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Volatility is the most widespread measure of risk. Volatility modeling allows investors to capture potential losses and investment opportunities. This work aims to examine the impact of the two waves of COVID-19 infections on the return and volatility of the stock market indices of the euro area countries. The study also focuses on other important aspects such as time-varying risk premium and leverage effect. This investigation employed the Threshold GARCH(1,1)-in-Mean model with exogenous dummy variables. Daily returns of the euro area stock markets indices from 4 January 2016 to 31 December 2020 has been used for the analysis. The results reveal that euro area stock markets respond differently to the COVID-19 pandemic. Specifically, the first wave of COVID-19 infections had a notable impact on stock market volatility of euro area countries with middle-large financial centres while the second wave had a significant impact only on stock market volatility of Belgium.
Pierdomenico Duttilo; Stefano Gattone; Tonio Di Battista. Volatility Modeling: An Overview of Equity Markets in the Euro Area during COVID-19 Pandemic. Mathematics 2021, 9, 1212 .
AMA StylePierdomenico Duttilo, Stefano Gattone, Tonio Di Battista. Volatility Modeling: An Overview of Equity Markets in the Euro Area during COVID-19 Pandemic. Mathematics. 2021; 9 (11):1212.
Chicago/Turabian StylePierdomenico Duttilo; Stefano Gattone; Tonio Di Battista. 2021. "Volatility Modeling: An Overview of Equity Markets in the Euro Area during COVID-19 Pandemic." Mathematics 9, no. 11: 1212.
Volatility is the most widespread measure of risk. Volatility modeling allows investors to capture potential losses and investment opportunities. This work aims to examine the impact of the two waves of COVID-19 infections on the return and volatility of the stock market indices of the euro area countries. The study also focuses on other important aspects such as time-varying risk premium and leverage effect. Thus, this investigation employed the Threshold GARCH(1,1)-in-Mean model with exogenous dummy variables. Daily returns of ten euro area stock indices from 4th January 2016 to 31th December 2020 has been used for the analysis. The results reveal that euro area stock markets respond differently to the COVID-19 pandemic. Specifically, the first wave of COVID-19 infections had a notable impact on stock market volatility of euro area countries with large and middle financial centres while the second wave had a significant impact only on stock market volatility of Belgium.
Pierdomenico Duttilo; Stefano Antonio Gattone; Tonio Battista. Volatility Modeling: An Overview of Equity Markets in the Euro Area During COVID-19 Pandemic. 2021, 1 .
AMA StylePierdomenico Duttilo, Stefano Antonio Gattone, Tonio Battista. Volatility Modeling: An Overview of Equity Markets in the Euro Area During COVID-19 Pandemic. . 2021; ():1.
Chicago/Turabian StylePierdomenico Duttilo; Stefano Antonio Gattone; Tonio Battista. 2021. "Volatility Modeling: An Overview of Equity Markets in the Euro Area During COVID-19 Pandemic." , no. : 1.
Cluster analysis has for long been an effective tool for analysing data. Thus, several disciplines, such as marketing, psychology and computer sciences, just to mention a few, did take advantage from its contribution over time. Traditionally, this kind of algorithm concentrates only on numerical or categorical data at a time. In this work, instead, we analyse a dataset composed of mixed data, namely both numerical than categorical ones. More precisely, we focus on profiling visitors of the National Park of Majella in the Abruzzo region of Italy, which observations are characterized by variables such as gender, age, profession, expectations and satisfaction rate on park services. Applying a standard clustering procedure would be wholly inappropriate in this case. Therefore, we hereby propose an unsupervised classification of mixed data, a specific procedure capable of processing both numerical than categorical variables simultaneously, releasing truly precious information. In conclusion, our application therefore emphasizes how cluster analysis for mixed data can lead to discover particularly informative patterns, allowing to lay the groundwork for an accurate customers profiling, starting point for a detailed marketing analysis.
Giulia Caruso; Adelia Evangelista; Stefano Antonio Gattone. Profiling visitors of a national park in Italy through unsupervised classification of mixed data. Proceedings e report 2021, 127, 135 -140.
AMA StyleGiulia Caruso, Adelia Evangelista, Stefano Antonio Gattone. Profiling visitors of a national park in Italy through unsupervised classification of mixed data. Proceedings e report. 2021; 127 ():135-140.
Chicago/Turabian StyleGiulia Caruso; Adelia Evangelista; Stefano Antonio Gattone. 2021. "Profiling visitors of a national park in Italy through unsupervised classification of mixed data." Proceedings e report 127, no. : 135-140.
