The impact of COVID-19 on the portfolio optimization

PurposeThis article unveils first the lead–lag structure between the confirmed cases of COVID-19 and financial markets, including the stock (DJI), cryptocurrency (Bitcoin) and commodities (crude oil, gold, copper and brent oil) compared to the financial stress index. Second, this paper assesses the role of Bitcoin as a hedge or diversifier by determining the efficient frontier with and without including Bitcoin before and during the COVID-19 pandemic.Design/methodology/approachThe authors examine the lead–lag relationship between COVID-19 and financial market returns compared to the financial stress index and between all markets returns using the thermal optimal path model. Moreover, the authors estimate the efficient frontier of the portfolio with and without Bitcoin using the Bayesian approach.FindingsEmploying thermal optimal path model, the authors find that COVID-19 confirmed cases are leading returns prices of DJI, Bitcoin and crude oil, gold, copper and brent oil. Moreover, the authors find a strong lead–lag relationship between all financial market returns. By relying on the Bayesian approach, findings show when Bitcoin was included in the portfolio optimization before or during COVID-19 period;the Bayesian efficient frontier shifts to the left giving the investor a better risk return trade-off. Consequently, Bitcoin serves as a safe haven asset for the two sub-periods: pre-COVID-19 period and COVID-19 period.Practical implicationsBased on the above research conclusions, investors can use the number of COVID-19 confirmed cases to predict financial market dynamics. Similarly, the work is helpful for decision-makers who search for portfolio diversification opportunities, especially during health crisis. In addition, the results support the fact that Bitcoin is a safe haven asset that should be combined with commodities and stocks for better performance in portfolio optimization and hedging before and during COVID-19 periods.Originality/valueThis research thus adds value to the existing literature along four directions. First, the novelty of this study lies in the analysis of several financial markets (stock, cryptocurrencies and commodities)' response to different pandemics and epidemics events, financial crises and natural disasters (Correia et al., 2020;Ma et al., 2020). Second, to the best of the authors' knowledge, this is the first study that examine the lead–lag relationship between COVID-19 and financial markets compared to financial stress index by employing the Thermal Optimal Path method. Third, it is a first endeavor to analyze the lead–lag interplay between the financial markets within a thermal optimal path method that can provide useful insights for the spillover effect studies in all countries and regions around the world. To check the robustness of our findings, the authors have employed financial stress index compared to COVID-19 confirmed cases. Fourth, this study tests whether Bitcoin is a hedge or diversifier given this current pandemic situation using the Bayesian approach.

Applications of mathematical modeling and simulation in COVID-19

In this chapter, we describe the use of mathematical and simulation tools applied in various aspects of the coronavirus disease 2019 pandemic through an extensive and careful review of the recently published works. We detailed the existing implementations of models dealing with (i) the spread of the disease, (ii) the prediction of new outbreaks, (iii) the existence of new variants of the virus, (iv) the effects on the at-risk population, (v) the long-term health consequences, (vi) the resource allocation for supportive staffs and clinical beds, (vii) the dynamics of transmission and how to cut the transmission chain, (viii) the impacts of travel restrictions, social distancing and early detection, (ix) the efficacy of prophylactic agents, (x) the effects of optimum interventions, (xi) the impact of existing vaccines, and (xii) the economic effects of the pandemic. © 2023 Elsevier Inc. All rights reserved.

Economic activities' response to the COVID-19 pandemic in developing countries.

Since the occurrence of the COVID-19 pandemic, many governments around the world have instituted several economic policy responses to swathe the real sectors of their economies from the ramifications of the pandemic. However, most economies still remain vulnerable to the pandemic. In this paper, we evaluate and quantify the potential short-run impact of the COVID-19 pandemic on economic activities in eighteen (18) developing countries using monthly time series data on Industrial Production Index and Composite Index of Economic Activity from January 2010 to December 2020. In addition, we employ a state-space model (a Bayesian structural time series model) to estimate the absolute and relative effects of the COVID-19 pandemic on economic activities in those countries. The results of our Bayesian posterior estimate show that, in relative terms, economic activities of six countries have significantly reduced during the occurrence of the COVID-19 pandemic, usually between -4.4% and -16%. Our Bayesian posterior distribution graphs show that the significant negative impacts of the COVID-19 pandemic on economic activities of most of the countries are rather short-lived. This finding suggests that the real sectors of those countries have seen a recovery after being adversely affected by the COVID-19 pandemic. We recommend a continuation of the policy tools introduced by the central banks and the international organizations with a key focus on sectors of that economy that involves significant human interactions such as the hospitality and tourism as well as the aviation industry which was hugely hit by the pandemic.

