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1. Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe

Author

Cerqueti, Roy, Maranzano, Paolo, and Mattera, Raffaele

Subjects
Statistics - Methodology, Statistics - Applications, Statistics - Computation
Abstract

In this paper, we present an extension of the spatially-clustered linear regression models, namely, the spatially-clustered spatial autoregression (SCSAR) model, to deal with spatial heterogeneity issues in clustering procedures. In particular, we extend classical spatial econometrics models, such as the spatial autoregressive model, the spatial error model, and the spatially-lagged model, by allowing the regression coefficients to be spatially varying according to a cluster-wise structure. Cluster memberships and regression coefficients are jointly estimated through a penalized maximum likelihood algorithm which encourages neighboring units to belong to the same spatial cluster with shared regression coefficients. Motivated by the increase of observed values of the Gini index for the agricultural production in Europe between 2010 and 2020, the proposed methodology is employed to assess the presence of local spatial spillovers on the market concentration index for the European regions in the last decade. Empirical findings support the hypothesis of fragmentation of the European agricultural market, as the regions can be well represented by a clustering structure partitioning the continent into three-groups, roughly approximated by a division among Western, North Central and Southeastern regions. Also, we detect heterogeneous local effects induced by the selected explanatory variables on the regional market concentration. In particular, we find that variables associated with social, territorial and economic relevance of the agricultural sector seem to act differently throughout the spatial dimension, across the clusters and with respect to the pooled model, and temporal dimension.

Published
2024

6. Network log-ARCH models for forecasting stock market volatility

Author

Mattera, Raffaele and Otto, Philipp

Subjects
Statistics - Applications, Economics - Econometrics, Quantitative Finance - Statistical Finance
Abstract

This paper presents a novel dynamic network autoregressive conditional heteroscedasticity (ARCH) model based on spatiotemporal ARCH models to forecast volatility in the US stock market. To improve the forecasting accuracy, the model integrates temporally lagged volatility information and information from adjacent nodes, which may instantaneously spill across the entire network. The model is also suitable for high-dimensional cases where multivariate ARCH models are typically no longer applicable. We adopt the theoretical foundations from spatiotemporal statistics and transfer the dynamic ARCH model for processes to networks. This new approach is compared with independent univariate log-ARCH models. We could quantify the improvements due to the instantaneous network ARCH effects, which are studied for the first time in this paper. The edges are determined based on various distance and correlation measures between the time series. The performances of the alternative networks' definitions are compared in terms of out-of-sample accuracy. Furthermore, we consider ensemble forecasts based on different network definitions.

Published
2023

9. Corrigendum: The new Italian registry of infantile thrombosis (RITI): a reflection on its journey, challenges and pitfalls

