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This article examines the role of oil price uncertainty measured by the crude oil volatility index (OVX) in the risk‐return relation of stock markets from oil‐importing and exporting countries with the extended GARCH‐M models. It is found that oil price uncertainties have significant impacts on the stock risk‐return relationship in oil importers and exporters. Specifically, there is a positive risk‐return relation during the decreasing period of oil price uncertainty. This positive correlation will be undermined and become negative during the rising period of oil price uncertainty in most countries studied. What's more, change in oil price uncertainty negatively affects the stock risk‐return relation in general, and it has a more significant asymmetric effect in oil exporters than that in oil importers for the whole sample. In addition, we examine whether the impact of oil price uncertainty is sensitive to the global financial crisis in 2008. Our empirical results reveal that, on average, the stock risk‐return relation is more susceptible to OVX changes after the crisis period than that during the crisis period, suggesting impacts of OVX changes are undermined due to the extremely unstable global economy. [ABSTRACT FROM AUTHOR]

Using the Chinese equity market as the testing venue, this study explores how investor sentiment affects the immediate reaction of stock prices to earnings news in high‐ and low‐sentiment periods. Our key finding is that the sentiment‐driven pricing of earnings will differ between the two periods. Specifically, during high‐sentiment (low‐sentiment) periods, the stock price sensitivity to good (bad) earnings news increases (decreases) with investor sentiment, whereas the stock price sensitivity to bad (good) earnings news is unrelated to investor sentiment. Additionally, we find that the effect of sentiment is more pronounced for young, high volatility, growth and distressed stocks. However, contrary to the U.S. evidence, our results show that small stocks are not always more exposed to sentiment. Further analysis of the role of short‐sales constraints in the sensitivity of stock prices to bad earnings news implies that short sales can enhance informational efficiency. [ABSTRACT FROM AUTHOR]

This article investigates the effect of cross‐shareholdings on stock price synchronicity, as a measure of price informativeness, of the listed firms in the Chinese stock market. We gauge firms' levels of cross‐shareholdings in terms of centrality in the cross‐shareholding network. It is confirmed that it is through a noise‐reducing process that cross‐shareholdings promotes price synchronicity and reduces price delay. More importantly, this effect on price informativeness is pronounced for large firms and in the periods of market downturns. Overall, our analyses provide insights into the relation between the ownership structure and price informativeness. [ABSTRACT FROM AUTHOR]

In this paper, we apply two nonparametric approaches to mean absolute deviation (MAD) portfolio selection model. The first one is to use the nonparametric kernel mean estimation to replace the returns of assets with five different kernel functions. Then, we construct the nonparametric kernel mean estimation-based MAD portfolio model. The second one is to utilize the nonparametric kernel median estimation to replace the returns of assets with five different kernel functions. Then, we construct the nonparametric kernel median estimation-based MAD portfolio model. We also extend the two kinds of nonparametric approach to mean-Conditional Value-at-Risk portfolio model. Finally, we give the in-sample and out-of-sample analysis of the proposed strategies and compare the performance of the proposed models by using actual stock returns in Shanghai stock exchange of China. The experimental results show the nonparametric estimation-based portfolio models are more efficient than the original portfolio model. [ABSTRACT FROM AUTHOR]

This paper examines whether the equity market uncertainty (EMU) index contains incremental information for forecasting the realized volatility of crude oil futures. We use 5-min high-frequency transaction data for WTI crude oil futures and develop six heterogeneous autoregressive (HAR) models based on classical HAR-type models. The empirical results suggest that EMU contains more incremental information than the economic policy uncertainty (EPU) for forecasting the realized volatility of crude oil futures. More importantly, we argue that EMU is a non negligible additional predictive variable that can significantly improve the 1-day ahead predictive accuracy of all six HAR-type models, and improve the 1-week ahead forecasting performance of the HAR-RV, HAR-RV-J, HAR-RSV, HAR-RV-SJ models. These findings highlight a strong short-term and a weak mid-term predictive ability of EMU in the crude oil futures market. [ABSTRACT FROM AUTHOR]

This article mainly focuses on investigating the nonlinear co-integration and nonlinear causality relationships between oil prices and Chinese stock market at the overall and sectoral levels by using nonlinear autoregressive distributed lags (NARDL) model and Diks and Panchenko (DP) test. The empirical results show that there are not significantly asymmetric co-integration effects between oil prices and Chinese stock market for the overall and sectoral levels. However, the significantly nonlinear causality between oil prices and Chinese stock market can be found. Specifically, oil prices can widely affect Chinese stock indices through nonlinear channel. The cases in the reverse also work for overall indices and Mining, Utilities, Financial and Real Estate sectors. Furthermore, the potential sources of these nonlinear causality linkages are examined. The results suggest that volatility persistence rather than asymmetrical co-integration is the major factor that accounts for the nonlinear causality between oil prices and Chinese stock market. [ABSTRACT FROM AUTHOR]

The Pearson correlation coefficient is used by many researchers to construct complex financial networks. However, it is difficult to capture the structural characteristics of financial markets that have extreme fluctuations. To solve this problem, we resort to tail dependence networks. We first build the edge information of the stock network by adopting Pearson's correlation coefficient and the symmetrized Joe–Clayton copula model, respectively. By using the planar maximally filtered graph method, we filter the edge information, obtain Pearson's correlation coefficient and tail dependence network, and compare their efficiencies. The community structure of the constructed networks is investigated. We find that the global efficiency of tail‐dependent networks is higher than that of the Pearson correlation networks. Further analysis of the nodes in the upper‐ and lower‐tail dependence networks reveals that the European markets are more influential than Asian and African markets during a booming market and a recession market. In addition, different cliques are found in the two tail dependence networks. The finding indicates that financial risks will impact geographically adjacent markets. [ABSTRACT FROM AUTHOR]

The Hurst exponent derived by the R/S analysis method of Shanghai stock market's logarithmic return series is about 0.6298. This shows that the Shanghai stock market exhibits fractal features, and a long memory cycle of about one-and-a-half years. With the reconstruction of phase space, the Shanghai Stock attractor dimension converges to 1.335, which means that the Shanghai stock market has chaotic features, and constructing a dynamic system of the Shanghai stock market needs at least two variables. The findings from the principal component analysis support the conclusion of the existence of chaotic features of the Shanghai stock market. The fractal and chaotic features of the Shanghai stock market reveal the nonlinear properties of the Chinese stock market, and the nonlinearity perspective will be more conducive to the formulation of countermeasures for the development of the Chinese stock market. [ABSTRACT FROM AUTHOR]

In this paper, a copula-based correlation measure is proposed to test the interdependence among stochastic variables in terms of copula function. Based on a geometric analysis of copula function, a new derivation method is introduced to derive the Gini correlation coefficient. Meantime theoretical analysis finds that the Gini correlation coefficient tends to overestimate the tail interdependence in the case of stochastic variables clustering at the tails. For this overestimation issue, a fully new correlation coefficient called Co is developed and extended to measure the tail interdependence. Empirical study shows that the new correlation coefficient Co can effectively solve the overestimation issue, which implies that the proposed new correlation coefficient is more suitable to describe the interdependence among stochastic variables than the Gini correlation coefficient. [ABSTRACT FROM AUTHOR]