Abstract
This study examines China’s influence in the Asia–Pacific stock markets by focusing on spillover effects and market integration and employs how the financial crises and financial liberalization affect the relationship among these markets. Based on the series of studies of Diebold and Yilmaz (2009, 2012, 2015), this study employs the generalized vector autoregressive framework to examine the spillover effects among the main Asia–Pacific stock markets. The multifactor R-squared measure proposed by Pukthuanthong and Roll (2009) is employed to examine the market integration of Chinese stock market. The results indicate that spillover effects and market integration tend to increase, indicating that China stock market is playing a more important role in the Asia–Pacific stock markets. This study provides more evidence that financial crises and financial liberalization can strengthen spillover effects and market integration.
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Notes
In our robust test, the 300-, 600-, and 1000-day rolling samples are also used to estimate the models. Similar patterns of spillover dynamics are found.
We used the first six principal components, which generally account for close to 86% of the cumulative eigenvalues on average over the sample.
To examine the sensitivity of the results to the choice of the order of VAR and choice of forecast horizon, we also calculate the spillover index for orders 2 to 6 of VAR and for forecast horizons varying from 5 to 10 days. All results are not sensitive to the choice of the order of VAR or the choice of the forecast horizon.
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Acknowledgements
This research is supported by the projects of China Postdoctoral Science Foundation (No. 2018M643213 and 2018M640830) and the National Social Science Foundation of China (NSSF 17CTQ030).
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Appendices
Appendix 1: Static full-sample spillover tables
The VAR model is selected by the Akaike Information Criteria (AIC). To be consistent with the studies of Diebold and Yilmaz (2009, 2012), the generalized variance decompositions of 10-day-ahead forecast errors are selected to calculate the spillover index.Footnote 3 Following Diebold and Yilmaz (2009), we calculate the forecast error variance decompositions for return spillovers and volatility spillovers, respectively. The results are presented in Tables
6 and
7, which are labeled as Spillover Tables. The ijth entry is the estimated contribution to the forecast error variance of market \(i\) coming from innovations to market \(j\). The off-diagonal column sums and the row sums are the “to” and “from” directional spillovers, respectively, and the differences between “contribution to others” and “contribution from others” are net spillovers. The total spillover indices for return and volatility are in the lower right corners of Tables 6 and 7.
Several interesting findings can be obtained from the spillover tables. Firstly, for the return spillovers, the markets affecting others the most are the Hong Kong, Korea, Japan and Singapore stock markets, whereas the Australia, Singapore, Malaysia, and Hong Kong stock markets are most affected by others. The contribution of the Chinese stock market to others is 20.97%, and the contribution from others to the Chinese stock market is 29.32%, which means that the Chinese stock market is the return spillover receiver rather than the giver. Secondly, for the volatility spillovers, the markets affecting others, the most are the Korea, Hong Kong, Indonesia, Japan and Singapore stock markets, whereas the Australia, Singapore, Hong Kong, and Taiwan stock markets are most affected by others. The contribution of the Chinese stock market to others is 9.36%, and the contribution from others to the Chinese stock market is 9.53%, which means that the Chinese stock market is the volatility spillover receiver rather than the giver. In addition, for the total spillover indices, return and volatility spillovers are of the same magnitude which is consistent with the study of Diebold and Yilmaz (2009).
Appendix 2: Net pairwise spillovers between Chinese and other stock markets
See Figs.
Net pairwise spillovers between the Chinese and Hong Kong stock markets
Net pairwise spillovers between the Chinese and Taiwanese stock markets
Net pairwise spillovers between the Chinese and Japanese stock markets
Net pairwise spillovers between the Chinese and Korean stock markets
Net pairwise spillovers between the Chinese and Singaporean stock markets
Net pairwise spillovers between the Chinese and Australian stock markets
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Ma, X., Zou, D., Huang, C. et al. China’s growing influence and risk in Asia–Pacific stock markets: evidence from spillover effects and market integration. Risk Manag 22, 338–361 (2020). https://doi.org/10.1057/s41283-020-00065-0
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DOI: https://doi.org/10.1057/s41283-020-00065-0