Author

Maria Federica Pelizza, Matteo Martinato, Anna Rosati, Margherita Nosadini, Paola Saracco, Paola Giordano, Matteo Luciani, Laura Ilardi, Donatella Lasagni, Angelo Claudio Molinari, Rossana Bagna, Antonella Palmieri, Luca Antonio Ramenghi, Massimo Grassi, Mariella Magarotto, Federica Magnetti, Andrea Francavilla, Giuseppe Indolfi, Agnese Suppiej, Chiara Gentilomo, Roberta Restelli, Antonella Tufano, Daniela Tormene, Jacopo Norberto Pin, Clarissa Tona, Davide Meneghesso, Lidia Rota, Marta Conti, Giovanna Russo, Giulia Lorenzoni, Dario Gregori, Stefano Sartori, Paolo Simioni, Collaborators of the R.I.T.I. (Italian and Registry of Infantile Thrombosis), Accorsi Patrizia, Aceto Gabriella, Agnoletti Gabriella, Agostini Manuela, Alfarano Angela, Altieri Elena, Amador Carolina, Antonelli Camilla, Arena Vittoria, Asta Francesca, Baggio Laura, Ballardini Elisa, Baracetti Margherita, Baraldi Eugenio, Barberis Laura, Barisone Elena, Basso Anne Letizia, Battajon Nadia, Bersani Iliana, Biddeci Giada, Biffanti Roberta, Bonardi Claudia Maria, Bonaudo Roberto, Boniver Clementina, Boscarol Gianluca, Bottino Roberto, Bravar Giulia, Brizzi Ilaria, Brolatti Noemi, Braguglia Annabella, Guaragni Brunetta, Bugin Samuela, Calvo Pier Luigi, Capasso Antonella, Capodiferro Donatella, Cappelleri Alessia, Cascarano Maria Teresa, Casellato Susanna, Casini Tommaso, Catarzi Serena, Cavaliere Elena, Cavicchiolo Maria Elena, Celestino Silvia, Celle Maria Elena, Centonze Nicola, Cerutti Alessia, Chakrokh Roksana, Offer Chiara, Chiodin Elisabetta, Chirico Gaetano, Chukhlantseva Natalia, Cifarelli Paola, Cinelli Giulia, Coinu Marisa, Colonna Clara, Comito Donatella, Corato Alessandra, Cordelli Duccio Maria, Crichiutti Giovanni, Cursio Ida, Dagri Arianna, De Maria Beatrice, Del Borrello Giovanni, Di Rienzo Francesca, Doglioni Nicoletta, Dolcemascolo Valentina, Dotta Andrea, Drigo Paola, Drimaco Pietro, Ellero Serena, Falcone Alessandra, Fantauzzi Ambra, Farinasso Daniela, Ferilli Michela, Festa Silvia, Fischer Maximilian, Foiadelli Thomas, Fotzi Ilaria, Francavilla Rosa, Freschi Paola, Gaffuri Marcella, Gallo Elena, Gamalero Lisa, Gandioli Claudia, Garuccio Sergio, Gentile Diletta, Ghionzoli Marco, Giliberti Paola, Greco Filippo, Guariento Chiara, Guidotti Isotta, Iodice Alessandro, Janes Augusta, Laghi Elena, Lampugnani Elisabetta, Lassandro Giuseppe, Laverda Anna Maria, Lazzerotti Alessandra, Lo Tartaro Meragliotta Patrizia, Lombardini Martina, Lorenzon Eleonora, Mainini Nicoletta, Massoud Michela, Materia Valeria, Mattera Raffaele, Mauro Isabella, Melani Federico, Meli Mariaclaudia, Messina Giovanni, Monticone Sonia, Moras Marzia, Negro Ilaria, Olzai Giorgio, Pancani Simone, Pandolfi Maria, Passariello Annalisa, Passarini Alice, Passone Eva, Pastorino Myriam, Pegoraro Veronica, Pennoni Serena, Perilongo Giorgio, Pozzessere Anna, Pruna Dario, Pusiol Anna, Putti Maria Caterina, Rabbone Ivana, Radicioni Maurizio, Renna Salvatore, Ricci Maria Luisa, Rimini Alessandro, Rivellini Sara, Rustioni Gianluca, Salvadori Sabrina, Santoiemma Valentina, Santoro Nicola, Schiavulli Michele, Sebellin Sofia, Sesta Michela, Soffiati Massimo, Sorbo Monica, Spanedda Giuseppina, Stangalini Valeria, Stasolla Salvatore, Tanzi Giorgia, Testa Tiziana, Teutonico Federica, Timpani Giuseppina, Toldo Irene, Trapani Sandra, Vaccari Roberto, Vecchi Marilena, Vento Giovanni, Veraldi Daniele, Villa Giovanna, Visintin Gianluca, Zambelloni Cesare, and Zellini Francesco

Subjects
thrombosis, stroke, children, pediatric, registry, thromboembolism, Pediatrics, RJ1-570
Published
2024
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10. A mixed-frequency approach for exchange rates predictions

Author

Mattera, Raffaele, Misuraca, Michelangelo, Scepi, Germana, and Spano, Maria

Subjects
Statistics - Applications, 62J05
Abstract

Selecting an appropriate statistical model to forecast exchange rates is still today a relevant issue for policymakers and central bankers. The so-called Meese and Rogoff puzzle assesses that exchange rate fluctuations are unpredictable. In the literature, a lot of studies tried to solve the puzzle finding alternative predictors and statistical models based on temporal aggregation. In this paper, we propose an approach based on mixed frequency models to overcome the lack of information caused by temporal aggregation. We show the effectiveness of our approach in comparison with other proposed methods by performing CAD/USD exchange rate predictions.

