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.

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.

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Grey-markov forecasting model of traffic volume was founded by applying the model of GM (1,1) and Markov random process theory. The model utilizes the advantages of Grey-markov GM (1,1) forecasting model and Markov random process in order to discover the developing and varying tendency of the forecasting data sequences of traffic volume. The analysis of an example indicates that the grey-markov model has good forecasting accuracy and excellent applicability in predicting traffic volume.

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The uniqueness of this investigation lies in empirically testing and proving the contagion spillover of Bitcoin attention to carbon futures. Specifically, several models are adopted to investigate the explanatory and predictive abilities of Bitcoin attention to carbon futures. The results can be generalized as follows. First, Bitcoin attention Granger causes the variation of carbon futures. Second, Bitcoin attention shows a negative impact on carbon futures and an addition, an invert U-shaped connection exists. Third, the Bitcoin attention-based models can beat the commonly used historical average benchmark during out-of-sample forecasting both in statistical and economic levels. Fourth, we complete robustness checks to certify that the contagion spillover from Bitcoin attention to the pricing of carbon futures does exist. Finally, we prove the linear and non-linear impacts from Bitcoin attention to realized volatility of carbon futures. All the results prove that Bitcoin attention is an important pricing factor for carbon futures market.

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