The effect of mixed vaccination rollout strategy: A modelling study.

Vaccines have measurable efficacy obtained first from vaccine trials. However, vaccine efficacy (VE) is not a static measure and long-term population studies are needed to evaluate its performance and impact. COVID-19 vaccines have been developed in record time and the currently licensed vaccines are extremely effective against severe disease with higher VE after the full immunization schedule. To assess the impact of the initial phase of the COVID-19 vaccination rollout programmes, we used an extended Susceptible - Hospitalized - Asymptomatic/mild - Recovered (SHAR) model. Vaccination models were proposed to evaluate different vaccine types: vaccine type 1 which protects against severe disease only but fails to block disease transmission, and vaccine type 2 which protects against both severe disease and infection. VE was assumed as reported by the vaccine trials incorporating the difference in efficacy between one and two doses of vaccine administration. We described the performance of the vaccine in reducing hospitalizations during a momentary scenario in the Basque Country, Spain. With a population in a mixed vaccination setting, our results have shown that reductions in hospitalized COVID-19 cases were observed five months after the vaccination rollout started, from May to June 2021. Specifically in June, a good agreement between modelling simulation and empirical data was well pronounced.

Interactive impacts of walkability, social vulnerability, & travel behavior on COVID-19 mortality: A hierarchical Bayesian spatial random parameter approach.

While existing research highlights the built and social environment impacts on COVID-19 mortality, no empirical evidence exists on how the built and social environments may interact to influence COVID-19 mortality. This study presents a rigorous empirical assessment of the interactive impacts of social vulnerability and walkability on neighborhood-level COVID-19 mortality rates. Based in King County, WA, a unique data infrastructure is created by spatially integrating diverse census tract-level data on COVID-19 mortalities, walkability characteristics, social vulnerability, and travel behavior measures. Advanced Markov Chain Monte Carlo (MCMC) based Full Bayes hierarchical spatial random parameter models are developed to simultaneously capture spatial and unobserved random heterogeneity. Around 46% of the neighborhoods had opposite levels of walkability and social vulnerability. Compared to low walkability and high social vulnerability, neighborhoods with high walkability and low social vulnerability (i.e., best case scenario) had on average 20.2% (95% Bayesian CI: -37.2% to -3.3%) lower COVID-19 mortality rates. Analysis of the interactive impacts when only one of the social and built environment metrics was in a healthful direction revealed significant offsetting effects - suggesting that the underlying structural social vulnerability issues faced by our communities should be addressed first for the infectious disease-related health impacts of walkable urban design to be observed. Concerning travel behavior, the findings indicate that COVID-19 mortality rates may be reduced by discouraging auto use and encouraging active transportation. The study methodologically contributes by simultaneously capturing spatial and unobserved heterogeneity in a holistic Full Bayesian framework.

Do Political Connections and Bank Funding Diversity Increase Non-Performing Loans: New Evidence from the Bayesian Approach

Our study aims to fill the gap in estimating the impacts of political connections and bank funding diversity on the risk-taking behaviors of Vietnamese commercial banks. By employing the Bayesian methodology, our paper can overcome the small sample issues to reduce the bias in estimation results. We construct a data sample that includes 38 commercial banks in Vietnam from 2003 to 2020. Our results suggest several findings in the Vietnamese banking sector. Firstly, our findings suggest that politically connected banks have 0.4% non-performing loans higher than unconnected peers. Secondly, we find a positive relationship between funding diversity and non-performing loans of commercial banks in Vietnam. Interestingly, our findings report that the commercial banks, especially the politically connected banks, reduced non-performing loans by 0.2% and 0.4% for a year before the recent two National Congress of the Communist Party of Vietnam, respectively. It could be due to the notion that the bank managers secure their job and political promotions by reducing non-performing loans before the National Congress of the Communist Party of Vietnam. Finally, our study argues that the commercial bank had a lower level of non-performing loans during the Covid19 pandemic because the government offered stimulus supports to the local economy. Our study has substantial implications for bank managers and authorities in emerging markets. © 2022, Economic Laboratory for Transition Research. All rights reserved.