Published
2021
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11. Model-based fuzzy time series clustering of conditional higher moments

Author

Cerqueti, Roy, Giacalone, Massimiliano, and Mattera, Raffaele

Subjects
Statistics - Methodology
Abstract

This paper develops a new time series clustering procedure allowing for heteroskedasticity, non-normality and model's non-linearity. At this aim, we follow a fuzzy approach. Specifically, considering a Dynamic Conditional Score (DCS) model, we propose to cluster time series according to their estimated conditional moments via the Autocorrelation-based fuzzy C-means (A-FCM) algorithm. The DCS parametric modelling is appealing because of its generality and computational feasibility. The usefulness of the proposed procedure is illustrated using an experiment with simulated data and several empirical applications with financial time series assuming both linear and nonlinear models' specification and under several assumptions about time series density function.

Published
2021

15. Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling

Author

Cerqueti, Roy, Giacalone, Massimiliano, and Mattera, Raffaele

Subjects
Quantitative Finance - Statistical Finance
Abstract

Recently, cryptocurrencies have attracted a growing interest from investors, practitioners and researchers. Nevertheless, few studies have focused on the predictability of them. In this paper we propose a new and comprehensive study about cryptocurrency market, evaluating the forecasting performance for three of the most important cryptocurrencies (Bitcoin, Ethereum and Litecoin) in terms of market capitalization. At this aim, we consider non-Gaussian GARCH volatility models, which form a class of stochastic recursive systems commonly adopted for financial predictions. Results show that the best specification and forecasting accuracy are achieved under the Skewed Generalized Error Distribution when Bitcoin/USD and Litecoin/USD exchange rates are considered, while the best performances are obtained for skewed Distribution in the case of Ethereum/USD exchange rate. The obtain findings state the effectiveness -- in terms of prediction performance -- of relaxing the normality assumption and considering skewed distributions., Comment: 54 pages, 7 figures, 34 tables. To be published in Information Sciences

