Aggregation of Dependent Risks with Specific Marginals by the Family of Koehler-Symanowski Distributions

Many problems in Finance, such as risk management, optimal asset allocation, and derivative pricing, require an understanding of the volatility and correlations of assets returns. In these cases, it may be necessary to represent empirical data with a parametric distribution. In the literature, many distributions can be found to represent univariate data, but few can be extended to multivariate populations. The most widely used multivariate distribution in the aggregation of dependent risks in a portfolio is the Normal distribution, which has the drawbacks of inflexibility and frequent inappropriateness. Here, we consider modelling assets and measuring portfolio risks using the family of Koehler-Symanowski multivariate distributions with specific marginals, as, for example, the generalized lambda distribution. This family of distributions can be defined using the cdf along with interaction terms in the independence case. This family can be derived using a particular transformation of exponential random variables and an independent gamma. This distribution has the advantage of allowing models with complex dependence structures, as we demonstrate with Monte Carlo simulations and the analysis of the risk of a portfolioRisk Management, Monte Carlo Method, Generalized Lambda Distribution, Koehler-Symanowski Distribution

Design a robot that is able to...: gender stereotypes in children’s imagination of robots

This study explores the perceptions of gender and anthropomorphism in robots as imagined and drawn by children. It was conducted in a lower secondary school in Siena with children aged 11-13. Participants were asked to draw a robot fit for one of two job roles: house decoration (stereotypically more feminine) or snow shovelling (stereotypically more masculine). Pupils were also asked to fill out a printed ques- tionnaire with the aim to collect some general personal information and descriptions of the robot that each of them had drawn. The findings show a tendency to ascribe male or gender-neutral traits to the robots. Notably, younger children more frequently drew colourful, anthropomorphic robots for the house decoration task, whereas older students predominantly designed black and white, machine-like robots suited for the snow shovelling task

How many cyberbullying(s)? A non-unitary perspective for offensive online behaviours

Research has usually considered cyberbullying as a unitary phenomenon. Thus, it has been neglected to explore whether the specific online aggressive behaviours relate differentially to demographic features of the perpetrators of online aggressive actions, their personality characteristics, or to the ways in which they interact with the Internet. To bridge this gap, a study was conducted through a questionnaire administered online to 1228 Italian high-school students (Female: 61.1%; 14-15 yo: 48.%; 16-17 yo: 29.1%; 18-20 yo: 20.4%, 21-25 yo: 1.6%; Northern Italy: 4.1%; Central Italy: 59.2%; Southern Italy: 36.4%). The questionnaire, in addition to items about the use of social media, mechanisms of Moral Disengagement and personality characteristics of the participants in the study, also included a scale for the measurement of cyberbullying through the reference to six aggressive behaviours. The results indicate that cyberbullying can be considered as a non-unitary phenomenon in which the different aggressive behaviours can be related to different individual characteristics such as gender, personality traits and the different ways of interacting with social media. Moreover, the existence of two components of cyberbullying has been highlighted, one related to virtual offensive actions directly aimed at a victim, the other to indirect actions, more likely conducted involving bystanders. These findings open important perspectives for understanding, preventing, and mitigating cyberbullying among adolescents

Perception of Faces and Elaboration of Gender and Victim/Aggressor Stereotypes: The Influence of Internet Use and of the Perceiver’s Personality

