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At the beginning of the COVID-19 pandemic, the entire world was waiting for a medical solution (for example, vaccines) in order to return to normality. Sanitary restrictions changed our consumption behaviors and feelings. Therefore, this paper analyzes the stochastic properties of consumer sentiment during the COVID-19 episode and the appearance of vaccines against the virus in December 2020 in the United States of America. This study adds a new dimension to the literature because it is the first research paper that uses advanced methodologies based on fractional integration and fractional cointegration analysis to understand the statistical properties of these time series and their behavior in the long term. The results using fractional integration methodologies exhibit a high degree of persistence, finding behavior of mean reversion during the pandemic episode. Therefore, the shock duration in consumer sentiment will be transitory, recovering to its previous trend in the short run. Focusing on the cointegrating part, we arrive at two main conclusions. First, an increase in total vaccination produces a positive reaction or impact on the behavior of consumers. On the other hand, an increase in new COVID-19 cases negatively affects the behavior of the consumer. [ABSTRACT FROM AUTHOR]

Pooled sample testing is an effective strategy to reduce the cost of disease surveillance in human and animal medicine. Testing pooled samples commonly produces matched observations with dichotomous responses in medical and epidemiological research. Although standard approaches exist for one-to-one paired binary data analyses, there is not much work on one-to-two or one-to-N matched binary data in the current statistical literature. The existing Miettinen's test assumes that the multiple observations from the same matched set are mutually independent. In this paper, we propose exact and asymptotic tests for one-to-two matched binary data. Our methods are markedly different from the previous studies in that we do not rely on the mutual independence assumption. The emphasis on the interdependence of observations within a matched set is inherent and attractive in both human health and veterinary medicine. It can be applied to all kinds of diagnostic studies with a one-to-two matched data structure. Our methods can be generalized to the one-to-N matched case. We discuss applications of the proposed methods to the environmental testing of salmonella in the United States. [ABSTRACT FROM AUTHOR]

This paper analyzes the stochastic properties of consumer sentiment to understand how they affected the luxury sector in the United States before and after COVID-19. The results were derived using fractional integration methodologies and suggest that, before the pandemic episode, both variables were expected to be mean reverting and the shocks were transitory, having similar behavior. However, after the appearance of COVID-19, results suggest that consumer sentiment recovered before the luxury sector. Results from the use of cointegration methodologies show that the effects of COVID-19 disappeared in the short-run. Finally, the sentiment of consumers acts as a leading indicator of the behavior of the luxury sector according to wavelet analysis. Thus, an increase in consumer sentiment implies an increase of 3.6% in the luxury sector. [ABSTRACT FROM AUTHOR]

The number of COVID-19 fatalities fluctuated widely across United States (US) counties. The number of deaths is stochastic. When the average number of deaths is equal to the dispersion, the distribution is the usual Poisson. When the average number of deaths is higher than the dispersion, the distribution is an intervened Poisson. When the average number of deaths is lower than the dispersion, the distribution is an incidence-rate-restricted Poisson (IRRP) type. Because dispersion of COVID-19 fatalities in some counties is higher than the average number of fatalities, the underlying model for the chance-oriented mechanism might be IRRP. Understanding where this overdispersion or volatility exists and the implications of it is the topic of this research. In essence, this paper focuses on the number of COVID-19 fatalities that fluctuated widely across United States (US) counties and develops an incidence-rate-restricted Poisson (IRRP) to understand where this overdispersion or volatility exists and the implications of it. [ABSTRACT FROM AUTHOR]

One important parameter in the Black–Scholes option pricing model is the implied volatility. Implied volatility surface (IVS) is an important concept in finance that describes the variation of implied volatility across option strike price and time to maturity. Over the last few decades, economists and financialists have long tried to exploit the predictability in the IVS using various parametric models, which require deep understanding of financial practices in the area. In this paper, we explore how a data-driven machine learning method, symbolic regression, performs in identifying the implied volatility surface even without deep financial knowledge. Two different approaches of symbolic regression are explored through a simulation study and an empirical study using a large panel of option data in the United States options market. [ABSTRACT FROM AUTHOR]

