A Generalized Error Distribution Copula-based method for portfolios risk assessment

In this paper, we deal with the evaluation of Conditional Value-at-Risk in the framework of portfolio theory by using a modified Gaussian Copula – where the modification is obtained by introducing the Generalized Correlation Coefficient – and by assuming a Generalized Error Distribution with properly estimated shape parameter for the returns of the considered risky assets. In so doing, we add to the connection between standard Copula theory and financial risk assessment. A comparison analysis of our findings with those obtainable through a standard Gaussian Copula-based procedure in a set of real data is also presented

A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher Moments

In this paper, by considering a model-based approach for conditional moment estimation, a nonparametric test was performed to study the long-memory property of higher moments. We considered the daily returns of the stocks included in the S&P500 index in the last ten years (for the period running from the 1st of January 2011 to the 1st of January 2021). We found that mean and skewness were characterized by short memory, while variance and shape had long memory. These results have deep implications in terms of asset allocation, option pricing and market efficiency evaluation

Sustainable urban mobility: evidence from three developed European countries

The importance acquired by private cars as the leading travel mode in most advanced countries has drawn attention to concerns related to pro-environmental travel behaviour. Indeed, the car has brought great benefits to society, albeit causing a whole lot of environmental and socio-economic consequences. In this perspective, we exploit Eurobarometer data on the attitudes of Europeans towards urban mobility to investigate the main motivations of citizens’ public transport use frequency. Ordered logistic regressions are estimated by country (Germany, Italy, and the Netherlands) and by gender. Our results suggest the key role played by a comprehensive set of socio-demographic, economic, and environmental aspects in determining urban travel behaviour. Moreover, our investigation brings to light some relevant cross-country and cross-gender commonalities and differences. The provided evidence may give policymakers a better knowledge of travel behaviour, useful for designing new interventions for environmentally-sustainable travelling

Weight-Based Discrimination in the Italian Labor Market: an Analysis of the Interaction with Gender and Ethnicity

Access to the Italian job market is undermined by several kinds of discrimination influencing the opportunities for individuals to obtain a job. In this study, we analyze together the impact of three of the most relevant kinds of discrimination operating in the Italian labor market: gender, race, and weight. Our aim is to assess whether gender and race either increase or decrease the impact of weight-based discrimination. In this respect, we submit a set of fictitious r\ue9sum\ue9s including photos of either obese or thin applicants in response to real online job offers. Our results indicate that the strongest kind of discrimination operating in the Italian labor market is the one connected to the candidate\u2019s geographical origin. Moreover, we find discrimination based on body weight to be more relevant within immigrants than within natives, and gender gap appears to be higher within the obese candidates\u2019 group compared to the normal-weight candidates\u2019 one. This last result is particularly relevant, as the growing rates of obesity forecasted for the next years could in turn produce an increase in the gender gap, which in Italy is already massive

Immigrants and Italian labor market: statistical or taste-based discrimination?

Types of discrimination are usually distinguished by economic theory in statistical and taste-based. Using a correspondence experiment, we analyze which of the two affects Italian labor market the most. In this respect, we studied the difference in discrimination reserved to first- and second-generation immigrants, taking gender differences into account. Even if we want to admit a rational discrimination based on perceived productivity differences (statistical discrimination) against first-generation immigrants (concerning language and education gaps), the same would not be reasonable for second-generation ones. Since they are born and educated in Italy, where they have always lived, the associated discrimination must be taste-based

Building a smart city service platform in Messina with the #SmartME project

© 2018 IEEE. Some words mark an era, and "Smart City" is definitely one of these. A Smart City is an urban area where the Information and Communication Technologies (ICT) are employed to improve citizens' Quality of Life (QoL) in areas such as: mobility, urban surveillance, and energy management. Throughout this paper, we present the #SmartME project, which aims to create an infrastructure and an ecosystem of "smart" services by exploiting existing devices, sensors, and actuators distributed in the city of Messina. We also present the Stack4Things framework, which is the management core of the #SmartME project

Antithrombin supplementation during extracorporeal membrane oxygenation: Study protocol for a pilot randomized clinical trial

