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In this paper, we propose a novel approach to improving forecasts of stock market indexes by considering common stock prices as hierarchical time series, combining clustering with forecast reconciliation. We propose grouping the individual stock price series in various ways including via metadata and using unsupervised learning techniques. The proposed approach is applied to the Dow Jones Industrial Average Index and the Standard & Poor 500 Index and their component stocks, and the results obtained with different grouping approaches are compared. The results empirically demonstrate that the combined use of clustering and reconciliation improves the forecast accuracy of the stock market indexes and their constituents. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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]

This paper deals with the challenging themes of the way sporting teams and athletes are ranked in sports competitions. Starting from the paradigmatic case of soccer, we advance a new method for ranking teams in the official national championships through computational statistics methods based on Kendall correlations and radar charts. In detail, we consider the goals for and against the teams in the individual matches as a further source of score assignment beyond the usual win-tie-lose trichotomy. Our approach overcomes some biases in the scoring rules that are currently employed. The methodological proposal is tested over the relevant case of the Italian “Serie A” championships played during 1930–2023. [ABSTRACT FROM AUTHOR]

Selecting an appropriate statistical model to forecast exchange rates is still today a relevant issue for policymakers and central bankers. The socalled 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. [ABSTRACT FROM AUTHOR]

We introduced a new method to compute the European Call (and Put) Option price under the assumption of multifractional Brownian motion (mBm). The reason why we need a procedure for estimating the Option price is due to the absence of a closed formula for this process. To compute the Option price, we first simulated the logarithmic price under mBm and, by using a discount factor, we computed the option's pay-off. Then, we fitted the best probability distribution associated to the discounted pay-off, computing the European Call Option price as its average. [ABSTRACT FROM AUTHOR]

Autoencoders are dimension reduction models in the field of machine learning which can be thought of as a neural network counterpart of principal components analysis (PCA). Due to their flexibility and good performance, autoencoders have been recently used for estimating nonlinear factor models in finance. The main weakness of autoencoders is that the results are less explainable than those obtained with the PCA. In this paper, we propose the adoption of the Shapley value to improve the explainability of autoencoders in the context of nonlinear factor models. In particular, we measure the relevance of nonlinear latent factors using a forecast-based Shapley value approach that measures each latent factor's contributions in determining the out-of-sample accuracy in factor-augmented models. Considering the interesting empirical instance of the commodity market, we identify the most relevant latent factors for each commodity based on their out-of-sample forecasting ability. [ABSTRACT FROM AUTHOR]

The ongoing invasion of Russia of Ukraine and energy crises have sparked concern about economic and geopolitical stability all over the world. In Europe, the war has destabilized and endangered the energy cooperation and transition between European countries within and outside of the EU. This emergency has shown once more the importance of energy resilience policies to offset the vulnerability of energy systems and energy insecurity at the national and regional levels. Consilience has been reached on the necessity of enhancing EU energy security as an adaptation strategy. This work contributes to the existing scholarship on renewable energy transition and citizens' perception, focusing on European Union member states. Key characteristics of the renewable energy transition in the EU prior to the energy crisis and the war in Ukraine are examined. To this end, we analyze selected economic, environmental, social, policy and political variables on energy sorting from the Eurobarometer studying European citizens' perceptions. The exercise makes use of spatially-clustered regression to explore spatial heterogeneity and to elicit determinant information on specific regional groups. We learn that southern Europeans attribute less importance to energy infrastructure to facilitate the renewable energy transition and repute the EU solidity not a requirement for energy security access. Conversely, northern European citizens tend not to associate the responsibility of the EU in guiding competitiveness and policy toward green energy sources transformation. Robustness tests confirm our hypothesis. Regardless of regional differences, the EU energy and ecological transition will thrive with industrial and political cohesion. This process will pass through increased trust in institutions and dedicated energy policy action which will smooth the risks and disruptions coming from current and future shocks. • Eurobarometer variables European citizens' perceptions of renewable energy transition are analyzed. • Spatial heterogeneity statistics builds regional clusters southern Europeans ex-ante analysis of the Ukraine war and energy crisis. • Southern Europeans attribute less importance to energy infrastructure for renewable energy transition • Southern Europeans repute EU solidity not a requirement for energy security access. • Northern European citizens do not associate EU competitiveness with green energy sources transformation. [ABSTRACT FROM AUTHOR]

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. • We propose a Composite Indicator based on Spatial Principal Component Analysis considering the presence of spatial autocorrelation. • The application concerns 103 Italian Province capital cities and employs Urban BES considering 64 indicators belonging to 8 dimensions. • The addressed approach is useful for policymakers in the promotion of well-being for public policy. • Results contribute to a better understanding of spatial variations and disparities across Italian provinces. [ABSTRACT FROM AUTHOR]

Determining the spatial thickness (z) of in situ and reworked fallout pyroclastic deposits plays a key role in volcanological studies and in shedding light on geomorphological and hydrogeological processes in peri-volcanic areas. 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 and/or denudation processes after ash deposition on the ground surface affect the expected spatial distribution of these deposits. This article tries to bridge this knowledge gap by applying statistical techniques for making representative spatial thickness predictions to be used for the analysis of geomorphic processes at the catchment and sub-catchment scales. 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 the territories of several municipalities in Campania, southern Italy. Second, 18 predictor variables were derived mainly from digital elevation models and satellite images and were 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 the prediction accuracy of RF (RMSE <82.46 and MAE <48.36) is better than that of existing models in the literature. Moreover, statistical combination of the predictions obtained from the above-mentioned models through a 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 change significantly, but the estimations by LAD are smaller. This combined approach can help in estimating the thickness of fallout pyroclastic deposits in other volcanic regions and in managing geohazards in areas covered with loose pyroclastic materials. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to fuzzy clustering of time series based on the dissimilarity among conditional higher moments. A system of weights accounts for the relevance of each conditional moment in defining the clusters. Robustness against outliers is also considered by extending the above clustering method using a suitable exponential transformation of the distance measure defined on the conditional higher moments. To show the usefulness of the proposed approach, we provide a study with simulated data and an empirical application to the time series of stocks included in the FTSEMIB 30 Index. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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]