One of the key indicators of a population’s well-being and the economic development of a country is represented by health, the main proxy for which is life expectancy at birth. Some factors, such as industrialization and modernization, have allowed this to improve considerably. On the other hand, along with high global population growth, the factor which may jeopardize human health the most is environmental degradation, which can be tackled through the transition to renewable energy. The main purpose of our study is to investigate the relationship between renewable energy consumption, social factors, and health, using a Panel Vector Auto Regression (PVAR) technique. We explore the link between some proxy variables for renewable energy consumption, government policy, general public awareness, the market, lobbying activity, the energy dependence on third countries, and health, spanning the period from 1990 to 2015, for a cluster of 12 European countries characterized by common features. Specifically, our analysis shows the importance of having a stringent policy for the development of renewable energy consumption and its influence over other social factors, rather than the existence of causal relationships between health and renewable energy consumption for the analyzed countries. This kind of analysis has a great potential for policy-makers. Further, a deeper understanding of these relationships can create a more effective decision-making process.
Giulia Caruso; Emiliano Colantonio; Stefano Antonio Gattone. Relationships between Renewable Energy Consumption, Social Factors, and Health: A Panel Vector Auto Regression Analysis of a Cluster of 12 EU Countries. Sustainability 2020, 12, 2915 .
AMA StyleGiulia Caruso, Emiliano Colantonio, Stefano Antonio Gattone. Relationships between Renewable Energy Consumption, Social Factors, and Health: A Panel Vector Auto Regression Analysis of a Cluster of 12 EU Countries. Sustainability. 2020; 12 (7):2915.
Chicago/Turabian StyleGiulia Caruso; Emiliano Colantonio; Stefano Antonio Gattone. 2020. "Relationships between Renewable Energy Consumption, Social Factors, and Health: A Panel Vector Auto Regression Analysis of a Cluster of 12 EU Countries." Sustainability 12, no. 7: 2915.
The increase in global population and the improvement of living standards in developing countries has resulted in higher solid waste generation. Solid waste management increasingly represents a challenge, but it might also be an opportunity for the municipal authorities of these countries. To this end, the awareness of a variety of factors related to waste management and an efficacious in-depth analysis of them might prove to be particularly significant. For this purpose, and since data are both qualitative and quantitative, a cluster analysis specific for mixed data has been implemented on the dataset. The analysis allows us to distinguish two well-defined groups. The first one is poorer, less developed, and urbanized, with a consequent lower life expectancy of inhabitants. Consequently, it registers lower waste generation and lower C O 2 emissions. Surprisingly, it is more engaged in recycling and in awareness campaigns related to it. Since the cluster discrimination between the two groups is well defined, the second cluster registers the opposite tendency for all the analyzed variables. In conclusion, this kind of analysis offers a potential pathway for academics to work with policy-makers in moving toward the realization of waste management policies tailored to the local context.
Giulia Caruso; Stefano Antonio Gattone. Waste Management Analysis in Developing Countries through Unsupervised Classification of Mixed Data. Social Sciences 2019, 8, 186 .
AMA StyleGiulia Caruso, Stefano Antonio Gattone. Waste Management Analysis in Developing Countries through Unsupervised Classification of Mixed Data. Social Sciences. 2019; 8 (6):186.
Chicago/Turabian StyleGiulia Caruso; Stefano Antonio Gattone. 2019. "Waste Management Analysis in Developing Countries through Unsupervised Classification of Mixed Data." Social Sciences 8, no. 6: 186.
In this paper, the problem of clustering rotationally invariant shapes is studied and a solution using Information Geometry tools is provided. Landmarks of a complex shape are defined as probability densities in a statistical manifold. Then, in the setting of shapes clustering through a K-means algorithm, the discriminative power of two different shapes distances are evaluated. The first, derived from Fisher–Rao metric, is related with the minimization of information in the Fisher sense and the other is derived from the Wasserstein distance which measures the minimal transportation cost. A modification of the K-means algorithm is also proposed which allows the variances to vary not only among the landmarks but also among the clusters.
Stefano Antonio Gattone; Angela De Sanctis; Stéphane Puechmorel; Florence Nicol. On the Geodesic Distance in Shapes K-means Clustering. Entropy 2018, 20, 647 .
AMA StyleStefano Antonio Gattone, Angela De Sanctis, Stéphane Puechmorel, Florence Nicol. On the Geodesic Distance in Shapes K-means Clustering. Entropy. 2018; 20 (9):647.