Bayesian Modeling of COVID-19 to Classify the Infection and Death Rates in a Specific Duration: The Case of Algerian Provinces.

COVID-19 causes acute respiratory illness in humans. The direct consequence of the spread of the virus is the need to find appropriate and effective solutions to reduce its spread. Similar to other countries, the pandemic has spread in Algeria, with noticeable variation in mortality and infection rates between regions. We aimed to estimate the proportion of people who died or became infected with SARS-CoV-2 in each provinces using a Bayesian approach. The estimation parameters were determined using a binomial distribution along with an a priori distribution, and the results had a high degree of accuracy. The Bayesian model was applied during the third wave (1 January-15 August 2021), in all Algerian's provinces. For spatial analysis of duration, geographical maps were used. Our findings show that Tissemsilt, Ain Defla, Illizi, El Taref, and Ghardaia (Mean = 0.001) are the least affected provinces in terms of COVID-19 mortality. The results also indicate that Tizi Ouzou (Mean = 0.0694), Boumerdes (Mean = 0.0520), Annaba (Mean = 0.0483), Tipaza (Mean = 0.0524), and Tebessa (Mean = 0.0264) are more susceptible to infection, as they were ranked in terms of the level of corona infections among the 48 provinces of the country. Their susceptibility seems mainly due to the population density in these provinces. Additionally, it was observed that northeast Algeria, where the population is concentrated, has the highest infection rate. Factors affecting mortality due to COVID-19 do not necessarily depend on the spread of the pandemic. The proposed Bayesian model resulted in being useful for monitoring the pandemic to estimate and compare the risks between provinces. This statistical inference can provide a reasonable basis for describing future pandemics in other world geographical areas.

A hierarchical Bayesian approach for modeling the evolution of the 7-day moving average of the number of deaths by COVID-19.

In this paper, we propose a hierarchical Bayesian approach for modeling the evolution of the 7-day moving average for the number of deaths due to COVID-19 in a country, state or city. The proposed approach is based on a Gaussian process regression model. The main advantage of this model is that it assumes that a nonlinear function f used for modeling the observed data is an unknown random parameter in opposite to usual approaches that set up f as being a known mathematical function. This assumption allows the development of a Bayesian approach with a Gaussian process prior over f. In order to estimate the parameters of interest, we develop an MCMC algorithm based on the Metropolis-within-Gibbs sampling algorithm. We also present a procedure for making predictions. The proposed method is illustrated in a case study, in which, we model the 7-day moving average for the number of deaths recorded in the state of São Paulo, Brazil. Results obtained show that the proposed method is very effective in modeling and predicting the values of the 7-day moving average.

The effect of antiviral treatment on SARS-CoV-2 infectious disease progression: A comparison of the multi-state models

Combating SARS-CoV-2 is the first concern and goal of the whole world faced with the global health crisis. Since 2019, the SARS-CoV-2 infection (COVID-19) and even mutated infection cases have been increasing rapidly. From 2019 through 27 August 2021, a total of 214,468,601 individuals were confirmed cases of SARS-CoV-2, including 4,470,969 death toll. Some of these individuals were able to access treatment and some could not, but for a while there was complete uncertainty. It was not known whether those who accessed treatment were lucky, but treatment was based on trial and error because of this uncertainty around the world until data was collected. Therefore, the aim of this study was to model SARS-CoV-2 infectious disease progression from the date of polymerase chain reaction (PCR) test to the date of negative outcome via Bayesian multi-state model approaches considering risk factors such as gender, age, and antiviral treatment. Data from 746 inpatients were collected from August 1st until the December 1st, 2020. For the multi-state model, five various discrete states were selected according to the Republic of Turkey Ministery of Health treatment algorithm. The results showed that Bayesian multi-state models with the Weibull distributed baseline hazard function were more appropriate models in the presence of risk factors and antiviral treatment.