This paper treats a well-established public evaluation problem, which is the analysis of the funded research projects. We specifically deal with the collection of the research actions funded by the European Union over the 7th Framework Programme for Research and Technological Development and Horizon 2020. The reference period is 2007–2020. The study is developed through three methodological steps. First, we consider the networked scientific institutions by stating a link between two organizations when they are partners in the same funded project. In doing so, we build yearly complex networks. We compute four nodal centrality measures with relevant, informative content for each of them. Second, we implement a rank-size procedure on each network and each centrality measure by testing four meaningful classes of parametric curves to fit the ranked data. At the end of such a step, we derive the best fit curve and the calibrated parameters. Third, we perform a clustering procedure based on the best-fit curves of the ranked data for identifying regularities and deviations among years of research and scientific institutions. The joint employment of the three methodological approaches allows a clear view of the research activity in Europe in recent years. [ABSTRACT FROM AUTHOR]

(1) Background: SARS-CoV-2 infection is notably mild in children, though comorbidities may increase the risk of hospitalization and may represent a risk for increased disease severity. There is an urgent need for targeted therapies with an acceptable efficacy and safety profile. To date, most of the medicines for COVID-19-specific treatment are prescribed off-label for children due to a lack of clinical trials and consequent evidence in this population. (2) Methods: This was a retrospective, observational study investigating the safety of treatments for the prevention of severe COVID-19 in fragile pediatric patients who received monoclonal antibodies and antivirals for mild-to-moderate symptoms between December 2021 and July 2022. (3) Results: Thirty-two patients were included. Monoclonal antibodies were prescribed to 62%, intravenous antivirals to 22%, and oral antivirals to 16% of children. Sotrovimab was the most frequently prescribed drug among monoclonal antibodies and overall (59%). The second most prescribed drug was remdesivir (22%). No severe adverse drug reaction was reported. There was no progression to severe disease and no death cases due to COVID-19 or drug administration. At drug-type stratification, resolution of symptoms and swab positivity time showed no difference between the two groups at 7 and 28 days. Off-label prescriptions were 84% overall, and in similar proportions between the two groups. (4) Conclusions: in this small sample, antivirals seemed safe and showed no differences in efficacy as compared to MAbs for the early treatment of COVID-19 in fragile children, thus representing a valuable choice, even when administered off-label. [ABSTRACT FROM AUTHOR]

In this paper we propose a framework for fuzzy clustering of time series based on directional volatility spillovers. In the case of financial time series, detecting clusters of volatility spillovers provides insights into the market structure, which can be useful to both portfolio managers and policy makers. We measure directional—i.e. “From” and “To” the others—volatility spillovers with a methodology based on the generalized forecast-error variance decomposition. Then, we propose a weighted fuzzy clustering model for grouping stocks with a similar degree of directional spillovers. By using a weighted approach, we allow the algorithm to decide which dimension of spillover is more relevant for clustering. Moreover, a robust clustering model is also proposed to alleviate the effect of possible outlier stocks. We apply the proposed clustering model for the analysis of spillover effects in the Italian stock market. [ABSTRACT FROM AUTHOR]

In recent years, the research of statistical methods to analyze complex structures of data has increased. In particular, a lot of attention has been focused on the interval-valued data. In a classical cluster analysis framework, an interesting line of research has focused on the clustering of interval-valued data based on fuzzy approaches. Following the partitioning around medoids fuzzy approach research line, a new fuzzy clustering model for interval-valued data is suggested. In particular, we propose a new model based on the use of the entropy as a regularization function in the fuzzy clustering criterion. The model uses a robust weighted dissimilarity measure to smooth noisy data and weigh the center and radius components of the interval-valued data, respectively. To show the good performances of the proposed clustering model, we provide a simulation study and an application to the clustering of scientific journals in research evaluation. [ABSTRACT FROM AUTHOR]

Time series data are commonly clustered based on their distributional characteristics. The moments play a central role among such characteristics because of their relevant informative content. This paper aims to develop a novel approach that faces still open issues in moment-based clustering. First of all, we deal with a very general framework of time-varying moments rather than static quantities. Second, we include in the clustering model high-order moments. Third, we avoid implicit equal weighting of the considered moments by developing a clustering procedure that objectively computes the optimal weight for each moment. As a result, following a fuzzy approach, two weighted clustering models based on both unconditional and conditional moments are proposed. Since the Dynamic Conditional Score model is used to estimate both conditional and unconditional moments, the resulting framework is called weighted score-driven clustering. We apply the proposed method to financial time series as an empirical experiment. • The paper proposes a new method for time series clustering. • The procedure is based on an optimal combination of conditional and unconditional moments. • The clustering procedure includes fuzziness. • The clustering method is applied to financial time series. [ABSTRACT FROM AUTHOR]