Published
2020

19. The new Italian registry of infantile thrombosis (RITI): A reflection on its journey, challenges and pitfalls

Author

Maria Federica Pelizza, Matteo Martinato, Anna Rosati, Margherita Nosadini, Paola Saracco, Paola Giordano, Matteo Luciani, Laura Ilardi, Donatella Lasagni, Angelo Claudio Molinari, Rossana Bagna, Antonella Palmieri, Luca Antonio Ramenghi, Massimo Grassi, Mariella Magarotto, Federica Magnetti, Andrea Francavilla, Giuseppe Indolfi, Agnese Suppiej, Chiara Gentilomo, Roberta Restelli, Antonella Tufano, Daniela Tormene, Jacopo Norberto Pin, Clarissa Tona, Davide Meneghesso, Lidia Rota, Marta Conti, Giovanna Russo, Giulia Lorenzoni, Dario Gregori, Stefano Sartori, Paolo Simioni, Collaborators of the R.I.T.I. (Italian Registry of Infantile Thrombosis), Accorsi Patrizia, Aceto Gabriella, Agnoletti Gabriella, Agostini Manuela, Alfarano Angela, Altieri Elena, Amador Carolina, Antonelli Camilla, Arena Vittoria, Asta Francesca, Baggio Laura, Ballardini Elisa, Baracetti Margherita, Baraldi Eugenio, Barberis Laura, Barisone Elena, Basso Anne Letizia, Battajon Nadia, Bersani Iliana, Biddeci Giada, Biffanti Roberta, Bonardi Claudia Maria, Bonaudo Roberto, Boniver Clementina, Boscarol Gianluca, Bottino Roberto, Bravar Giulia, Brizzi Ilaria, Brolatti Noemi, Braguglia Annabella, Guaragni Brunetta, Bugin Samuela, Calvo Pier Luigi, Capasso Antonella, Capodiferro Donatella, Cappelleri Alessia, Cascarano Maria Teresa, Casellato Susanna, Casini Tommaso, Catarzi Serena, Cavaliere Elena, Cavicchiolo Maria Elena, Celestino Silvia, Celle Maria Elena, Centonze Nicola, Cerutti Alessia, Chakrokh Roksana, Offer Chiara, Chiodin Elisabetta, Chirico Gaetano, Chukhlantseva Natalia, Cifarelli Paola, Cinelli Giulia, Coinu Marisa, Colonna Clara, Comito Donatella, Corato Alessandra, Cordelli Duccio Maria, Crichiutti Giovanni, Cursio Ida, Dagri Arianna, De Maria Beatrice, Del Borrello Giovanni, Di Rienzo Francesca, Doglioni Nicoletta, Dolcemascolo Valentina, Dotta Andrea, Drigo Paola, Drimaco Pietro, Ellero Serena, Falcone Alessandra, Fantauzzi Ambra, Farinasso Daniela, Ferilli Michela, Festa Silvia, Fischer Maximilian, Foiadelli Thomas, Fotzi Ilaria, Francavilla Rosa, Freschi Paola, Gaffuri Marcella, Gallo Elena, Gamalero Lisa, Gandioli Claudia, Garuccio Sergio, Gentile Diletta, Ghionzoli Marco, Giliberti Paola, Greco Filippo, Guariento Chiara, Guidotti Isotta, Iodice Alessandro, Janes Augusta, Laghi Elena, Lampugnani Elisabetta, Lassandro Giuseppe, Laverda Anna Maria, Lazzerotti Alessandra, Lo Tartaro Meragliotta Patrizia, Lombardini Martina, Lorenzon Eleonora, Mainini Nicoletta, Massoud Michela, Materia Valeria, Mattera Raffaele, Mauro Isabella, Melani Federico, Meli Mariaclaudia, Messina Giovanni, Monticone Sonia, Moras Marzia, Negro Ilaria, Olzai Giorgio, Pancani Simone, Pandolfi Maria, Passariello Annalisa, Passarini Alice, Passone Eva, Pastorino Myriam, Pegoraro Veronica, Pennoni Serena, Perilongo Giorgio, Pozzessere Anna, Pruna Dario, Pusiol Anna, Putti Maria Caterina, Rabbone Ivana, Radicioni Maurizio, Renna Salvatore, Ricci Maria Luisa, Rimini Alessandro, Rivellini Sara, Rustioni Gianluca, Salvadori Sabrina, Santoiemma Valentina, Santoro Nicola, Schiavulli Michele, Sebellin Sofia, Sesta Michela, Soffiati Massimo, Sorbo Monica, Spanedda Giuseppina, Stangalini Valeria, Stasolla Salvatore, Tanzi Giorgia, Testa Tiziana, Teutonico Federica, Timpani Giuseppina, Toldo Irene, Trapani Sandra, Vaccari Roberto, Vecchi Marilena, Vento Giovanni, Veraldi Daniele, Villa Giovanna, Visintin Gianluca, Zambelloni Cesare, and Zellini Francesco

Subjects
thrombosis, stroke, children, pediatric, registry, thromboembolism, Pediatrics, RJ1-570
Abstract

IntroductionThrombotic events in neonates and children represent a rare although severe occurrence in view of the associated risk of mortality and sequelae. Quality evidence is limited in this field, and registry studies provide an essential base for research. The aim of this paper is to present the new Italian Registry of Infantile Thrombosis (RITI), set it into the scene of international thrombosis and stroke registries, and provide some insight on the challenges associated with registry management.MethodsWe present the detailed structure and content of the new RITI registry, a brief overview of its main data, and a reflection on its features, pitfalls and the main challenges related to its management.ResultsThe RITI, initially started in 2007 and officially re-launched in 2017 after structural modifications, is a non-interventional retrospective and prospective registry study collecting data on neonatal and pediatric patients (0–18 years) who experienced a systemic or cerebral thrombotic event in Italy. The RITI is managed by a multidisciplinary team with expertise in pediatric thrombosis, and participation is open to all Italian physicians, on a voluntary basis. The overall aim of the registry is to acquire new evidence to better characterize the population of children with thrombotic events and improve their management and outcome. 48 Italian pediatric and intensive care units are actively involved in the RITI, including 85 medical doctors from 16 Italian regions. A total of 1,001 neonates and children affected by cerebral or systemic thrombosis have been enrolled.DiscussionThe RITI is one of the largest available European registries of neonatal and pediatric thrombosis. National registries like the RITI represent a model for the study of rare conditions based on multidisciplinary and multicenter collaboration, aimed at overcoming the limitations due to small populations of patients, and creating a network of experts for patient referral and continuous education. Moreover, registry studies have a pivotal role in the research on pediatric thrombosis, due to the limited feasibility of high-quality studies. In our experience, the main critical stages, pitfalls and challenges in registry management include adequate registry designing, diffusion, data completeness and quality control.

