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The main goal of this paper is to investigate whether there is long-memory behavior in the CBOE Brazil ETF volatility index (named here VIXBR). As structural breaks may create a spurious long-range dependence, the presence of structural breaks is also gauged.

The study considers the period from October 2011 to March 2021, using daily data. To test the long-memory behavior, three empirical approaches are adopted: GPH, ELW and robust GPH (RGPH) estimator. To estimate the structural break points adopted to date the subsamples, the ICSS algorithm is used.

Results considering the total period (TP) and subsamples show that the breaks did not create a spurious long-memory behavior and together with the rolling estimation, reveal strong evidence of the long-range dependence in the CBOE Brazil ETF volatility index. The higher degree of persistent of the VIXBR series suggests an extended period of increased uncertainty that agents need consider when making their investment decision.

As possible extension of this study is to investigate the behavior of long memory and structural breaks for different frequencies (weekly, monthly, among others).

The presence of long-range dependence in the CBOE Brazil ETF volatility index reveals that the past information is important for the predictability of risks, and therefore, can help to protect against market risks, which has important implications regarding the future decisions of economic agents (for example, policy makers and investors).

Brazil is an emerging capital market (ECM) that has attracted a great deal of attention from investors and investment funds seeking to diversify its assets. This paper contributes to the empirical financial literature, by studying the long-memory behavior of the CBOE Brazil ETF volatility index, considering possible structural breaks. To the best of knowledge, this has not been done so far.

The purpose of this paper is to perform Shariah review of Brownian motion that is used for prediction of Islamic stock prices and their volatility.

It uses the Shariah compliant development model guidelines to review the Brownian motion and its applications.

The model of Brownian motion does not involve any variable that renders it non-Shariah compliant; neither all applications of Brownian motion are Shariah compliant. Because the model is based on stochastic properties that involve randomness, therefore the issue of gharar takes the utmost important to handle in the applications of the model. The results need to be analyzed strictly in accordance with the Shariah whether they create any element of gharar or uncertainty in case of expected price and volatility estimates.

The research suffers from the limitation that it analyses only one model of physics, i.e. Brownian motion model from Shariah perspective.

The research opens an area for Shariah analysis of results generated from the application of advanced models of physics on matters related to Islamic financial markets.

The originality of this study stems from the fact that to the best of the authors’ knowledge, it is the first study that extends Shariah guidelines into Financial physics for making the foundations of Islamic econophysics.

This paper aims to design a global controller that is operational throughout all flight modes and less dependent on an accurate model.

By adopting the interconnection and damping assignment passivity-based control (IDA-PBC) technology and compensating extra inputs for handling the unknown dynamics and time-varying disturbances, a model-free control (MFC)-based global controller is proposed.

Test results indicate that the designed controllers are more suitable for actual flight as they have smaller position tracking errors and energy consumption in all flight phases than the excellent model-free controller intelligent-PID.

The designed global controller, which works in all flight modes without adjusting its structure and parameters, can realize a stable and accurate tracking control of a tail-sitter and improve the resistance to unknown disturbances and model uncertainties.

The newly-designed controller is considered as an enhanced version of the traditional MFC. It further improves the control effect by using the poorly known dynamics of the system and choosing the IDA-PBC as the control auxiliary input. This method eliminates the unnecessary dynamics to continuously stabilize the vehicle with suitable energy consumption covering its entire flight envelope.

This paper uses financial report information to analyze the accounting results of the COVID-19 vaccine development for Johnson & Johnson (J&J). This paper also uses stock price information to analyze the market reactions to the COVID-19 vaccine development and the state of clinical trials for J&J.

This instructional case investigates the interaction between J&J and the COVID-19 vaccine. This paper uses information from financial reports to analyze the accounting results of the COVID-19 vaccine development for J&J. This paper also uses stock price information to analyze the market’s reactions to the COVID-19 vaccine development and the state of clinical trials for J&J.

This case has been used in both undergraduate and graduate levels to highlight the application of accounting theories to practice and improve the understanding of financial statements, especially when Covid-19 has affected the global economy. Under this new context, students could explore new ideas from accounting aspect.