Chicago/Turabian StyleStefano Antonio Gattone; Angela De Sanctis; Stéphane Puechmorel; Florence Nicol. 2018. "On the Geodesic Distance in Shapes K-means Clustering." Entropy 20, no. 9: 647.
Stefano Antonio Gattone; Angela De Sanctis; Tommaso Russo; Domitilla Pulcini. A shape distance based on the Fisher–Rao metric and its application for shapes clustering. Physica A: Statistical Mechanics and its Applications 2017, 487, 93 -102.
AMA StyleStefano Antonio Gattone, Angela De Sanctis, Tommaso Russo, Domitilla Pulcini. A shape distance based on the Fisher–Rao metric and its application for shapes clustering. Physica A: Statistical Mechanics and its Applications. 2017; 487 ():93-102.
Chicago/Turabian StyleStefano Antonio Gattone; Angela De Sanctis; Tommaso Russo; Domitilla Pulcini. 2017. "A shape distance based on the Fisher–Rao metric and its application for shapes clustering." Physica A: Statistical Mechanics and its Applications 487, no. : 93-102.
Shape Analysis studies geometrical objects, as for example a flat fish in the plane or a human head in the space. The applications range from structural biology, computer vision, medical imaging to archaeology. We focus on the selection of an appropriate measurement of distance among observations with the aim of obtaining an unsupervised classification of shapes. Data from a shape are often realized as a set of representative points, called landmarks. For planar shapes, we assume that each landmark is modeled via a bivariate Gaussian, where the means capture uncertainties that arise in landmarks placement and the variances the natural variability across the population of shapes. At first we consider the Fisher-Rao metric as a Riemannian metric on the Statistical Manifold of the Gaussian distributions. The induced geodesic-distance is related with the minimization of information in the Fisher sense and we can use it to discriminate shapes. Another suitable distance is the Wasserstein distance, which is induced by a Riemannian metric and is related with the minimal transportation cost. In this work, a simulation study is conducted in order to make a comparison between Wasserstein and Fisher-Rao metrics when used in shapes clustering.
Angela De Sanctis; Stefano Antonio Gattone. A Comparison between Wasserstein Distance and a Distance Induced by Fisher-Rao Metric in Complex Shapes Clustering. Proceedings 2017, 2, 163 .
AMA StyleAngela De Sanctis, Stefano Antonio Gattone. A Comparison between Wasserstein Distance and a Distance Induced by Fisher-Rao Metric in Complex Shapes Clustering. Proceedings. 2017; 2 (4):163.
Chicago/Turabian StyleAngela De Sanctis; Stefano Antonio Gattone. 2017. "A Comparison between Wasserstein Distance and a Distance Induced by Fisher-Rao Metric in Complex Shapes Clustering." Proceedings 2, no. 4: 163.
The implementation of an adaptive cluster sampling design often becomes logistically challenging because variation in the final sampling effort introduces uncertainty in survey planning. To overcome this drawback, an inexpensive and easy to measure auxiliary variable could be used in a two-phase survey strategy, called adaptive cluster double sampling (Félix-Medina and Thompson in Biometrika 91:877–891, 2004). In this paper, a two-phase sampling strategy is proposed which combines the idea of adaptive cluster double sampling with the principle of post-stratification. In the first-phase an adaptive cluster sample is selected by means of an inexpensive auxiliary variable. Networks from the first phase sampling are then post-stratified according to their size. In the second-phase, the network structure is used to select a subsample of units by means of stratified random sampling. The proposed sampling strategy employs stratification without requiring an a priori delineation of the strata. Indeed, the strata sizes are estimated in the course of the two-phase sampling process. Therefore, it is suitable for situations where stratification is suspected to be efficient but strata cannot be easily delineated in advance. In this framework, a new type of estimator for the population mean which mimics the stratified sampling mean estimator and an estimator of the sampling variance are proposed. The results of a simulation study confirm, as expected, that the use of post-stratification leads to gain in precision for the estimator. The proposed sampling strategy is applied for targeting an epiphytic lichen community Lobarion pulmonariae in a forest area of the Northern Apennines (N-Italy), characterized by several species of conservation concern.
Stefano Antonio Gattone; Paolo Giordani; Tonio di Battista; Francesca Fortuna. Adaptive cluster double sampling with post stratification with application to an epiphytic lichen community. Environmental and Ecological Statistics 2017, 25, 125 -138.
AMA StyleStefano Antonio Gattone, Paolo Giordani, Tonio di Battista, Francesca Fortuna. Adaptive cluster double sampling with post stratification with application to an epiphytic lichen community. Environmental and Ecological Statistics. 2017; 25 (1):125-138.