Published
2023
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24. A field-based thickness measurement dataset of fallout pyroclastic deposits in the peri-volcanic areas of Campania region (Italy): Statistical combination of different predictions for spatial thickness estimation.

Author

Ebrahimi, Pooria, Matano, Fabio, Amato, Vincenzo, Mattera, Raffaele, and Scepi, Germana

Subjects
THICKNESS measurement, INDEPENDENT variables, EXPLOSIVE volcanic eruptions, VOLCANIC eruptions, DIGITAL elevation models, REMOTE-sensing images, RANDOM forest algorithms, MACHINE theory
Abstract

Determining spatial thickness (z) of fallout pyroclastic deposits plays a key role in volcanological studies and shedding light on geomorphological and hydrogeological processes. However, this is a challenging line of research because: (1) field-based measurements are expensive and time-consuming; (2) the ash might have been dispersed in the atmosphere by several volcanic eruptions; and (3) wind characteristics during an eruptive event and soil-forming/denudation processes after ash deposition on the ground surface affect the expected spatial distribution of the fallout pyroclastic deposits. This article tries to bridge this knowledge gap by applying statistical techniques for making representative predictions. First, we compiled a field-based thickness measurement dataset (https://doi.org/10.5281/zenodo.8399487 ; Matano et al., 2023) of fallout pyroclastic deposits in several municipalities of Campania region, southern Italy. Second, 18 predictor variables were derived mainly from digital elevation models and satellite imageries and assigned to each measurement point. Third, the stepwise regression (STPW) model and random forest (RF) machine learning technique are used for thickness modeling. Fourth, the estimations are compared with those of three models that already exist in the literature. Finally, the statistical combination of different predictions is implemented to develop a less biased model for estimating pyroclastic thickness. The results show that prediction accuracy of RF (RMSE < 82.46 and MAE < 48.36) is better than that existing literature models. Moreover, statistical combination of the predictions obtained from the above-mentioned models through Least Absolute Deviation (LAD) combination approach leads to the most representative thickness estimation (MAE < 45.12) in the study area. The maps for the values estimated by RF and LAD (as the best single model and combination approach, respectively) illustrate that the spatial patterns did not alter significantly, but the estimations by LAD are smaller. This combined approach can help in estimating thickness of fallout pyroclastic deposits in other volcanic regions and in managing geohazards in areas covered with loose pyroclastic materials. [ABSTRACT FROM AUTHOR]

Published
2024
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25. P374: THROMBOTIC EVENTS IN CHILDREN WITH ACUTE LYMPHOBLASTIC LEUKEMIA: EXPERIENCE OF ITALIAN AIEOP CENTERS (2009-2017)

Author

Mattera, Raffaele, primary, Caterina Putti, Maria, additional, Biffi, Alessandra, additional, Parasole, Rosanna, additional, Conter, Valentino, additional, Rizzari, Carmelo, additional, Grazia Valsecchi, Maria, additional, Silvestri, Daniela, additional, Biondi, Andrea, additional, Locatelli, Franco, additional, Barisone, Elena, additional, Micalizzi, Concetta, additional, Mina, Tommaso, additional, and Kiren, Valentina, additional

Published
2023
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26. Early Treatments of Fragile Children with COVID-19—Results of CLEVER (Children COVID Early Treatment), a Retrospective, Observational Study

Author

Minotti, Chiara, primary, Mengato, Daniele, additional, De Pieri, Marica, additional, Trivellato, Sabrina, additional, Francavilla, Andrea, additional, Di Chiara, Costanza, additional, Liberati, Cecilia, additional, Mattera, Raffaele, additional, Biffi, Alessandra, additional, Giaquinto, Carlo, additional, Venturini, Francesca, additional, and Donà, Daniele, additional

Published
2023
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28. Measuring conditional correlation between financial markets' inefficiency.