This paper extends the conditional autoregressive range (CARR) model to the multivariate CARR (MCARR) model and further to the two-stage MCARR-return model to model and forecast volatilities, correlations and returns of multiple financial assets. The first stage model fits the scaled realised Parkinson volatility measures using individual series and their pairwise sums of indices to the MCARR model to obtain the fitted volatilities. Then covariances are calculated to construct the fitted variance–covariance matrix of returns which are imputed into the stage-two return model to capture the heteroskedasticity of assets' returns. We investigate different choices of mean functions to describe the volatility dynamics. Empirical applications are based on the Standard and Poor 500, Dow Jones Industrial Average and Dow Jones United States Financial Service Indices. Results show that the stage-one MCARR models using asymmetric mean functions give better in-sample model fits than those based on symmetric mean functions. They also provide better out-of-sample volatility forecasts than those using CARR models based on two robust loss functions. We also find that the stage-two return models with constant means and multivariate Student-t errors give better in-sample fits than the Baba–Engle–Kraft–Kroner generalised autoregressive conditional heteroskedasticity models. The estimates and forecasts of value-at-risk (VaR) and conditional VaR based on the best MCARR-return models for each asset are provided and tested using backtests to confirm the accuracy of the VaR forecasts. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study the role of broadband in mitigating the economic losses resulting from COVID-19 in the United States by providing a necessary infrastructure to keep economic systems operating, albeit partially. The study is based on an empirical framework underlined by a Cobb–Douglas production function and estimated within a structural multi-equation model through the three-stage least squares approach. To consider the impact of COVID-19 on the economy, we rely on two main variables: an indicator of the quantity of deaths attributed to the disease for every 100,000 inhabitants; and the Stringency Index, a metric linked to the intensity of social restrictions imposed by national and local governments. The main contribution of this article is to provide robust evidence for how the heterogeneous effects of the pandemic across states are in part explained by differences in broadband adoption. Our results indicate that those states with higher broadband adoption were able to mitigate a larger portion of their economic losses derived from the pandemic-induced lockdowns. Addressing the digital divide and ensuring universal access to broadband represent critical goals for building economic resilience to face future emergencies. [ABSTRACT FROM AUTHOR]

The power control of horizontal axis wind turbines can affect significantly the vibration loads and fatigue life of the tower and the blades. In this paper, we both consider the power control and vibration load mitigation of the tower fore-aft vibration. For this purpose, at first, we developed a fully coupled model of the NREL 5MW turbine. This model considers the full aeroelastic behaviour of the blades and tower and is validated by experiment results, comparing the time history data with the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code which is developed by NREL (National Renewable Energy Lab in the United States). In the next, novel sensorless control algorithms are developed based on the supper twisting sliding mode control theory and sliding mode observer for disturbance rejection. In region II (the wind speed is between the cut-in and rated wind velocity), the novel sensorless control algorithm increased the power coefficient in comparison to the conventional indirect speed control (ISC) method (the conventional method in the industry). In region III (the wind speed is between the rated and cut-out speed), an adaptive neural fuzzy inference system (ANFIS) is developed to estimate pitch sensitivity. The rotor speed, pitch angle, and effective wind velocity are inputs, and pitch sensitivity is the output. The designed novel pitch control performance is compared with the gain scheduled PI (GPI) method (the conventional approach in this region). The simulation results demonstrate that the flapwise blade displacement is reduced significantly. Finally, to reduce the fore-aft vibration of the tower, a tuned mass damper (TMD) was designed by using the genetic algorithm and the fully coupled model. In comparison to the literature body, we demonstrate that the fully coupled model provides much better accuracy in comparison to the uncoupled model to estimate the vibration loads. [ABSTRACT FROM AUTHOR]