The case aims to investigate the interaction between J&J as a pharmaceutical company and COVID-19. It provides a context in which to discuss the consequences of COVID-19 vaccines from several financial perspectives, such as stock prices, accounting policies, earnings and cash flows:

LO1: Understand the responses of stakeholders to J&J’s COVID-19 vaccines.

LO2: Understand the accounting policies that J&J and its competitors follow regarding COVID-19 vaccines related to revenues, R&D expenditures and government funds.

LO3: Apply Ball and Brown’s theory to the impact of COVID-19 vaccine development on earnings quality of J&J and its competitors.

LO4: Assess the importance of COVID-19 vaccines in management decision-making through dividend policy and management compensation structure.

This chapter introduces the best linear predictor (BLP) with the asymptotic minimum mean squared forecasting error (MSFE) among linear predictors of variables in cointegrated systems. Accordingly, the authors show that (i) if the autocorrelation coefficient of the cointegration error between the prediction time and the predicted targeting time is larger than ½ (representing a short prediction period), then the BLP is deduced from the random walk model; and (ii) in other cases (representing a long prediction period), the BLP is deduced from the cointegration model. Under this scheme, we suggest a switching predictor that automatically selects the random walk or cointegration model according to the size of the estimated autocorrelation coefficient. These results effectively explain the superiority reversal in the short- and long-term prediction of the exchange rate between the random walk and the structural/cointegration model (known as the Meese–Rogoff or disconnect puzzle).

Outpatient care delivery is one of the key revenue sources of a hospital which plays a salient role in timely care delivery. The key purpose of the study is to propose a multi-objective simulation-based decision support model that considers the cost of care delivery and patient dissatisfaction as its two key conflicting objectives. Patient dissatisfaction considers service fairness. Patient idiosyncrasies such as no-show, unpunctuality and balking have been considered in the model involving multiple classes of patients.

A model has been designed using data collected from field investigations. In the first stage, queuing theory based discrete event simulation model has been developed. Genetic algorithm has been used to solve the scalarized problem and obtain actionable insights. In the second stage, non-dominated sorting genetic algorithm II (NSGA-II) has been involved to achieve the Pareto optimal fronts considering equal priority of the two objectives.

The computational results considering various parameter settings can help in efficient resource planning while ensuring better care delivery. The model proposed in the study provides structural insights on the business strategy of healthcare service providers on optimizing the dual goals of care delivery cost and service fairness.

The study is one of the early works that helps to improve the care delivery process by taking into consideration the environmental factors as well as service fairness. The study demonstrates the usage of simulation-based multi-objective optimization to provide a more sustainable patient centric care delivery.

A stylized fact in finance literature is the belief in positive relationship between ex ante return and risk. Hence, a rational investor, by utility preference axiom can only consider committing fund in asset which promises commensurate higher return for higher risk. Questions have been asked as to whether this holds true across securities, sectors and markets. Empirical evidence appears less convincing, especially in developing markets. Accordingly, the author investigates the nature of reward for taking risk in the Nigerian Capital Market within the context of individual assets and markets.

The author employed ex post design to collect weekly stock prices of firms listed on the Premium Board of Nigerian Stock Exchange for period 2014–2022 to attempt to answer research questions. Data were analyzed using a unique M Vec TGarch-in-Mean model considered to be robust in handling many assets, and hence portfolio management.

The study found that idea of risk-expected return trade-off is perhaps more general than as depicted by traditional finance literature. The regression revealed that conditional variance and covariance risks reveal minimal or no differences in sign and sizes of coefficients. However, standard errors were also found to be large suggesting somewhat inconclusive evidence of existence of defined incentive structure for taking additional risk in the market.

In terms of choice of methodology and outcomes, this research adds substantial value to body of knowledge. The adapted multivariate model used in this paper is a rare approach especially for management of portfolios in developing markets. Remarkably, the research found empirical evidence that positive risk-expected return trade-off, as known in mainstream literature, is not supported especially using a typical developing country data.