The impact of COVID-19 on the portfolio optimization

Purpose: This article unveils first the lead–lag structure between the confirmed cases of COVID-19 and financial markets, including the stock (DJI), cryptocurrency (Bitcoin) and commodities (crude oil, gold, copper and brent oil) compared to the financial stress index. Second, this paper assesses the role of Bitcoin as a hedge or diversifier by determining the efficient frontier with and without including Bitcoin before and during the COVID-19 pandemic. Design/methodology/approach: The authors examine the lead–lag relationship between COVID-19 and financial market returns compared to the financial stress index and between all markets returns using the thermal optimal path model. Moreover, the authors estimate the efficient frontier of the portfolio with and without Bitcoin using the Bayesian approach. Findings: Employing thermal optimal path model, the authors find that COVID-19 confirmed cases are leading returns prices of DJI, Bitcoin and crude oil, gold, copper and brent oil. Moreover, the authors find a strong lead–lag relationship between all financial market returns. By relying on the Bayesian approach, findings show when Bitcoin was included in the portfolio optimization before or during COVID-19 period;the Bayesian efficient frontier shifts to the left giving the investor a better risk return trade-off. Consequently, Bitcoin serves as a safe haven asset for the two sub-periods: pre-COVID-19 period and COVID-19 period. Practical implications: Based on the above research conclusions, investors can use the number of COVID-19 confirmed cases to predict financial market dynamics. Similarly, the work is helpful for decision-makers who search for portfolio diversification opportunities, especially during health crisis. In addition, the results support the fact that Bitcoin is a safe haven asset that should be combined with commodities and stocks for better performance in portfolio optimization and hedging before and during COVID-19 periods. Originality/value: This research thus adds value to the existing literature along four directions. First, the novelty of this study lies in the analysis of several financial markets (stock, cryptocurrencies and commodities)’ response to different pandemics and epidemics events, financial crises and natural disasters (Correia et al., 2020;Ma et al., 2020). Second, to the best of the authors' knowledge, this is the first study that examine the lead–lag relationship between COVID-19 and financial markets compared to financial stress index by employing the Thermal Optimal Path method. Third, it is a first endeavor to analyze the lead–lag interplay between the financial markets within a thermal optimal path method that can provide useful insights for the spillover effect studies in all countries and regions around the world. To check the robustness of our findings, the authors have employed financial stress index compared to COVID-19 confirmed cases. Fourth, this study tests whether Bitcoin is a hedge or diversifier given this current pandemic situation using the Bayesian approach. © 2022, Emerald Publishing Limited.

Structural basis of SARS-CoV-2 Omicron immune evasion and receptor engagement

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant of concern evades antibody-mediated immunity that comes from vaccination or infection with earlier variants due to accumulation of numerous spike mutations. To understand the Omicron antigenic shift, we determined cryo-electron microscopy and x-ray crystal structures of the spike protein and the receptor-binding domain bound to the broadly neutralizing sarbecovirus monoclonal antibody (mAb) S309 (the parent mAb of sotrovimab) and to the human ACE2 receptor. We provide a blueprint for understanding the marked reduction of binding of other therapeutic mAbs that leads to dampened neutralizing activity. Remodeling of interactions between the Omicron receptor-binding domain and human ACE2 likely explains the enhanced affinity for the host receptor relative to the ancestral virus.

Uncertainty calculation methodologies in microflow measurements: Comparison of GUM, GUM-S1 and Bayesian approach.

The importance of measurement quality cannot be over emphasized in medical applications, as one is dealing with life issues and the wellbeing of society, from oncology to new-borns, and more recently to patients of the COVID-19 pandemic. In all these dire situations, the accuracy of fluid delivered according to a prescribed dose can be critical. Microflow applications are growing in importance for a wide variety of scientific fields, namely drug development and administration, Organ-on-a-Chip, or bioanalysis, but accurate and reliable measurements are a tough challenge in micro-to-femto flow operating ranges, from 2.78 × 10-4 mL/s down to 2.78 × 10-7 mL/s (1000 µL/h down to 1 µL/h). Several sources of error have been established such as the mass measurement, the fluid evaporation dependent on the gravimetric methodology implemented, the tube adsorption and the repeatability, believed to be closely related to the operating mode of the stepper motor and drive screw pitch of a syringe pump. In addition, the difficulty in dealing with microflow applications extends to the evaluation of measurement uncertainty which will qualify the quality of measurement. This is due to the conditions entailed when measuring very small values, close to zero, of a quantity such as the flow rate which is inherently positive. Alternative methods able to handle these features were developed and implemented, and their suitability will be discussed.