For rare disease drug development, the United States (US) Food and Drug Administration (FDA) has indicated that the same standards as those for drug products for common conditions will be applied. To assist the sponsors in rare disease drug development, the FDA has initiated several incentive programs to encourage the sponsors in rare disease drug development. In practice, these incentive programs may not help in achieving the study objectives due to the limited small patient population. To overcome this problem, some out-of-the-box innovative thinking and/or approaches, without jeopardizing the integrity, quality, and scientific validity of rare disease drug development, are necessarily considered. These innovative thinking and/or approaches include but are not limited to (i) sample size justification based on probability statements rather than conventional power analysis; (ii) demonstrating not-ineffectiveness and not-unsafeness rather than demonstrating effectiveness and safety with the small patient population (i.e., limited sample size) available; (iii) the use of complex innovative designs such as a two-stage seamless adaptive trial design and/or an n-of-1 trial design for flexibility and the efficient assessment of the test treatment under study; (iv) using real-world data (RWD) and real-world evidence (RWE) to support regulatory submission; and (v) conducting an individual benefit–risk assessment for a complete picture of the clinical performance of the test treatment under investigation. In this article, we provide a comprehensive summarization of this innovative thinking and these approaches for an efficient, accurate and reliable assessment of a test treatment used for treating patients with rare diseases under study. Statistical considerations including challenges and justifications are provided whenever possible. In addition, an innovative approach that combines innovative thinking and these approaches is proposed for regulatory consideration in rare disease drug development. [ABSTRACT FROM AUTHOR]

COVID-19, known as Coronavirus disease 2019, is caused by a coronavirus called SARS-CoV-2. As coronavirus restrictions ease and cause changes to social and business activities around the world, and in the United States in particular, including social distancing, reopening states, reopening schools, and the face mask mandates, COVID-19 outbreaks are on the rise in many states across the United States and several other countries around the world. The United States recorded more than 1.9 million new infections in July, which is nearly 36 percent of the more than 5.4 million cases reported nationwide since the pandemic began, including more than 170,000 deaths from the disease, according to data from Johns Hopkins University as of 16 August 2020. In April 2020, the author of this paper presented a model to estimate the number of deaths related to COVID-19, which assumed that there would be no significant change in the COVID-19 restrictions and guidelines in the coming days. This paper, which presents the evolved version of the previous model published in April, discusses a new explicit mathematical model that considers the time-dependent effects of various pandemic restrictions and changes related to COVID-19, such as reopening states, social distancing, reopening schools, and face mask mandates in communities, along with a set of selected indicators, including the COVID-19 recovered cases and daily new cases. We analyzed and compared the modeling results to two recent models based on several model selection criteria. The model could predict the death toll related to the COVID-19 virus in the United States and worldwide based on the data available from Worldometer. The results show the proposed model fit the data significantly better for the United States and worldwide COVID-19 data that were available on 16 August 2020. The results show very encouraging predictability that reflected the time-dependent effects of various pandemic restrictions for the proposed model. The proposed model predicted that the total number of U.S. deaths could reach 208,375 by 1 October 2020, with a possible range of approximately 199,265 to 217,480 deaths based on data available on 16 August 2020. The model also projected that the death toll could reach 233,840 by 1 November 2020, with a possible range of 220,170 to 247,500 American deaths. The modeling result could serve as a baseline to help decision-makers to create a scientific framework to quantify their guidelines related to COVID-19 affairs. The model predicted that the death toll worldwide related to COVID-19 virus could reach 977,625 by 1 October 2020, with a possible range of approximately 910,820 to 1,044,430 deaths worldwide based on data available on 16 August 2020. It also predicted that the global death toll would reach nearly 1,131,000 by 1 November 2020, with a possible range of 1,030,765 to 1,231,175 deaths. The proposed model also predicted that the global death toll could reach 1.47 million deaths worldwide as a result of the SARS CoV-2 coronavirus that causes COVID-19. We plan to apply or refine the proposed model in the near future to further study the COVID-19 death tolls for India and Brazil, where the two countries currently have the second and third highest total COVID-19 cases after the United States. [ABSTRACT FROM AUTHOR]