Author

Sciorio, Fabrizio Di, Mattera, Raffaele, and Segovia, Juan Evangelista Trinidad

Subjects
FINANCIAL markets, FINANCIAL crises, BROWNIAN motion, GRANGER causality test, EFFICIENT market theory, STOCK prices
Abstract

Assuming that stock prices follow a multi-fractional Brownian motion, we estimated a time-varying Hurst exponent ( h t ). The Hurst value can be considered a relative volatility measure and has been recently used to estimate market inefficiency. Therefore, the Hurst exponent offers a level of comparison between theoretical and empirical market efficiency. Starting from this point of view, we adopted a multivariate conditional heteroskedastic approach for modeling inefficiency dynamics in various financial markets during the 2007 financial crisis, the COVID-19 pandemic and the Russo-Ukranian war. To empirically validate the analysis, we compared different stock markets in terms of conditional and unconditional correlations of dynamic inefficiency and investigated the predicted power of inefficiency measures through the Granger causality test. [ABSTRACT FROM AUTHOR]

Published
2023
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31. Forecasting binary outcomes in soccer.

Author

Mattera, Raffaele

Subjects
*SOCCER tournaments, *FORECASTING, *SOCCER, *SPORTS statistics, *BINOMIAL distribution, *BINARY sequences, *SOCCER fans, *FUTUROLOGISTS
Abstract

Several studies deal with the development of advanced statistical methods for predicting football match results. These predictions are then used to construct profitable betting strategies. Even if the most popular bets are based on whether one expects that a team will win, lose, or draw in the next game, nowadays a variety of other outcomes are available for betting purposes. While some of these events are binary in nature (e.g. the red cards occurrence), others can be seen as binary outcomes. In this paper we propose a simple framework, based on score-driven models, able to obtain accurate forecasts for binary outcomes in soccer matches. To show the usefulness of the proposed statistical approach, two experiments to the English Premier League and to the Italian Serie A are provided for predicting red cards occurrence, Under/Over and Goal/No Goal events. [ABSTRACT FROM AUTHOR]

Published
2023
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32. Essays in statistical methods for asset allocation

Author

Mattera, Raffaele and Mattera, Raffaele

Abstract

The present work, divided into three main chapters, discusses the development and the application of novel statistical techniques for portfolio selection problems. The first chapter is devoted to the estimation theory, and a new estimator for the precision matrix, called precision shrinkage, is developed to reduce the estimation error. The analysis provided in the chapter show that the use of precision shrinkage lead to the construction of more desirable portfolios in terms of return/risk trade-off with respect to well established alternatives. The second chapter studies the ability of forecasting techniques in constructing more attractive portfolios than strategies based on static estimation. Classical model-based econometric methods are compared with data-driven machine learning ones. We find that, for both low and large dimensions, the use of forecasts improves the out-of-sample portfolio performances if model-based approaches are employed. The last chapter discusses the usefulness of clustering in portfolio selection. Clustering can be used to reduce the asset allocation dimensionality. Several algorithms are compared in terms of out-of-sample profitability. As a main result, we show that clustering-based portfolios dominate the classical approaches.

