The primary purpose of this paper is to investigate whether a novel Markov regime-switching mixed-data sampling (MRS-MIADS) model we design can improve the prediction accuracy of the realized variance (RV) of Bitcoin. Moreover, to verify whether the importance of jumps for RV forecasting changes over time, we extend the standard MIDAS model to characterize two volatility regimes and introduce a jump-driven time-varying transition probability between the two regimes. Our results suggest that the proposed novel MRS-MIDAS model exhibits statistically significant improvement for forecasting the RV of Bitcoin. In addition, we find that jump occurrences significantly increase the persistence of the high-volatility regime and switch between high-and low-volatility regimes. A wide range of checks confirm the robustness of our results. Finally, the proposed model shows significant improvement for 2-week and 1-month horizon forecasts.
Many researchers from a variety of fields including computer science, network science and mathematics have focused on how to contain the outbreaks of Internet misinformation that threaten social systems and undermine societal health. Most research on this topic treats the connections among individuals as static, but these connections change in time, and thus social networks are also temporal networks. Currently there is no theoretical approach to the problem of containing misinformation outbreaks in temporal networks.We thus propose a misinformation spreading model for temporal networks and describe it using a new theoretical approach. We propose a heuristic-containing (HC) strategy based on optimizing final outbreak size that outperforms simplified strategies such as those that are random-containing (RC) and targetedcontaining (TC). We verify the effectiveness of our HC strategy on both artificial and real-world networks by performing extensive numerical simulations and theoretical analyses. We find that the HC strategy greatly increases the outbreak threshold and decreases the final outbreak threshold.
This paper investigates the impact of the US stock market on the co-movements among the BRIC stock markets using conditional Granger causality which allows a comprehensive exploration on direct and indirect causality. The results from linear conditional causality test show a strong influence of the US stock market on the co-movements of BRIC. Our findings identify the US stock market which is the main inner factor making major contributions to the co-movements among the BRIC stock markets. Further, this study provides robust evidence that the co-movements cannot be significantly influenced by the common information factor. These findings show a more complete picture of the relationships between the US and the BRIC stock markets, offering important implications for policymakers and investors.
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