Renewable jet fuel (RJF) production has been recognized as a promising approach for reducing the aviation sector's carbon footprint. Over the last decade, the commercial production of RJF has piqued the interest of airlines and governments around the world. However, RJF production can be challenging due to its dispersed supply resources. Furthermore, the production of RJF is more costly compared to producing conventional jet fuel. In this study, using a mixed integer linear programming (MILP), we design a corn-stover-based RJF supply chain network in which we obtain an optimized configuration of the supply chain and determine operational decisions required to meet RJF demand at airports. To accelerate the commercialization of RJF production, we examined four incentive programs designed to cover the supply chain's costs, with agricultural statistics districts serving as the designated supply regions. This study is validated by employing the model to design the supply chain in the Midwestern United States. The results from this study are promising as they show the supply chain can achieve commercialization with partial financial coverage from the incentive programs. Based on the findings of this study, policymakers can devise policies to commercialize RJF production and accelerate its adoption by the industry. [ABSTRACT FROM AUTHOR]

Studying the question of whether macroeconomic predictors play a role in forecasting stock-market volatility has a long and significant tradition in the empirical finance literature. We went beyond the earlier literature in that we studied whether the presidential approval rating can be used as a single-variable substitute in place of standard macroeconomic predictors when forecasting stock-market volatility in the United States (US). Political-economy considerations imply that the presidential approval rating should reflect fluctuations in macroeconomic predictors and, hence, may absorb or even improve on the predictive value for stock-market volatility of the latter. We studied whether the presidential approval rating has predictive value out-of-sample for realized stock-market volatility and, if so, which types of investors benefit from using it. [ABSTRACT FROM AUTHOR]

Homelessness has been a persistent social concern in the United States. A combination of political and economic events since the 1960s has driven increases in poverty that, by 1991, had surpassed 1928 depression era levels in some accounts. This paper explores how the emerging field of behavioral economics can use machine learning and data science methods to explore preventative responses to homelessness. In this study, machine learning data mining strategies, specifically K-means cluster analysis and later, decision trees, were used to understand how environmental factors and resultant behaviors can contribute to the experience of homelessness. Prevention of the first homeless event is especially important as studies show that if a person has experienced homelessness once, they are 2.6 times more likely to have another homeless episode. Study findings demonstrate that when someone is at risk for not being able to pay utility bills at the same time as they experience challenges with two or more of the other social determinants of health, the individual is statistically significantly more likely to have their first homeless event. Additionally, for men over 50 who are not in the workforce, have a health hardship, and experience two or more other social determinants of health hardships at the same time, the individual has a high statistically significant probability of experiencing homelessness for the first time. [ABSTRACT FROM AUTHOR]

In this paper, we discuss an explicit model function that can estimate the total number of deaths in the population, and particularly, estimate the cumulative number of deaths in the United States due to the current Covid-19 virus. We compare the modeling results to two related existing models based on a new criteria and several existing criteria for model selection. The results show the proposed model fits significantly better than the other two related models based on the U.S. Covid-19 death data. We observe that the errors of the fitted data and the predicted data points on the total number of deaths in the U.S. on the last available data point and the next coming day are less than 0.5% and 2.0%, respectively. The results show very encouraging predictability for the model. The new model predicts that the maximum total number of deaths will be approximately 62,100 across the United States due to the Covid-19 virus, and with a 95% confidence that the expected total death toll will be between 60,951 and 63,249 deaths based on the data until 22 April, 2020. If there is a significant change in the coming days due to various testing strategies, social-distancing policies, the reopening of community strategies, or a stay-home policy, the predicted death tolls will definitely change. Future work can be explored further to apply the proposed model to global Covid-19 death data and to other applications, including human population mortality, the spread of disease, and different topics such as movie reviews in recommender systems. [ABSTRACT FROM AUTHOR]

