The rapid spread of new coronaviruses throughout China and the world in 2019–2020 has had a great impact on China’s economic and social development. As the backbone of Chinese society, Chinese universities have made significant contributions to emergency risk management. Such contributions have been made primarily in the following areas: alumni resource collection, medical rescue and emergency management, mental health maintenance, control of staff mobility, and innovation in online education models. Through the support of these methods, Chinese universities have played a positive role in the prevention and control of the epidemic situation. However, they also face the problems of alumni’s economic development difficulties, the risk of deadly infection to medical rescue teams and health workers, infection of teachers and students, and the unsatisfactory application of information technology in resolving the crisis. In response to these risks and emergency problems, we propose some corresponding solutions for public dissemination, including issues related to medical security, emergency research, professional assistance, positive communication, and hierarchical information-based teaching.

The SARS-CoV-2 virus that causes the COVID-19 disease is highly infectious and contagious. The long-term consequences for individuals are as yet unknown, while the long-term effects on the international community will be dramatic. COVID-19 has changed the world forever in every imaginable respect and has impacted heavily on the international travel, tourism demand, and hospitality industry, which is one of the world’s largest employers and is highly sensitive to significant shocks like the COVID-19 pandemic. It is essential to investigate how the industry will recover after COVID-19 and how the industry can be made sustainable in a dramatically changed world. This paper presents a charter for tourism, travel, and hospitality after COVID-19 as a contribution to the industry.

Various univariate and multivariate models of volatility have been used to evaluate market risk, asymmetric shocks, thresholds, leverage effects, and Value-at-Risk in economics and finance. This article is concerned with market risk, and develops a constant conditional correlation vector ARMA–asymmetric GARCH (VARMA–AGARCH) model, as an extension of the widely used univariate asymmetric (or threshold) GJR model of Glosten et al. ( 1992 Glosten , L. , Jagannathan , R. , Runkle , D. ( 1992 ). On the relation between the expected value and volatility of nominal excess return on stocks . Journal of Finance 46 : 1779 – 1801 . [Google Scholar] ), and establishes its underlying structure, including the unique, strictly stationary, and ergodic solution of the model, its causal expansion, and convenient sufficient conditions for the existence of moments. Alternative empirically verifiable sufficient conditions for the consistency and asymptotic normality of the quasi-maximum likelihood estimator are established under non-normality of the standardized shocks.

The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators; DCC has no asymptotic properties; DCC is not a special case of Generalized Autoregressive Conditional Correlation (GARCC), which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news is typically extremely small; DCC cannot be distinguished empirically from diagonal Baba, Engle, Kraft and Kroner (BEKK) in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.

A novel coronavirus was reported to the World Health Organization (WHO) in China on 31 December 2019. The WHO named the disease COVID-19 on 11 February 2020. As of 26 February 2020, the disease has been detected on all continents, except for Antarctica. Daily updates on COVID-19 since early February 2020 have made headline news worldwide for much of 2020. This editorial evaluates risk management based on the Global Health Security (GHS) Index of global health security capabilities in 195 countries. The GHS Index lists the countries best prepared for an epidemic or pandemic. COVID-19 is compared with two related coronavirus epidemics, SARS and MERS, in terms of the number of reported human infections, deaths, countries, major country clusters, timelines, and the likelihood of discovering a safe, effective, and approved vaccine.

The SARS-CoV-2 coronavirus that causes the COVID-19 disease led to the most significant change in the world order over the past century, destabilizing the global economy and financial stock markets, the world’s economy, social development, business, risk, financial management and financial markets, among others. COVID-19 has generated great uncertainty, and dramatically affected tourism, travel, hospitality, supply chains, consumption, production, operations, valuations, security, financial stress and the prices of all products, including fossil fuel and renewable energy sources. This Editorial introduces a Special Issue of the Journal of Risk and Financial Management (JRFM) on the “Risk and Financial Management of COVID-19 in Business, Economics and Finance”. This Special Issue will attract practical, state-of-the-art applications of mathematics, probability and statistical techniques on the topic, including empirical applications. This paper investigates important issues that have been discussed in tourism, global health security and risk management in business as well as the social and medical sciences.