Background: Normal levels of plasma antithrombin (AT) activity might decrease heparin requirements to achieve an adequate level of anticoagulation during treatment with extracorporeal membrane oxygenation (ECMO). Acquired AT deficiency during ECMO is common, but formal recommendations on target, timing, and rate of AT supplementation are lacking. Thus, we conceived a pilot trial to evaluate the feasibility and safety of prolonged AT supplementation in patients requiring veno-venous ECMO for respiratory failure. Methods: Grifols Antithrombin Research Awards (GATRA) is a prospective, randomized, single blinded, multicenter, controlled two-arm trial. Patients undergoing veno-venous ECMO will be randomized to either receive AT supplementation to maintain a functional AT level between 80 and 120% (AT supplementation group) or not (control group) for the entire ECMO course. In both study groups, anticoagulation will be provided with unfractionated heparin following a standardized protocol. The primary endpoint will be the dose of heparin required to maintain the ratio of activated partial thromboplastin time between 1.5 and 2. Secondary endpoints will be the adequacy of anticoagulation and the incidence of hemorrhagic and thrombotic complications. Discussion: GATRA is a pilot trial that will test the efficacy of a protocol of AT supplementation in decreasing the heparin dose and improving anticoagulation adequacy during ECMO. If positive, it might provide the basis for a future larger trial aimed at verifying the impact of AT supplementation on a composite outcome endpoint including hemorrhagic events, transfusion requirements, and mortality. Trial registration: ClinicalTrials.gov, NCT03208270. Registered on 5 July 2017

GreeDi: Energy Efficient Routing Algorithm for Big Data on Cloud

The ever-increasing density in cloud computing parties, i.e. users, services, providers and data centres, has led to a significant exponential growth in: data produced and transferred among the cloud computing parties; network traffic; and the energy consumed by the cloud computing massive infrastructure, which is required to respond quickly and effectively to users requests. Transferring big data volume among the aforementioned parties requires a high bandwidth connection, which consumes larger amounts of energy than just processing and storing big data on cloud data centres, and hence producing high carbon dioxide emissions. This power consumption is highly significant when transferring big data into a data centre located relatively far from the users geographical location. Thus, it became high-necessity to locate the lowest energy consumption route between the user and the designated data centre, while making sure the users requirements, e.g. response time, are met. The main contribution of this paper is GreeDi, a network-based routing algorithm to find the most energy efficient path to the cloud data centre for processing and storing big data. The algorithm is, first, formalised by the situation calculus. The linear, goal and dynamic programming approaches used to model the algorithm. The algorithm is then evaluated against the baseline shortest path algorithm with minimum number of nodes traversed, using a real Italian ISP physical network topology

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures

Off–label long acting injectable antipsychotics in real–world clinical practice: a cross-sectional analysis of prescriptive patterns from the STAR Network DEPOT study

Introduction: Information on the off–label use of Long–Acting Injectable (LAI) antipsychotics in the real world is lacking. In this study, we aimed to identify the sociodemographic and clinical features of patients treated with on– vs off–label LAIs and predictors of off–label First– or Second–Generation Antipsychotic (FGA vs. SGA) LAI choice in everyday clinical practice. Method: In a naturalistic national cohort of 449 patients who initiated LAI treatment in the STAR Network Depot Study, two groups were identified based on off– or on–label prescriptions. A multivariate logistic regression analysis was used to test several clinically relevant variables and identify those associated with the choice of FGA vs SGA prescription in the off–label group. Results: SGA LAIs were more commonly prescribed in everyday practice, without significant differences in their on– and off–label use. Approximately 1 in 4 patients received an off–label prescription. In the off–label group, the most frequent diagnoses were bipolar disorder (67.5%) or any personality disorder (23.7%). FGA vs SGA LAI choice was significantly associated with BPRS thought disorder (OR = 1.22, CI95% 1.04 to 1.43, p = 0.015) and hostility/suspiciousness (OR = 0.83, CI95% 0.71 to 0.97, p = 0.017) dimensions. The likelihood of receiving an SGA LAI grew steadily with the increase of the BPRS thought disturbance score. Conversely, a preference towards prescribing an FGA was observed with higher scores at the BPRS hostility/suspiciousness subscale. Conclusion: Our study is the first to identify predictors of FGA vs SGA choice in patients treated with off–label LAI antipsychotics. Demographic characteristics, i.e. age, sex, and substance/alcohol use co–morbidities did not appear to influence the choice towards FGAs or SGAs. Despite a lack of evidence, clinicians tend to favour FGA over SGA LAIs in bipolar or personality disorder patients with relevant hostility. Further research is needed to evaluate treatment adherence and clinical effectiveness of these prescriptive patterns