Forecasting global stock market volatilities in an uncertain world

We investigate the predictive relationship between uncertainty and global stock market volatilities from a high-frequency perspective. We show that uncertainty contains information beyond fundamentals (volatility) and strongly affects stock market volatility. Using several crucial uncertainty measures (i.e., uncertainty and implied volatility indices), we prove that the CBOE volatility index (VIX) performs best in point (density) forecasting;the financial stress index (FSI) in directional forecasting. Furthermore, VIX's predictive power improved dramatically after the COVID-19 outbreak, and the VIX-based portfolio strategy enables mean-variance investors to achieve higher returns. There are two empirical properties of VIX: (i) it helps reduce significantly forecast variance rather than bias;and (ii) its forecasts encompass other uncertainty forecasts well. Overall, we highlight the importance of considering uncertainty when exploring the expected stock market volatility. © 2022 Elsevier Inc.

Forecasting global stock market volatilities in an uncertain world

We investigate the predictive relationship between uncertainty and global stock market volatilities from a highfrequency perspective. We show that uncertainty contains information beyond fundamentals (volatility) and strongly affects stock market volatility. Using several crucial uncertainty measures (i.e., uncertainty and implied volatility indices), we prove that the CBOE volatility index (VIX) performs best in point (density) forecasting;the financial stress index (FSI) in directional forecasting. Furthermore, VIX's predictive power improved dramatically after the COVID-19 outbreak, and the VIX-based portfolio strategy enables mean-variance investors to achieve higher returns. There are two empirical properties of VIX: (i) it helps reduce significantly forecast variance rather than bias;and (ii) its forecasts encompass other uncertainty forecasts well. Overall, we highlight the importance of considering uncertainty when exploring the expected stock market volatility.

Toward a New Model of Public Relations Crisis and Risk Communication Following Pandemics

This chapter examines how the COVID-19 pandemic started much like other crises, especially those of a medical nature. It also examines how that pandemic crisis morphed from a “natural cause” through miscues and missed opportunities to identify and mitigate vulnerabilities that allowed what was a medical crisis to morph into a socio-political, economic, and cultural crisis. The chapter provides a non-linear model of crisis communication management and focuses on the impact of the social media in altering, advancing, and modifying the crisis over time. Several factors constrain the effectiveness of risk communication efforts. The B.A.S.I.C. model can be applied to both non-crisis-related public relations/communication management programs and those related to crisis communication management. Research studies have demonstrated that the essential role of social media in crisis management with a large number of users and real-time use. © 2021 John Wiley and Sons Inc.

Permissioned Blockchain and Deep Reinforcement Learning Enabled Security and Energy Efficient Healthcare Internet of Things

Recently, the Healthcare Internet of Things (H-IoT) has been widely applied to alleviate the global challenge of the coronavirus disease 2019 (COVID-19) pandemic. However, security and limited energy capacity issues remain the two main factors that prevent the large-scale application of the H-IoT. Therefore, a permissioned blockchain and deep reinforcement learning (DRL)-empowered H-IoT system is presented in this research to address these two issues. The proposed H-IoT system can provide real-time security and energy-efficient healthcare services to control the propagation of the COVID-19 pandemic. To address the security issue, a permissioned blockchain method is adopted to guarantee the security of the proposed H-IoT system. As for handling the limited energy constraint, we employ the mobile edge computing (MEC) method to offload the computing tasks to alleviate the computational burden and energy consumption of the proposed H-IoT system. We also adopt an energy harvesting method to improve performance. In addition, a DRL method is employed to jointly optimize both the security and energy efficiency performance of the proposed system. The simulation results demonstrate that the proposed solution can balance the requirements of security and energy efficiency issues and hence can better respond to the COVID-19 pandemic.

A mixed-equilibrium model of individual and household activity–travel choices in multimodal transportation networks

This paper develops a novel household-oriented activity-based mixed-equilibrium model for estimating individual and household activity–travel choices in multimodal transportation networks with interactions between private car and public transit modes. In the novel model, household members with heterogeneous errors of perception on the time-dependent utility of different activity types make daily joint/solo activity–travel choices in a mixed-equilibrium manner, which maximizes either perceived household utility or perceived individual utility. A logit-based stochastic choice model is developed to capture the mixed equilibrium with heterogeneous errors of perception and used to predict the choices of alternative joint activity–travel paths (JATPs) on a supernetwork platform. Based on this stochastic JATP choice model, the mixed-equilibrium model is formulated as an equivalent variational inequality (VI) problem and solved using a modified diagonalization method. This converts the time-dependent activity–travel scheduling problem into an equivalent static traffic assignment problem on JATPs. The conditions required for the existence and uniqueness of a solution to the equivalent VI problem in terms of a JATP flow pattern are also identified. Numerical examples are provided to illustrate the model's merits and its applications for examining the effect of the coronavirus disease 2019 (COVID-19) pandemic. © 2021 Elsevier Ltd