The purpose of this paper is to test the weak form of the efficient market hypothesis (EMH) using monthly data from 2004M08 to 2018M04 for two Canadian stock indices: the Islamic (DJICPI) and the conventional (CCSI). This paper investigates whether Islamic and/or conventional stock market would be efficient through the non-stationarity test of the stock indices.

The authors conduct the linearity test of Harvey et al. (2008) to identify whether the considered series has linear or nonlinear behavior. If the time series exhibits nonlinear evolution, then the authors apply nonlinear unit root tests (three KSS type tests and Sollis tests).

Linearity test results say that LCCSI has nonlinear behavior, while Dow Jones Islamic Canadian Price Index, LDJICPI, is a linear process. Then, the findings of this paper show that only Canadian Islamic Price Index (DJICPI) has the characteristics of random walk indicating that only conventional stock markets are inefficient. The major implication is that in Canada, fund managers and investors can (cannot) enjoy excess returns to their investment in conventional (Islamic) stock market.

Numerous empirical studies of the weak EMH are carried out within a linear framework. However, stock indices can show nonlinear behavior as a result of 2008 global financial crisis. To contribute to the existing literature on the Islamic and conventional stock market efficiency, the authors take into account both structural breaks and nonlinearity. Thus, as a testing strategy for weak EMH, the authors perform (Harvey et al., 2008) linearity test to examine the presence of nonlinear behavior and correct for outliers effect when it is needed.

Privacy preservation is a significant concern in Internet of Things (IoT)-enabled event-driven wireless sensor networks (WSNs). Low energy utilization in the event-driven system is essential if events do not happen. When events occur, IoT-enabled sensor network is required to deal with enormous traffic from the concentration of demand data delivery. This paper aims to explore an effective framework for safeguarding privacy at source in event-driven WSNs.

This paper discusses three algorithms in IoT-enabled event-driven WSNs: source location privacy for event detection (SLP_ED), chessboard alteration pattern (SLP_ED_CBA) and grid-based source location privacy (GB_SLP). Performance evaluation is done using simulation results and security analysis of the proposed scheme.

The sensors observe bound events or sensitive items within the network area in the field of interest. The open wireless channel lets an opponent search traffic designs, trace back and reach the start node or the event-detecting node. SLP_ED and SLP_ED_CBA provide better safety level results than dynamic shortest path scheme and energy-efficient source location privacy protection schemes. This paper discusses security analysis for the GB_SLP. Comparative analysis shows that the proposed scheme is more efficient on safety level than existing techniques.

The authors develop the privacy protection scheme in IoT-enabled event-driven WSNs. There are two categories of occurrences: nominal events and critical events. The choice of the route from source to sink relies on the two types of events: nominal or critical; the privacy level required for an event; and the energy consumption needed for the event. In addition, phantom node selection scheme is designed for source location privacy.

The existing literature offers various perspectives on integrating cryptocurrencies into investment portfolios; yet, there is a gap in understanding the behaviours, attitudes and cross-investment links of individual investors. This study, grounded in the modern portfolio theory and the random walk theory, aims to add empirical insights that are specific to the UK context. It explores four hypotheses related to the influence of socio-demographics, digital adoption, cross-investment behaviours and financial attitudes on cryptocurrency owners.

This study uses a logistic regression model with secondary data from the Financial Lives Survey 2020 to assess the factors impacting cryptocurrency ownership. A total of 29 variables are used, categorized into four groups aligned with the hypotheses. Additionally, hierarchical clustering analysis was conducted to further explore the cross-investment links.

The study reveals a significant lack of diversification among UK cryptocurrency investors, a pronounced inclination towards high-risk investments such as peer-to-peer lending and crowdfunding, and parallels with gambling behaviours, including financial dissatisfaction and a propensity for risk-taking. It highlights the influence of demographic traits, risk tolerance, technological literacy and emotional attitudes on cryptocurrency investment decisions.

This study provides valuable insights into cryptocurrency regulation and retail investor protection, underscoring the necessity for tailored financial education and a holistic regulatory approach for investment products with comparable risk levels, with the aim of minimizing regulatory arbitrage. It significantly enhances our understanding of the unique dynamics of cryptocurrency investments within the evolving financial landscape.