prices between the two markets for the period 11/17/2014–5/19/2015 is 0.90 (t = 23.29); the correlation is up by 10 %, showing the impact of policy influence. We would like to thank an anonymous referee who suggested that we look at this impact.

  17. On November 10, 2001, the World Trade Organization (WTO) officially approved China's entry as a member. (http://www.wto.org/english/thewto_e/countries_e/china_e.htm#top). QFII is a program that was launched in 2002 in the People's Republic of China to allow licensed foreign investors to buy and sell yuan-denominated A-shares in China's mainland stocks on the Shanghai and Shenzhen exchanges. As reported by the China Securities Regulatory Commission, the number of licensed QFII investors increased from 12 in December 2003 to 263 in March 2014 (see http://china-xbr.com/xbr-quota-data/qfii/). The QFII from the top three markets are Hong Kong (42), the US (42) and the UK (22).

  18. Whaley (2009) observes that the Chicago Board Options Exchange (CBOE) volatility index (VIX) spikes during periods of market turmoil, reflecting market conditions: if expected market volatility increases (decreases), stock prices fall (rise), and investors require a higher (lower) rate of return to compensate for risk taking.

  19. In our early study, the conditional variances in the exchange rate of the Chinese yuan and different foreign currencies were also included. However, the testing results suggest that most of them are statistically insignificant. One of the main reasons to exclude this variable is that the bilateral exchange rates between China and the markets under investigation do not change frequently enough, especially with respect to Hong Kong’s currency. As a result, the inclusion of exchange rate uncertainty does not play a significant role in the test equation.

  20. The VIX index (VXFXI) in the Chinese stock market is not available until 12/31/2012. The limited observations of this data set prevent us from using it in the estimations. For this reason, we do not include the Chinese VIX in this model.

  21. One can include these explanatory variables in the original equation to model stock returns and to detect their explanatory power. However, we use these variables in Eqs. (10) and (11), which enables us to test their impact on the stock-return correlations.

  22. Alternatively, we measure \(\widehat{V}_{S,j,t}\) by using the residual series obtained from regressing VIX S,j,t on the conditional variance, \(\widehat{\sigma }_{S,j,t}^{2}\). To save space, we do not report it in Table 7. The estimated results, however, are similar to those in Panel A, which are available upon request.

  23. Since the dependent variable is bound to interval [−1, + 1], we apply a Fisher transformation, \(\left( {\widehat{{\widehat{\rho }}}_{i,j,t} = \frac{1}{2}\ln \left[ {\frac{{1 + \widehat{\rho }_{i,j,t} }}{{1 - \widehat{\rho }_{i,j,t} }}} \right]} \right)\), on the correlation coefficient first and then conduct the regression estimation. We report only the mean equation and do not report the variance equation to save space. The results of the variance equation are available upon request.

  24. Given the dominant role of the US market, the variations in the US’s VIX will soon disseminate to the global markets through various contagion channels, as witnessed in the 2008 financial crisis. The late availability (starting on 12/31/2012) of the Chinese VIX data prevents us from including the Chinese VIX in the test.

  25. Oil prices in the Chinese market are highly regulated, which prevents frequent changes in prices. This price stickiness moderates the uncertainty; prices in other markets are mainly market driven, producing a higher degree of price uncertainty. The unparalleled price changes and different degrees of sensitivity of stock markets in reacting to oil shocks could lead to more parametric uncertainty.

  26. The parameters will be estimated by the log-maximum likelihood method. The density function in Nelson (1991) is given by

    \(f(\mu_{\tau ,t} ,\sigma_{\tau ,t} ,\nu ) = \frac{{\nu [\varGamma (3/\nu )]^{1/2} }}{{2[\varGamma (1/\nu )]^{ - 3/2} \sigma_{\tau ,t} }}\exp \left[ { - \left| {\frac{{\varepsilon_{\tau ,t} }}{{\sigma_{\tau ,t} }}} \right|^{\nu } \left[ {\frac{\varGamma (3/\nu )}{\varGamma (1/\nu )}} \right]^{\nu /2} } \right]\)

    where \(\varGamma ( \cdot )\) is the gamma function and \(\nu\) is a scale parameter or degree of freedom to be estimated. For \(\nu = 2\), the GED yields the normal distribution, while for \(\nu = 1\) it yields the Laplace or double-exponential distribution. Given initial values of \(\varepsilon_{\tau ,t}\) and \(\sigma_{\tau ,t}^{2}\), the parameter vector \(\varTheta = \left\{ {\mu_{\tau } , \sigma_{\tau } ,\phi_{\tau } , \varepsilon_{\tau } , \nu } \right\}\) can be estimated by the log-maximum likelihood method (log-MLE) over the sample period. The log-maximum likelihood function can be expressed as

    \(L(\varTheta ) = \sum\limits_{t = 1}^{T} {\log f(\mu_{\tau ,t} ,\sigma_{\tau ,t} ,\nu )}\)

    where \(\mu_{\tau ,t}\) is the conditional mean and \(\sigma_{\tau ,t}\) is the conditional standard deviation. Since the log-likelihood function is non-linear, the numerical procedure is used to derive estimates of the parameter vector.

  27. We did not report the estimates of the variance equations to save space. However, all of the coefficients satisfy the parameter restrictions and are consistent with a clustering phenomenon.

  28. After fitting the LSTM or detrended data, the effect of the QFII becomes less significant.

  29. To save space, we report only the results of the test equation based on the variance premium \(\widehat{V}_{S,j,t}\) derived from the residual by regressing VIX j,t on \(\widehat{\sigma }_{S,j,t}^{2}\). However, the test results are available upon request.

  30. Bekaert et al. (2012) show that there are great interactions between monetary policy and the variance premium, and it is likely that both stock returns and the variance premium are endogenous to changes in monetary policy. A further investigation along this line is beyond the scope of the current study.

  31. The model is able to test whether the Shanghai-Hong Kong stock exchange connection announced in 2014 has a significant impact on the correlation of the two stock markets.

  32. Given a relatively restricted investment policy and capital control in China, investments in Hong Kong’s market provide a good forum for understanding Chinese markets. The Shanghai-Hong Kong stock exchange connection certainly provides a channel for accessing the Chinese equity market.

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Authors and Affiliations

  1. Drexel University, 3220 Market Street, Philadelphia, PA, 19104, USA

    Thomas C. Chiang

  2. Fudan University, 670 Guoshun Road, Shanghai, 200433, People’s Republic of China

    Lanjun Lao

  3. CES Finance Holding Co. Ltd., 686 Wuzhong Road, Shanghai, 201103, People’s Republic of China

    Qingfeng Xue

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  1. Thomas C. Chiang
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  2. Lanjun Lao
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  3. Qingfeng Xue
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Correspondence to Thomas C. Chiang.

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Chiang, T.C., Lao, L. & Xue, Q. Comovements between Chinese and global stock markets: evidence from aggregate and sectoral data. Rev Quant Finan Acc 47, 1003–1042 (2016). https://doi.org/10.1007/s11156-015-0529-x

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