Detecting SARS-CoV-2 RNA prone clusters in a municipal wastewater network using fuzzy-Bayesian optimization model to facilitate wastewater-based epidemiology.

The current pandemic disease coronavirus (COVID-19) has not only become a worldwide health emergency, but also devoured the global economy. Despite appreciable research, identification of targeted populations for testing and tracking the spread of COVID-19 at a larger scale is an intimidating challenge. There is a need to quickly identify the infected individual or community to check the spread. The diagnostic testing done at large-scale for individuals has limitations as it cannot provide information at a swift pace in large populations, which is pivotal to contain the spread at the early stage of its breakouts. Recently, scientists are exploring the presence of SARS-CoV-2 RNA in the faeces discharged in municipal wastewater. Wastewater sampling could be a potential tool to expedite the early identification of infected communities by detecting the biomarkers from the virus. However, it needs a targeted approach to choose optimized locations for wastewater sampling. The present study proposes a novel fuzzy based Bayesian model to identify targeted populations and optimized locations with a maximum probability of detecting SARS-CoV-2 RNA in wastewater networks. Consequently, real time monitoring of SARS-CoV-2 RNA in wastewater using autosamplers or biosensors could be deployed efficiently. Fourteen criteria such as population density, patients with comorbidity, quarantine and hospital facilities, etc. are analysed using the data of 14 lac individuals infected by COVID-19 in the USA. The uniqueness of the proposed model is its ability to deal with the uncertainty associated with the data and decision maker's opinions using fuzzy logic, which is fused with Bayesian approach. The evidence-based virus detection in wastewater not only facilitates focused testing, but also provides potential communities for vaccine distribution. Consequently, governments can reduce lockdown periods, thereby relieving human stress and boosting economic growth.

Evaluating the Impact of Intervention Strategies on the First Wave and Predicting the Second Wave of COVID-19 in Thailand: A Mathematical Modeling Study.

Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide. This study aimed to assess and predict the incidence of COVID-19 in Thailand, including the preparation and evaluation of intervention strategies. An SEIR (susceptible, exposed, infected, recovered) model was implemented with model parameters estimated using the Bayesian approach. The model's projections showed that the highest daily reported incidence of COVID-19 would be approximately 140 cases (95% credible interval, CrI: 83-170 cases) by the end of March 2020. After Thailand declared an emergency decree, the numbers of new cases and case fatalities decreased, with no new imported cases. According to the model's predictions, the incidence would be zero at the end of June if non-pharmaceutical interventions (NPIs) were strictly and widely implemented. These stringent NPIs reduced the effective reproductive number (Rt) to 0.73 per day (95% CrI: 0.53-0.93) during April and May. Sensitivity analysis showed that contact rate, hand washing, and face mask wearing effectiveness were the parameters that most influenced the number of reported daily new cases. Our evaluation shows that Thailand's intervention strategies have been highly effective in mitigating disease propagation. Continuing with these strict disease prevention behaviors could minimize the risk of a new COVID-19 outbreak in Thailand.

The impact of COVID-19 on stock market performance in Africa: A Bayesian structural time series approach.

This paper evaluates and quantifies the short-term impact of the coronavirus disease of 2019 (COVID-19) on stock market performance in thirteen (13) African countries, using daily time series stock market data spanning 1st October 2019 to 30th June 2020. We employ a novel Bayesian structural time series approach (a state-space model) to estimate the relative effects of the COVID-19 pandemic on stock market performance in those countries. Generally, our Bayesian posterior estimates show that, in relative terms, stock market performances in Africa have significantly reduced during and after the occurrence of the COVID-19, usually between -2.7 % and -21 %. At the heterogeneous level, we find that 10 countries have their stock markets significantly and adversely affected by the COVID-19, whereas the remaining 3 countries see no significant impact (or a rather short-lived negative significant impact) of the COVID-19 pandemic on their stock markets. We find that, within our sample period, there is almost no chance that the COVID-19 pandemic would have positive effects on the stock market performance in Africa. Our findings contribute to the discussion and research on the economic impact of the COVID-19 pandemic by providing empirical evidence that the pandemic has restrictive effects on stock market performance in African economies.