This study evaluates the explosive behavior and Granger causality episodes in manufacturing job postings in the United States (JOBPUS) and COVID-19-induced uncertainty (COVIDEMV). This study applied the novel unit root tests with explosive behavior, and the novel time-varying Granger causality test for a sample period ranging from 1 January 2020 to 29 July 2022. Further, this study used date stamping to identify the subperiods of the explosive behavior and causality. The findings revealed that JOBPUS exhibits explosive behavior, with several episodes of exuberance (bubbles) across the sample period while COVIDEMV does not exhibit explosivity during the period. However, the results of the causality provide evidence of bidirectional causality, with several episodes between the variables. Moreover, the episodes of the explosivity and causality coincide with significant episodes in the history of the COVID-19 pandemic worldwide and in the United States particularly, such as the date when United States recorded a COVID-19-related death toll of over 100,000 people for the first time, after the presidential election, after Halloween celebrations, after the discovery and administration of COVID-19 vaccines as well as the discovery of the Delta and the Omicron variants of COVID-19. Therefore, the time-series characteristics of JOBPUS and its causal nexus with COVIDEMV largely depend on the intensity of the instability caused by the pandemics. Hence, explosivity and time-varying causal behavior should necessarily be accounted for when modelling the job market conditions in the United States, particularly during pandemic-related crises. [ABSTRACT FROM AUTHOR]

The issuance of multiple-event catastrophe bonds (MECBs) has the potential to increase in the next few years. This is due to the increasing trend in the frequency of global catastrophes, which makes single-event catastrophe bonds (SECBs) less relevant. However, there are obstacles to issuing MECBs since the pricing framework is still little studied. Therefore, this study aims to develop such a new pricing framework. The model uniquely involves three new variables: the trigger event correlation, interest, and inflation rates. The trigger event correlation rate was accommodated by the involvement of the copula while the interest and inflation rates were simultaneously considered using an integrated autoregressive vector stochastic model. After the model was obtained, the model was simulated on storm catastrophe data in the United States. Finally, the effect of the three variables on MECB prices was also analyzed. The analysis results show that the three variables make MECB prices more fairly than other models. This research is expected to guide special purpose vehicles to set fairer MECB prices and can also be used as a reference for investors in choosing MECBs based on the rates of trigger event correlation and the real interest they can expect. [ABSTRACT FROM AUTHOR]

When facing a multi-period defined contribution (DC) pension plan investment problem during the accumulation phase, the risk aversion attitude of a mean-variance investor may depend on state variables. In this paper, we propose a state-dependent risk aversion model which is a linear function of the current wealth level after contribution. This risk aversion model is reasonable from both the dimensional analysis and the economic point of view. Moreover, we incorporate the wage income factor into our model. In the field of dynamic investment analysis, most studies have irrational situations in their models because of the lack of the positiveness for the wealth process. In view of it, we further improve the work of Wang and Chen by completely eliminating the irrationality of the model. Due to the time-inconsistency of the resulting stochastic control problem, we derive the explicit expressions of the equilibrium control and the corresponding equilibrium value function by adopting the game theoretic framework developed in Björk and Murgoci. Further, two special cases are discussed. Finally, using a more realistic risk aversion coefficient, we provide a series of empirical tests based on the real data from the American market and compare our results with the relevant results in the literature. [ABSTRACT FROM AUTHOR]

The middle school concept, aimed at creating a more developmentally responsive learning environment for young adolescents, gained a stronghold in the later part of the 20th century. Proponents of this concept have argued continually for the holistic implementation of its six key characteristics if its benefits are to be realized. These characteristics include: (a) a challenging, integrative, and exploratory curriculum; (b) varied teaching and learning approaches; (c) assessment and evaluation that promote learning; (d) flexible organizational structures (i.e., including the physical space, scheduling, and grouping of students and teachers); (e) programs and policies that foster health, wellness and safety; and (f) comprehensive guidance and support services. Recently, Ellerbrock, Falbe, and Pomykal Franz identified key middle school organizational structures of people, place, and time as being interconnected and integral to effective middle school practices. Main also demonstrated the interconnected nature of these key characteristics and how organizational structures of people affected the successful implementation of other characteristics. Thus, how these organizational structures can and are being implemented has implications for our understanding of the effectiveness of other middle school practices. In this paper, researchers from both the United States and Australia examine and compare literature published between 2000 and 2018 addressing ways in which middle school/middle years organizational structures have been reported and categorized by structures of people, place, and time in these two countries. Pertinent literature related to organizational structures of middle schools in the United States and to middle years education in Australia was examined. Findings from studies and evaluations from each country are reported to provide an international perspective on the organizational structures of middle schools/middle years education across the two countries. Overall, since 2000, the body of knowledge about middle schools/middle years organizational structures has been surprisingly limited in comparison to their perceived importance in the field. This lack of research is concerning in the midst of educational reform in both countries, resulting in questions about the impact of school organizational structures on young adolescent development and learning. [ABSTRACT FROM AUTHOR]

