Annals of Financial Economics

This paper presents a novel analysis of the global spread of the SARS-CoV-2 virus that causes the COVID-19 disease using the R package “nCov2019”, with an emphasis on the global spread and forecasts of the disease, and the rate of transmission in individual countries at two different points in time, namely, March and September 2020. This throws in sharp relief the relative effectiveness of the attempts to risk manage the spread of the virus by “flattening the curve” (aka planking the curve) of the speed of transmission, and the efficacy of lockdowns in terms of the spread of the disease and death rates. Simple cross-sectional regressions are able to predict quite well both the total number of cases and deaths, which cast doubt on the above measures. The algorithmic techniques, results and analysis presented in the paper should prove useful to the medical and health professions, science advisers and risk management and decision making of healthcare by state, regional and national governments in all countries.

This paper considers a class of Levy process namely the variance gamma (VG) process to offer a more realistic way to model the dynamics of the logarithm of stock prices. Then, we verify the uniqueness and existence of the solution to the stochastic differential equation of the model. We also examine the valuation of multi-asset American options under VG model when the correlation coefficient is governed by the modified Ornstein–Uhlenbeck process. Various simulation experiments are presented and the achieved results are tested empirically for option prices using S&P 500 data.

In this paper, it is proposed to combine the forecasts using a simple Bayesian forecast combination algorithm. The algorithm is applied to forecasts from three non-nested diffusion models for S shaped processes like virus diffusion. An illustration to daily data on first-wave cumulative Covid-19 cases in the Netherlands shows the ease of use of the algorithm and the accuracy of the newly combined forecasts.

The paper presents a critical analysis of the European spread of the SARS-CoV-2 virus that causes the COVID-19 disease across 48 European countries and territories, including the Monaco and Andorra principalities and Vatican City. Simple cross-sectional regressions, using country populations, are able to predict quite accurately both the total number of cases and deaths, which cast doubt on measures aimed at controlling the disease via lockdowns. This throws into sharp contrast the relative effectiveness of the attempts to risk manage the spread of the virus by ‘flattening the curve’ of the speed of transmission, and the efficacy of lockdowns in terms of the spread of the disease and death rates. The algorithmic techniques, results and analysis presented in the paper should prove useful to the medical and health professions, science advisers and risk management and decision making of healthcare by state, regional and national governments in all countries in Europe.

Uncertainty is known to have negative impact on financial markets through its effects on investors’ decisions. In the wake of the “Great Recession”, quite a few recent studies have highlighted the role of uncertainty in predicting in-sample movements of interest rates. Since in-sample predictability does not guarantee out-of-sample forecasting gains, in this paper, we used historical daily and monthly data for the US to forecast mean and volatility of interest rate. The results show that changes in uncertainty measure movements fail to add any significant statistical gains to the forecast of interest rates for the US. In other words, policy makers in the US are not likely to improve their accuracy of future movements of the policy rate’s mean and volatility by incorporating information derived from changes in metrics of uncertainty.

Our paper investigates the influence of state ownership on the linkage between revenue diversification and risk of Vietnam domestic commercial banks in the period 2009–2018. By using the Generalized Method of Moments (GMM) estimation for a dynamic panel model, the empirical results indicate that Vietnamese domestic commercial banks with higher state equity are promoted to take more risks in the revenue diversification process. Our findings are robustly checked by a variety of measures of banking risk, income diversification, and state equity. Empirical results from our dynamic model are not only accordant with the previous findings of Batten and Vo [(2016). Bank risk shifting and diversification in an emerging market. Risk Management, 18(4), 217–235] estimated by Ordinary Least Square (OLS) regression on the positive relationship between banking risk and income diversification in Vietnamese domestic commercial banks but also provide new evidence on the tradeoff relationship between risk-return in the operating strategy of Vietnamese state-owned banks in the post-financial crisis. This paper proposes a framework for evaluating the nexus between revenue diversification and risk from the state ownership aspect in other frontier markets.