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The last few years in the financial markets have shown great instability and high volatility. In order to capture the amount of risk a financial firm takes on in a single trading day, risk managers use a technology known as value‐at‐risk (VaR). There are many methodologies available to calculate VaR, and each has its limitations. Many past methods have included a normality assumption, which can often produce misleading figures as most financial returns are characterized by skewness (asymmetry) and leptokurtosis (fat‐tails). The purpose of this paper is to provide an overview of VaR and describe some of the most recent computational approaches.

This paper compares the Student‐t, autoregressive conditional heteroskedastic (ARCH) family of models, and extreme value theory (EVT) as a means of capturing the fat‐tailed nature of a returns distribution.

Recent research has utilized the third and fourth moments to estimate the shape index parameter of the tail. Other approaches, such as extreme value theory, focus on the extreme values to calculate the tail ends of a distribution. By highlighting benefits and limitations of the Student‐t, autoregressive conditional heteroskedastic (ARCH) family of models, and the extreme value theory, one can see that there is no one particular model that is best for computing VaR (although all of the models have proven to capture the fat‐tailed nature better than a normal distribution).

This paper details the basic advantages, disadvantages, and mathematics of current parametric methodologies used to assess value‐at‐risk (VaR), since accurate VaR measures reduce a firm's capital requirement and reassure creditors and investors of the firm's risk level.

Background: The ageing population is generating increasing concern over the occurrence and associated costs of falls in healthcare settings. Supplementary to the investigation of strategies to prevent falls, is the consideration of ways to reduce the number of injuries resulting from falls in these settings.Aims: This overview assesses the status of research on flooring in healthcare settings to reduce the incidence of injury resulting from falls.Methods: A comprehensive literature search, carried out in conjunction with a Cochrane Systematic Review on hospital environments for patient health‐related outcomes, identified the available evidence. Searches were also conducted in Medline and Scopus specifically to identify studies on flooring types, falls, and injuries. Reference lists of relevant studies and reviews were scanned and relevant authors were approached for further information.Conclusions: Flooring should be considered as a possible intervention for reducing injuries from falls, however, more rigorous and higher quality research is needed to identify the most appropriate materials for use.

Many jurisdictions fine illegal cartels using penalty guidelines that presume an arbitrary 10% overcharge. This article surveys more than 700 published economic studies and judicial decisions that contain 2,041 quantitative estimates of overcharges of hard-core cartels. The primary findings are: (1) the median average long-run overcharge for all types of cartels over all time periods is 23.0%; (2) the mean average is at least 49%; (3) overcharges reached their zenith in 1891–1945 and have trended downward ever since; (4) 6% of the cartel episodes are zero; (5) median overcharges of international-membership cartels are 38% higher than those of domestic cartels; (6) convicted cartels are on average 19% more effective at raising prices as unpunished cartels; (7) bid-rigging conduct displays 25% lower markups than price-fixing cartels; (8) contemporary cartels targeted by class actions have higher overcharges; and (9) when cartels operate at peak effectiveness, price changes are 60–80% higher than the whole episode. Historical penalty guidelines aimed at optimally deterring cartels are likely to be too low.

Tourism and crime are closely related phenomena, and security is one of the basic preconditions for the functioning of tourism since tourists and tourist areas have many characteristics that make them vulnerable to crime. In this chapter are presented the actual (objective) risk of crime and tourists victimization, visible in statistics on committed crimes and crime victims surveys, and the perceived (subjective) risk of crime, recorded in surveys conducted with tourists. The characteristics which influence the actual and perceived risk of crime and violence are presented by analysing three key elements in the relationship between tourism and crime: (1) tourist (these characteristics are classified as socio-demographic, socio-cultural and psychological); (2) trip (characteristics are the purpose of the trip, travel party, and stage of the trip); and (3) destination (characteristics are crime rates in destination, the occurrence of crime by place and time, type of accommodation and length of stay).