Published
2022

34. A mixed-frequency approach for exchange rates predictions

Author

Mattera Raffaele, Misuraca Michelangelo, Scepi Germana, Spano Maria, Mattera, Raffaele, Misuraca, Michelangelo, Scepi, Germana, and Spano, Maria

Subjects
FOS: Computer and information sciences, forecasting, frequency alignment, linear regression, MIDAS, 62J05, Applications (stat.AP), Statistics - Applications
Abstract

Selecting an appropriate statistical model to forecast exchange rates is still today a relevant issue for policymakers and central bankers. The so-called Meese and Rogoff puzzle assesses that exchange rate fluctuations are unpredictable. In the literature, a lot of studies tried to solve the puzzle finding both alternative predictors (e.g., interest rates, price levels) and statistical models based on temporal aggregation. In this paper, we propose an approach based on mixed frequency models to overcome the lack of information caused by temporal aggregation. We show the effectiveness of our approach with an application to CAD/USD exchange rate predictions.

Published
2021
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36. Distribution-based entropy weighting clustering of skewed and heavy tailed time series

Author

Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa, Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic, Mattera, Raffaele, Giacalone, Massimiliano, Gibert, Karina, Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa, Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic, Mattera, Raffaele, Giacalone, Massimiliano, and Gibert, Karina

Abstract

The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate properties, such as heavy tails and skewness. Thanks to its flexibility, the skewed exponential power distribution (also called skewed generalized error distribution) ensures a unified and general framework for clustering possibly skewed and heavy tailed time series. This paper develops a clustering procedure of model-based type, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine the estimated parameters to form the clusters with an entropy weighing k-means approach. The usefulness of the proposal is shown by means of application to financial time series, demonstrating also how the obtained clusters can be used to form portfolio of stocks., Peer Reviewed, Postprint (published version)

Published
2021

39. Mixed frequency composite indicators for measuring public sentiment in the EU

Author

Raffaele Mattera, Michelangelo Misuraca, Maria Spano, Germana Scepi, Mattera, Raffaele, Misuraca, Michelangelo, Spano, Maria, and Scepi, Germana

Subjects
Statistics and Probability, Confdence indicator, Confidence indicator, Composite indicator, Public opinion, Media sentiment, Mixed data sampling, Media sentiment, Confdence indicator, Composite indicator, Public opinion, Media sentiment, Mixed data sampling, General Social Sciences, Composite indicator, Public opinion, Mixed data sampling
Abstract

Monitoring the state of the economy in a short time is a crucial aspect for designing appropriate and timely policy responses in the presence of shocks and crises. Short-term confidence indicators can help policymakers in evaluating both the effect of policies and the economic activity condition. The indicator commonly used in the EU to evaluate the public opinion orientation is the Economic Sentiment Indicator (ESI). Nevertheless, the ESI shows some drawbacks, particularly in the adopted weighting scheme that is static and not country-specific. This paper proposes an approach to construct novel composite confidence indicators, focusing on both the weights and the information set to use. We evaluate these indicators by studying their response to the policies introduced to contain the COVID-19 pandemic in some selected EU countries. Furthermore, we carry out an experimental study where the proposed indicators are used to forecast economic activity.

Published
2022

40. Multiway clustering with time-varying parameters

Author

Roy Cerqueti, Raffaele Mattera, Germana Scepi, Cerqueti, Roy, Mattera, Raffaele, and Scepi, Germana

Subjects
Statistics and Probability, Computational Mathematics, Generalized Autoregressive Score, Generalized Autoregressive Score · Dynamic Conditional Score · time- varying parameters · Time series clustering · Multiway data · Air quality, time-varying parameters, Air quality, Time series clustering, Dynamic Conditional Score, Statistics, Probability and Uncertainty, Generalized Autoregressive Score, Dynamic Conditional Score, time-varying parameters, Time series clustering, Multiway data, Air quality, Multiway data
Abstract

This paper proposes a clustering approach for multivariate time series with time-varying parameters in a multiway framework. Although clustering techniques based on time series distribution characteristics have been extensively studied, methods based on time-varying parameters have only recently been explored and are missing for multivariate time series. This paper fills the gap by proposing a multiway approach for distribution-based clustering of multivariate time series. To show the validity of the proposed clustering procedure, we provide both a simulation study and an application to real air quality time series data.