It is challenging to obtain accurate and efficient predictions in short-term load forecasting (STLF) systems due to the complexity and nonlinearity of the electric load signals. To address these problems, we propose a hybrid predictive model that includes a sliding-window algorithm, a stacking ensemble neural network, and a similar-days predictive method. First, we leverage a sliding-window algorithm to process the time-series electric load data with high nonlinearity and non-stationarity. Second, we propose an ensemble learning scheme of stacking neural networks to improve forecasting performance. Specifically, the stacking neural networks contain two types of networks: the base-layer and the meta-layer networks. During the pre-training process, the base-layer network integrates a radial basis function (RBF), random vector functional link (RVFL), and backpropagation neural network (BPNN) to provide a robust predictive model. The meta-layer network utilizes a deep belief network (DBN) and the improved broad learning system (BLS) to enhance predictive accuracy. Finally, the similar-days prediction method is developed to extract the relationship of electric load data in different time dimensions, further enhancing the robustness and accuracy of the model. To demonstrate the effectiveness of our model, it is evaluated using real data from five regions of the United States in three consecutive years. We compare our method with several state-of-the-art and conventional neural-network-based models. Our proposed algorithm improves the prediction accuracy by 16.08%, 16.83%, and 22.64% compared to DWT-EMD-RVFL, SWT-LSTM, and EMD-BLS, respectively. Empirical results demonstrate that our model achieves better accuracy and robustness compared with the baselines. [ABSTRACT FROM AUTHOR]

The environmental degradation and the concern for sustainable development have garnered extensive attention from researchers to evaluate the prospects of green bonds over other traditional assets. Against this backdrop, the current study measures the asymmetric relationship between green bonds, U.S. economic policy uncertainty (EPU), and bitcoins by employing the Nonlinear Autoregressive Distribution Lag (NARDL) estimation technique recently developed by Shin et al. The outcome of the empirical analysis confirms an asymmetric cointegration between EPU, bitcoins, the clean energy index, oil prices, and green bonds. The NARDL estimation substantiates that positive shock in EPU exerts a negative impact on green bonds, whereas a negative shock in EPU increases the performance of green bonds. It implies, in the long run, a 1 percent increase (decrease) in EPU decreases (increases) the performance of green bonds by 0.22 percent and 0.11 percent, respectively. Likewise, the study also confirms a bidirectional relationship between bitcoins and green bonds. A positive shock in bitcoin increases the performance of green bonds and vice versa. In addition, our study also reveals a direct co-movement between clean energy, oil prices, and green bonds. This outcome implies that green bonds are not a different asset class, and they mirror the performance of other asset classes, such as clean energy, oil prices, and bitcoins. The findings offer several implications to understand the hedging and diversification properties of bitcoins, and assist in understanding the role of U.S. economic policy uncertainty on green bonds. [ABSTRACT FROM AUTHOR]

The abundance of type and quantity of available data in the healthcare field has led many to utilize machine learning approaches to keep up with this influx of data. Data pertaining to COVID-19 is an area of recent interest. The widespread influence of the virus across the United States creates an obvious need to identify groups of individuals that are at an increased risk of mortality from the virus. We propose a so-called clustered random forest approach to predict COVID-19 patient mortality. We use this approach to examine the hidden heterogeneity of patient frailty by examining demographic information for COVID-19 patients. We find that our clustered random forest approach attains predictive performance comparable to other published methods. We also find that follow-up analysis with neural network modeling and k-means clustering provide insight into the type and magnitude of mortality risks associated with COVID-19. [ABSTRACT FROM AUTHOR]

