This paper adds to the growing literature of cryptocurrency and behavioral finance. Specifically, we investigate the relationships between the novel investor attention and financial characteristics of Bitcoin, i.e., return and realized volatility, which are the two most important characteristics of one certain asset. Our empirical results show supports in the behavior finance area and argue that investor attention is the granger cause to changes in Bitcoin market both in return and realized volatility. Moreover, we make in-depth investigations by exploring the linear and non-linear connections of investor attention on Bitcoin. The results indeed demonstrate that investor attention shows sophisticated impacts on return and realized volatility of Bitcoin. Furthermore, we conduct one basic and several long horizons out-of-sample forecasts to explore the predictive ability of investor attention. The results show that compared with the traditional historical average benchmark model in forecasting technologies, investor attention improves prediction accuracy in Bitcoin return. Finally, we build economic portfolios based on investor attention and argue that investor attention can further generate significant economic values. To sum up, investor attention is a non-negligible pricing factor for Bitcoin asset.

Based on carbon spot prices selected from seven carbon pilots, we assess the financial performances related to carbon volatility in China on the overall perspective. According to the results, the Chinese carbon market fluctuated severely at the beginning of carbon trading, but has stabilised in general, despite several dramatic changes related to ‘yearly compliance events’. Long-term memory exists in the volatility series. Moreover, asymmetry exists in the Chinese carbon market, and volatility reacts more severely to good news than to bad news. Finally, we discuss our empirical results, and make certain suggestions regarding firms’ awareness, international cooperation and individual investors not only for policy makers in China but also for other developing countries who are contemplating either commencing carbon trading or improving the current market.

Carbon allowances traded in the EU-Emission Trading Scheme (EU-ETS) were initially designed as an economic motivation for efficiently curbing greenhouse as emissions, but now it mimics quite a few characteristics of financial assets, and have now been used as a candidate product in building financial portfolios. In this study, we examine the time-varying correlations between carbon allowance prices with other financial indices, during the third phase of EU-ETS. The results show that, at the beginning of this period, carbon price was still strongly corrected with other financial indices. However, this connection was weakened over time. Given the relative independence of carbon assets from other financial assets, we argue for the diversification benefits of including carbon assets in financial portfolios, and building such portfolios, respectively, with the traditional global minimum variance (GMV) strategy, the mean-variance-OGARCH (MV-OGARCH) strategy, and the dynamic conditional correlation (DCC) strategy. It is shown that the portfolio built with the MV-OGARCH strategy far out-performs the others and that including carbon assets in financial portfolios does help reduce investment risks.

Introduction: The global pandemic since 2019 has caused crushing blow to healthy economic developments, particularly the harmonious development of environment and economy. Given the violent contradiction between health and economy, this study focuses on the controversial relationship between inflation and important environmental indicators.Methods: Specifically, we collect monthly data on inflation and carbon returns from three representative pilots in China and make a comprehensive investigation of the relationship between carbon and inflation within the framework of the Environmental Kuznets Curve (EKC).Results: Some significant empirical results are found. First, inflation shows a linear impact on carbon returns, despite the signals of inflation vary in pilots. Second, there exists an inverted U-shape connection in Shenzhen and Guangdong pilots. Third, inflation appears to be a non-negligible factor in pilot fluctuations, despite differences exist among pilots.Discussion: Considering the pressing targets of carbon peaking and carbon neutrality in China, this investigation provides important implications to policy makers in implementing differentiated policies across regions, particularly in the turbulent situations caused by frequent climate policy changes and global pandemic developments.

Based on the prices selected from European Energy Exchange (EEX) from 2013 to 2018, we investigate the inter-correlation of carbon spot and futures markets. Specifically, we adopt the widely used DCC-GARCH model and VAR-BEKK-GARCH model to conduct a comprehensive analysis on the carbon market, i.e., the dynamic correlation and volatility spillover between carbon spot and carbon futures. Moreover, we develop a hedge strategy based on the VAR-BEKK-GARCH model and calculate the hedging effectiveness (HE) value to evaluate the strategy performance. The empirical results show that (i) during our sample period, carbon spot and futures markets are highly correlated, (ii) carbon spot overflows to the futures market and vice versa, and (iii) the HE value is equal to 0.9370, indicating a good performance for the hedging strategy. Then, we provide further discussion on the relationship between carbon spot and futures markets by replacing our dataset with the data of phase II. The results do not change our conclusions on the dynamic correlation and volatility spillover. However, the HE value of phase III is higher than that of phase II, which indicates that the carbon futures market of phase III is not only an available market to hedge risk from the contemporaneous carbon spot market but also has a better hedge effectiveness than phase II.

This study performed comprehensive investigations of the complex interconnections between investor attention and cotton futures price volatility under different term structures. In this paper, in-sample analysis, out-of-sample forecast, influencing mechanisms, as well as nonlinear connections are fully explored using several linear model specifications. The results can be summarized as follows: first, investor attention is the Granger causality of the cotton futures volatility and shows significant linear impacts on cotton volatility; second, models incorporated with investor attention significantly improve the prediction accuracy of cotton volatility in the long term compared with the commonly used AR benchmark model; third, the influence of investor attention on cotton volatility may occur through open interest; and fourth, investor attention presents nonlinear impacts on cotton volatility as well. Overall, the results of this article can provide strong supporting evidence for the important roles of investor attention in asset pricing applications.