Individual institutions marginal contributions to the systemic risk contain predictive power for its potential future exposure and provide early warning signals to regulators and the public. We use higher-order co-skewness and co-kurtosis to construct systemic risk contribution measures, which allow us to identify and characterize the co-movement driving the asymmetry and tail behavior of the joint distribution of asset returns. We illustrate the usefulness of higher-order moment augmented approach by using 4868 stocks living in the Chinese market from June 2002 to March 2022. The empirical results show that these higher-order moment measures convey useful information for systemic risk contribution measurement and portfolio selection, complementary to the information extracted from a standard principal components analysis.

We analyze the latent factors for the Chinese market through the recently proposed risk premium principal component analysis (RP-PCA). Our empirical research covers 95 firm characteristics. We demonstrate that the RP-PCA on the Chinese market can identify factors that capture co-movements and explain pricing. Compared to the traditional PCA approach, it explains a larger proportion of return variation in both double-sorted and single-sorted portfolios. The Sharpe ratios of the tangency portfolios are significantly higher than those of the standard PCA. Additionally, we show that the RP-PCA loadings are more closely associated with factor returns.

This paper investigates the link between firm headquarters location and firm stock return co-movements in a sample of Chinese firms spanning the years 1999 to 2007. The empirical results show a strong co-movement pattern of firms located in the same province. Moreover, both firm-level and provincial-level factors are found to influence this co-movement, including firm size and ownership structure at firm level and GDP per capita and financial depth at provincial level. A subsample of firms listed in the Shenzhen Stock Exchange shows that better firm-level information quality reduces local co-movements.

The current study documents an interesting phenomenon that retail investors prefer to invest in stocks listed at the stock exchange that is geographically close to them in China. This pattern is robust when we control for the well-documented local bias within a country. Among companies with similar distances to both stock exchanges, investors still display a much stronger tendency to invest in locally-listed companies. Among stocks with similar distances to both stock exchanges, those listed in Shanghai (Shenzhen) co-move more in returns and volume, with the benchmark at the Shanghai (Shenzhen) stock exchange. Such a preference for local exchange seems not to be motivated by information advantage, because investors do not obtain abnormal returns from their trades on stocks listed nearby. Our findings provide additional evidence that non-information-based familiarity bias induces investment and that such investor biases and exchange-level sentiment influence asset price formation.

Current integration and co-movement among international stock markets has been boosted by increased globalization of the world economy, and profit-chasing capital surfing across borders. With a reputation as the fastest growing economy in the world, China’s stock market has continued gaining momentum during recent years and incurred growing attention from academicians, as well as practitioners. Taking into account economic and geographical considerations, the US and Hong Kong are considerably the most comparable stock markets to China. As the usual vector error correction model (VECM) could overlook the long memory feature of cointegration residual series, which can in turn exert bias on the resulting inferences, we chose to employ a fractionally integrated VECM (FIVECM) in this paper to investigate the long-term cointegration relations binding China’s stock market to the aforementioned stock markets. In addition, by augmenting the FIVECM with multivariate GARCH model, the return transmission and volatility spillover between market return series were revealed simultaneously. Our empirical results show that China’s stock market is fractionally cointegrated with the two markets, and it appears that China’s stock market has stronger ties with its neighboring Hong Kong market than with the world superpower, the US market.