Since the introduction of the measure of economic policy uncertainty, businesses, policymakers, and academic scholars closely monitor its momentum due to expected economic implications. The US is the world's top-ranked equity market by size, and prior literature on policy uncertainty and stock prices for the US is conflicting. In this study, we reexamine the policy uncertainty and stock price nexus from the US perspective, using a novel dynamically simulated autoregressive distributed lag setting introduced in 2018, which appears superior to traditional models. The empirical findings document a negative response of stock prices to 10% positive/negative shock in policy uncertainty in the short-run, while in the long-run, an increase in policy uncertainty by 10% reduces the stock prices, which increases in response to a decrease with the same magnitude. Moreover, we empirically identified two significant thresholds: (1) policy score of 4.89 (original score 132.39), which negatively explain stock prices with high magnitude, and (2) policy score 4.48 (original score 87.98), which explains stock prices negatively with a relatively low magnitude, and interestingly, policy changes below the second threshold become irrelevant to explain stock prices in the United States. It is worth noting that all indices are not equally exposed to unfavorable policy changes. The overall findings are robust to the alternative measures of policy uncertainty and stock prices and offer useful policy input. The limitations of the study and future line of research are also highlighted. All in all, the policy uncertainty is an indicator that shall remain ever-important due to its nature and implication on the various sectors of the economy (the equity market in particular). [ABSTRACT FROM AUTHOR]

This study investigates the structure of the tail dependence between the United States (US) and Gulf Cooperation Council (GCC) banking sectors for the period February 2010 to July 2017. Conditional value at risk and conditional diversification benefits are calculated. The GCC banking sectors show lower tail dependence with the US banking sector. This is confirmed by the fact that GCC banking sectors receive higher downside risk spillover from the US banking system during downside market movements compared to upside risk spillover effects. Interestingly, an equally weighted portfolio of US and GCC banking stocks can provide relatively higher diversification benefits. These findings have implications for portfolio diversification, asset allocation and hedging strategies. [ABSTRACT FROM AUTHOR]

Modelers typically use detailed simulation models and vary the fraction vaccinated to study outbreak control. However, there is currently no guidance for modelers on how much detail (i.e., heterogeneity) is necessary and how large a population to simulate. We provide theoretical and numerical guidance for those decisions and also analyze the benefit of a faster public health response through a stochastic simulation model in the case of measles in the United States. Theoretically, we prove that the outbreak size converges as the simulation population increases and that the outbreaks are slightly larger with a heterogeneous community structure. We find that the simulated outbreak size is not sensitive to the size of the simulated population beyond a certain size. We also observe that in case of an outbreak, a faster public health response provides benefits similar to increased vaccination. Insights from this study can inform the control and elimination measures of the ongoing coronavirus disease (COVID-19) as measles has shown to have a similar structure to COVID-19. [ABSTRACT FROM AUTHOR]

Traditionally, financial mathematics has been used to solve financial problems. With globalization, financial transactions require new analysis based on tools of probability, statistics, and economic theory. Global research trends in this topic during the period 1935–2019 have been analyzed. With this objective, a bibliometric methodology of 1486 articles from the Scopus database was applied. The obtained results offer data on the scientific activity of countries, institutions, authors, and institutions that promote this research topic. The results reveal an increasing trend, mainly in the last decade. The main subjects of knowledge are social sciences and economics, econometrics, and finance. The author with the most articles is Khare from the Indian Institute of Management Rohtak. The most prolific affiliation is the British University of Oxford. The country with the most academic publications and international collaborations is the United States. In addition, the most used keywords in articles are "financial management", "financial transaction tax", "banking", "financial service", "blockchain", "decision making", and "financial market". The increase in publications in recent years at the international level confirms the growing trend in research on financial transactions. [ABSTRACT FROM AUTHOR]