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For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

The purpose of this paper is to examine the impact of structural breaks on the conditional variance of carbon emission allowance prices.

The authors employ the symmetric GARCH model, and two asymmetric models, namely the exponential GARCH and the threshold GARCH.

The authors show that the forecast performance of GARCH models improves after accounting for potential structural changes. Importantly, we observe a significant drop in the volatility persistence of emission prices. In addition, the effects of positive and negative shocks on carbon market volatility increase when breaks are taken into account. Overall, the findings reveal that when structural breaks are ignored in the emission price risk, the volatility persistence is overestimated and the news impact is underestimated.

The authors are the first to examine how the conditional variance of carbon emission allowance prices reacts to structural breaks.

This article examines the effect of China's soft power projection through the Chinese media in Africa on the African audiences' perception of China's national image through a case study of the residents of Nairobi. It adopted Joseph Nye's soft power model and sought to address three fundamental questions : What is the extent and objective of China's media diplomacy in Africa? How has China's media “offensive” in Africa impacted African audiences' imagery of China? What are the implications, if any, of China's media diplomacy on the Kenyan public view of China?

This article used a mixed-methods research design, which deployed elements of positivism and interpretivism. It used a deductive approach and deployed the survey strategy, which entailed the collection of data from Nairobi city residents. The secondary data were collected from relevant academic literature sources. The primary data were analyzed empirically using the Statistical Package for the Social Sciences (SPSS), while the secondary data were analyzed using discourse analysis.

China's soft power projection through the Chinese media in Africa is aimed at addressing the “misinformation” about China. China seeks its share of representation among the African public to correct negative perceptions of China. Kenyans had a generally positive attitude toward China. South Africa and Angola have “Fairly” positive perceptions of the Chinese media. However, this study did not reveal whether the perception was due to the influence of Chinese media. These findings implied that the African public's positive imagery of China cannot be fully attributed to Chinese media's influence.

This study is groundbreaking in that it is one of the few studies that have focused on China's public diplomacy in Africa and assessed the impact of Chinese media on the African public’s perception of China.

This study aims to investigate affective commitment (ACS) and leader–member exchange (LMX) as resources mitigating against burnout and counterproductive work behaviours (CWBs) in the hospitality sector, and examines the effect of zero-hour contracts on these relationships.

Through conservation of resources theory, this study tests a framework exploring ACS and LMX as resources against burnout and CWBs, using a data set of 260 frontline hospitality employees working in Ireland, considering zero-hour contracts as a moderator.

Findings indicate that burnout is associated with CWB, and ACS and LMX are resources against burnout and CWB. Furthermore, zero-hour contract perceptions moderate the resource effect of ACS and LMX. Yet, zero-hour contract perceptions do not moderate the relationship between burnout and CWB, indicating these employees may be caught in a resource-loss spiral.

This study proposes mechanisms to enhance resources against burnout, with specific strategies to support young employees who are more likely to experience burnout. As findings suggest unique negative impacts of burnout for employees on zero-hour contracts, this paper also provides guidance to support these vulnerable employees.

This study provides unique insights into hospitality employees’ ability to harness resources against burnout and CWB consequences of burnout. The results indicate that perceived precarity does not moderate these relationships, suggesting that burnout affects this cohort differently.