This paper discusses how minor counterproductive workplace behavior (CWB) scripts can be acquired or learned through automated processes from one employee to another.

This research is based on insights from social information processing and automated processing.

This paper helps explain the automated learning of minor CWBs from one’s coworkers.

While some employees purposefully engage in counterproductive workplace behaviors with the intent to harm their organizations, other less overt and minor behaviors are not always carried out with harmful intent, but remain counterproductive, nonetheless. By understanding how the transfer of minor CWBs occurs, employers can strive to set policies and practices in place to help reduce these occurrences.

This paper discusses how negative workplace learning can occur. We hope to contribute to the workplace learning literature by highlighting how and why the spread of minor CWBs occurs amongst coworkers and spur future research focusing on appropriate interventions.

In this chapter, we consider the complexity of issues associated with violence in schools and provide an overview of this 33rd volume of Advances in Learning and Behavioral Disabilities. We begin with a brief consideration of the nature and definitions of violence as it manifests in schools and then consider three broad areas addressed by the chapters in this volume. First, we consider bullying and the bullying dynamic, including cyberbullying, and the intersection of bullying and students with disabilities. Next, we address the extraordinarily difficult topic of school shootings, including whether and how we can predict, prevent, and respond to school shootings. Finally, we consider more broadly advances in building a more positive school climate and sense of community and creating safer schools generally. In all of these, we acknowledge the challenges of understanding the complexity and multiple causes of school violence, and the apparent rise in many forms of violence in schools, but conclude with thoughts on the positive avenues identified by authors in this volume for ways we might better support children and youth in both preventing violence and responding to it in appropriate, supportive ways.

There is a paucity of data available relating to the misconduct of police officers in larger policing agencies, typically resulting in case study approaches and limited insight into the factors associated with serious misconduct. This paper seeks to contribute to the emerging knowledge base on police misconduct through analysis of 28,429 complaints among 3,830 officers in the New York Police Department, between 2000 and 2019.

This study utilized a data set consisting of officer and complainant demographics, and officer complaint records. Machine learning analytics were employed, specifically random forest, to consider which variables were most associated with serious misconduct among officers that committed misconduct. Partial dependence plots were employed among variables identified as important to consider the points at which misconduct was most, and least likely to occur.

Prior instances of serious misconduct were particularly associated with further instances of serious misconduct, while remedial action did not appear to have an impact in preventing further misconduct. Inexperience, both in rank and age, was associated with misconduct. Specific prior complaints, such as minor use of force, did not appear to be particularly associated with instances of serious misconduct. The characteristics of the complainant held more importance than the characteristics of the officer.

The ability to analyze a data set of this size is unusual and important to progressing the knowledge area regarding police misconduct. This study contributes to the growing use of machine learning in understanding the police misconduct environment, and more accurately tailoring misconduct prevention policy and practice.

Students with emotional and behavioral disorders (EBD) who display aggression necessitate effective interventions for reducing highly disruptive behavior, while keeping learning environments safe and secure for all students and staff. In this chapter, we describe the merits of cognitive-behavioral interventions (CBIs) in school settings to reduce student aggression and other destructive and maladaptive behavior and to promote student success and lifelong learning. To that end, we first explore three theoretical frameworks for aggression: the general aggression model, social learning theory, and social information processing, each of which examines the role of environment, cognition, and behavior as foundational to the occurrence of aggression. Synthesizing these theories assists in the development and implementation of CBIs in classroom settings. We then describe the CBI approach to teaching students cognitive and behavioral strategies to reduce problematic behaviors and increase the use of more pro-social alternatives, and ultimately generalize learned skills to a variety of social situations. A brief history of CBIs is explored, followed by a discussion of several meta-analyses establishing CBI's effectiveness in decreasing aggression across a variety of venues and populations. We then focus on social problem solving as an example of a cognitive-behavioral approach and describe the Tools for Getting Along curriculum as an example of a school-based CBI. At the end of the chapter, we explain some limitations of CBIs in schools and delineate future research needs.