Chicago/Turabian StyleStefano Antonio Gattone; Paolo Giordani; Tonio di Battista; Francesca Fortuna. 2017. "Adaptive cluster double sampling with post stratification with application to an epiphytic lichen community." Environmental and Ecological Statistics 25, no. 1: 125-138.
The aim of this study is to model shapes from complex systems using Information Geometry tools. It is well-known that the Fisher information endows the statistical manifold, defined by a family of probability distributions, with a Riemannian metric, called the Fisher-Rao metric. With respect to this, geodesic paths are determined, minimizing information in Fisher sense. Under the hypothesis that it is possible to extract from the shape a finite number of representing points, called landmarks, we propose to model each of them with a probability distribution, as for example a multivariate Gaussian distribution. Then using the geodesic distance, induced by the Fisher-Rao metric, we can define a shape metric which enables us to quantify differences between shapes. The discriminative power of the proposed shape metric is tested performing a cluster analysis on the shapes of three different groups of specimens corresponding to three species of flatfish. Results show a better ability in recovering the true cluster structure with respect to other existing shape distances.
Angela De Sanctis; Stefano Antonio Gattone. Fisher Information Geometry for Shape Analysis. Proceedings of 3rd International Electronic and Flipped Conference on Entropy and Its Applications 2016, 1 .
AMA StyleAngela De Sanctis, Stefano Antonio Gattone. Fisher Information Geometry for Shape Analysis. Proceedings of 3rd International Electronic and Flipped Conference on Entropy and Its Applications. 2016; ():1.
Chicago/Turabian StyleAngela De Sanctis; Stefano Antonio Gattone. 2016. "Fisher Information Geometry for Shape Analysis." Proceedings of 3rd International Electronic and Flipped Conference on Entropy and Its Applications , no. : 1.
Roberto Di Mari; Roberto Rocci; Stefano Antonio Gattone. Finite Mixture of Linear Regression Models: An Adaptive Constrained Approach to Maximum Likelihood Estimation. Advances in Intelligent Systems and Computing 2016, 181 -186.
AMA StyleRoberto Di Mari, Roberto Rocci, Stefano Antonio Gattone. Finite Mixture of Linear Regression Models: An Adaptive Constrained Approach to Maximum Likelihood Estimation. Advances in Intelligent Systems and Computing. 2016; ():181-186.
Chicago/Turabian StyleRoberto Di Mari; Roberto Rocci; Stefano Antonio Gattone. 2016. "Finite Mixture of Linear Regression Models: An Adaptive Constrained Approach to Maximum Likelihood Estimation." Advances in Intelligent Systems and Computing , no. : 181-186.
Adaptive cluster sampling (ACS) has received much attention in recent years since it yields more precise estimates than conventional sampling designs when applied to rare and clustered populations. These results, however, are impacted by the availability of some prior knowledge about the spatial distribution and the absolute abundance of the population under study. This prior information helps the researcher to select a suitable critical value that triggers the adaptive search, the neighborhood definition and the initial sample size. A bad setting of the ACS design would worsen the performance of the adaptive estimators. In particular, one of the greatest weaknesses in ACS is the inability to control the final sampling effort if, for example, the critical value is set too low. To overcome this drawback one can introduce ACS with clusters selected without replacement where one can fix in advance the number of distinct clusters to be selected or ACS with a stopping rule which stops the adaptive sampling when a predetermined sample size limit is reached or when a given stopping rule is verified. However, the stopping rule breaks down the theoretical basis for the unbiasedness of the ACS estimators introducing an unknown amount of bias in the estimates. The current study improves the performance of ACS when applied to patchy and clustered but not rare populations and/or less clustered populations. This is done by combining the stopping rule with ACS without replacement of clusters so as to further limit the sampling effort in form of traveling expenses by avoiding repeat observations and by reducing the final sample size. The performance of the proposed design is investigated using simulated and real data.
Stefano Antonio Gattone; Esha Mohamed; Tonio Di Battista. Adaptive cluster sampling with clusters selected without replacement and stopping rule. Environmental and Ecological Statistics 2016, 23, 453 -468.
AMA StyleStefano Antonio Gattone, Esha Mohamed, Tonio Di Battista. Adaptive cluster sampling with clusters selected without replacement and stopping rule. Environmental and Ecological Statistics. 2016; 23 (3):453-468.
Chicago/Turabian StyleStefano Antonio Gattone; Esha Mohamed; Tonio Di Battista. 2016. "Adaptive cluster sampling with clusters selected without replacement and stopping rule." Environmental and Ecological Statistics 23, no. 3: 453-468.