Published
2022

41. A Bibliometric Analysis on Agent-Based Models in Finance: Identification of Community Clusters and Future Research Trends

Author

Juan E. Trinidad Segovia, Fabrizio Di Sciorio, Raffaele Mattera, Maria Spano, Trinidad Segovia, Juan E., Di Sciorio, Fabrizio, Mattera, Raffaele, and Spano, Maria

Subjects
Multidisciplinary, econophysics, General Computer Science, financial economics, bibliometrics, agent-based models
Abstract

Agent-based models are computational approaches used to reproduce the interactions between economic agents. These models are widely applied in many contexts to get deeper understanding about agents’ behaviors within complex systems. In this paper, we provide a bibliometric analysis about agent-based models in finance and, considering bibliographic coupling, we identify the presence of two distinct clusters of research communities, i.e., financial economics and econophysics. Cluster-specific thematic analyses are conducted to understand if the two communities are characterized by different emerging and motor topics. By highlighting several differences in the clusters, we also show the two research communities specialized in different specific topics.

Published
2022

42. Well-being analysis of Italian provinces with spatial principal components

Author

Massimiliano Giacalone, Raffaele Mattera, Eugenia Nissi, Giacalone, Massimiliano, Mattera, Raffaele, and Nissi, Eugenia

Subjects
Economics and Econometrics, BES. Urban area, BES, Strategy and Management, Geography, Planning and Development, Composite indicators, Spatial analysis, Management Science and Operations Research, Composite indicators. Spatial principal component analysis, BES. Urban areas, Spatial analysis, Territorial disparities, Composite indicators. Spatial principal component analysi, Spatial analysi, Territorial disparities, Spatial principal component analysis, Urban areas, Statistics, Probability and Uncertainty
Abstract

Well-being is a multidimensional concept that cannot be described using a single indicator. By the synthesis of different dimensions it is possible to obtain composite indicators (CIs). Principal Components Analysis (PCA) is one of the most popular multivariate statistical techniques used building CIs. However, the fact that PCA does not take into account the spatial dimension of the phenomenon makes it unsuitable for studying the well-being of urban areas. In this paper, we propose to use Spatial Principal Component Analysis (SPCA) for measuring well-being. The SPCA technique provides principal component scores that summarize both the spatial variability and the spatial autocorrelation structure among the statistical units. In this paper, the SPCA is used to construct well-being CIs for the Italian provinces.

Published
2022

43. Distribution-based entropy weighting clustering of skewed and heavy tailed time series

Author

Raffaele Mattera, Karina Gibert, Massimiliano Giacalone, Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa, Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic, Mattera, Raffaele, Giacalone, Massimiliano, Karina, Gibert, Raffaele, Mattera, and Gibert, Karina

Subjects
Classification, Financial time series, Generalized error distribution, Portfolio selection, Skewed exponential power distribution, Skewness, Physics and Astronomy (miscellaneous), Anàlisi estocàstica, General Mathematics, skewness, financial time series, financial time serie, 02 engineering and technology, Operations research, Investigació operativa, 01 natural sciences, Matemàtiques i estadística::Investigació operativa [Àrees temàtiques de la UPC], 010104 statistics & probability, 90 Operations research, mathematical programming::90B Operations research and management science [Classificació AMS], 0202 electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), QA1-939, Entropy (information theory), Statistical physics, 0101 mathematics, Cluster analysis, generalized error distribution, Generalized normal distribution, Mathematics, skewed exponential power distribution, skewne, Series (mathematics), Stochastic analysis, 60 Probability theory and stochastic processes::60G Stochastic processes [Classificació AMS], Weighting, Exponential function, classification, Chemistry (miscellaneous), Matemàtiques i estadística::Estadística matemàtica [Àrees temàtiques de la UPC], Probability distribution, portfolio selection, 020201 artificial intelligence & image processing
Abstract

The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate properties, such as heavy tails and skewness. Thanks to its flexibility, the skewed exponential power distribution (also called skewed generalized error distribution) ensures a unified and general framework for clustering possibly skewed and heavy tailed time series. This paper develops a clustering procedure of model-based type, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine the estimated parameters to form the clusters with an entropy weighing k-means approach. The usefulness of the proposal is shown by means of application to financial time series, demonstrating also how the obtained clusters can be used to form portfolio of stocks.

Published
2021
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