The role of cross-market linkages in the occurrence of tail events in stock and energy markets has not yet been fully understood in the contagion literature. This paper investigates the contagion from oil prices to Chinese stock sectors by considering differences between extreme positive returns and extreme negative returns. We compute time-varying cutoffs by employing a generalized Pareto distribution (GPD) function to estimate excess returns. We then use a multinomial logit (MNL) model to examine the probability of Chinese stock sector co-exceedances associated with oil price exceedances. Our results indicate that, compared to common domestic factors, the contagion between oil price and stock sectors is relatively weak, but never negligible. We argue that faced with volatile oil prices during turbulent periods, the existence of any contagion weakens the benefits of portfolio diversification related to oil and Chinese stock sector investment. Based on our findings, investors holding a portfolio of oil and Chinese sector stocks should pay special attention to the extreme changes in crude oil prices and adopt hedging measures to protect their portfolio from extreme shocks to oil markets.

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Research in the area of tail dependence between oil prices and the stock market is sparse, particularly at the firm level. This article investigates lower and upper tail dependences between the price of crude oil and China's A‐share market by estimating an empirical copula with a rolling window. Our results show that tail dependence is increasing over time and that there are differences between lower and upper tail dependences in terms of incremental magnitude. We also find that the impulse responses of tail dependences to shocks to variables of interests vary significantly over the sample period. Our results also indicate that lower tail dependence, in particular, is found to have more than one breakpoint, and the break dates are highly associated with financial crises. In addition, we find evidence of asymmetry in tail dependence, which varies across periods. Finally, we find that tail dependence is persistent in the short‐term but deteriorates as the duration increases. These findings have important implications for investors, risk managers and policy makers.

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The involvement of the tumor microenvironment (TME) in the biology of gliomas has expanded, while it is yet uncertain its potential of supporting diagnosis and therapy choices. According to immunological characteristics and overall survival, cohorts of glioma patients from public databases were separated into two TME‐relevant clusters in this analysis. Based on differentially expressed genes between TME clusters and correlative regression analysis, a 21‐gene molecular classifier of TME‐related prognostic signature (TPS) was constructed. Afterward, the prognostic efficacy and effectiveness of TPS were assessed in the training and validation groups. The outcome demonstrated that TPS might be utilized alone or in conjunction with other clinical criteria to act as a superior prognostic predictor for glioma. Also, high‐risk glioma patients classified by TPS were considered to associate with enhanced immune infiltration, greater tumor mutation, and worse general prognosis. Finally, possible treatment medicines specialized for different risk subgroups of TPS were evaluated in drug databases.