The use of social media, particularly among youngsters, is characterized by simple and fast image exploration, mostly of people, particularly faces. The study presented here was conducted in order to investigate stereotypical judgments about men and women concerning past events of aggression—perpetrated or suffered—expressed on the basis of their faces, and gender-related differences in the judgments. To this aim, 185 participants answered a structured questionnaire online. The questionnaire contained 30 photos of young people’s faces, 15 men and 15 women (Ma et al., 2015), selected on the basis of the neutrality of their expression, and participants were asked to rate each face with respect to masculinity/femininity, strength/weakness, and having a past of aggression, as a victim or as a perpetrator. Information about the empathic abilities and personality traits of participants were also collected. The results indicate that the stereotypes—both of gender and those of victims and perpetrators—emerge as a consequence of the visual exploration of faces that present no facial emotion. Some characteristics of the personality of the observers, such as neuroticism, extraversion, openness, conscientiousness, and affective empathy, have a role in facilitating or hindering stereotype processing, in different ways for male and female faces by male and female observers. In particular, both genders attribute their positive stereotypical attributes to same-gender faces: men see male faces as stronger, masculine, and more aggressive than women do, and women see female faces as more feminine, less weak, and less as victims than men do. Intensive use of social media emerges as a factor that could facilitate the expression of some stereotypes of violent experiences and considering female subjects as more aggressive. Findings in this study can contribute to research on aggressive behavior on the Internet and improve our understanding of the multiple factors involved in the elaboration of gender stereotypes relative to violent or victim behavior

Risk measures with Generalized Secant Hyperbolic Dependence

In this paper we propose to model the dependence of multiple time series returns with a multivariate extension of the generalized secant hyperbolic distribution (GSH) using the NORTA (NORmal-to-Anything) approach and the Koehler and Symanowski copula function. The two methodologies permit to generate random vectors with marginals dis- tributed as a GSH distribution and given correlation matrix, which can be used to measure the risk of a portfolio using the Monte Carlo method

GARCH-type Models with Generalized Secant Hyperbolic Innovations

GARCH-type models have been analyzed assuming various nongaussian distributions of errors. In general, the asymmetric generalized Student-t random variable seems to be the distribution which better captures the nonnormality features of financial data. However, a drawback of this distribution is represented by the technical dificulties due to the evaluation of moments, especially in the case of fractional degrees of freedom. In this paper we propose to model high frequency time series returns using GARCH-type models with a generalized secant hyperbolic (GSH) distribution. The main advantage of the GSH distribution over the Student-t distribution is that all the moments are finite for each value of the shape parameter. The distribution is symmetric with respect to the mean, but we show that it is still possible to obtain the density in a closed form introducing a skewness parameter according to the method proposed by Fernandez and Steel. We use a Monte Carlo experiment to validate this distribution in the context of GARCH models with maximum likelihood estimates of parameters. Finally, we show an application to log returns of a stock index.

Moral judgements of errors by AI systems and humans in civil and criminal law

The evaluation of the use of Artificial Intelligence (AI) in legal decisions still has unsolved questions. These may refer to the perceived degree of seriousness of the possible errors committed, the distribution of responsibility among the different decision-makers (human or artificial), and the evaluation of the error concerning its possible benevolent or malevolent consequences on the person sanctioned. Above all, assessing the possible relationships between these variables appears relevant. To this aim, we conducted a study through an online questionnaire (N = 288) where participants had to consider different scenarios in which a decision-maker, human or artificial, made an error of judgement for offences punishable by a fine (Civil Law infringement) or years in prison (Criminal Law infringement). We found that humans who delegate AIs are blamed less than solo humans, although the effect of decision maker was subtle. In addition, people consider the error more serious if committed by a human being when a sentence for a crime of the penal code is mitigated, and for an AI when a penalty for an infringement of the civil code is aggravated. The mitigation of the evaluation of seriousness for joint AI-human judgement errors suggests the potential for strategic scapegoating of AIs

Asymmetries in the moral judgements for human decision-makers and Artificial Intelligence Systems (AI) delegated to make legal decisions

The evaluation of the use of Artificial Intelligence (AI) in legal decisions may concern several factors. We structured a study conducted by administering an online questionnaire in which the participants had to consider different scenarios in which a decision-maker, human or artificial, made an unintentionally benevolent or malevolent error of judgement for offences punishable by a fine (Civil Law infringement) or years in prison (Criminal Law infringement). We found that humans who delegate AIs are blamed less than solo humans. In addition, people consider the error more serious if committed by a human being when a sentence for a crime of the penal code is mitigated, and for an AI when a penalty is aggravated for an infringement of the civil code