A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: an Application to High-Frequency Covariance Dynamics

The analysis of the intraday dynamics of correlations among high-frequency returns is challenging due to the presence of asynchronous trading and market microstructure noise. Both effects may lead to significant data reduction and may severely underestimate correlations if traditional methods for low-frequency data are employed. We propose to model intraday log-prices through a multivariate local-level model with score-driven covariance matrices and to treat asynchronicity as a missing value problem. The main advantages of this approach are: (i) all available data are used when filtering correlations, (ii) market microstructure noise is taken into account, (iii) estimation is performed through standard maximum likelihood methods. Our empirical analysis, performed on 1-second NYSE data, shows that opening hours are dominated by idiosyncratic risk and that a market factor progressively emerges in the second part of the day. The method can be used as a nowcasting tool for high-frequency data, allowing to study the real-time response of covariances to macro-news announcements and to build intraday portfolios with very short optimization horizons.Comment: 30 pages, 10 figures, 7 table

Filtering and Smoothing with Score-Driven Models

We propose a methodology for filtering, smoothing and assessing parameter and filtering uncertainty in misspecified score-driven models. Our technique is based on a general representation of the well-known Kalman filter and smoother recursions for linear Gaussian models in terms of the score of the conditional log-likelihood. We prove that, when data are generated by a nonlinear non-Gaussian state-space model, the proposed methodology results from a first-order expansion of the true observation density around the optimal filter. The error made by such approximation is assessed analytically. As shown in extensive Monte Carlo analyses, our methodology performs very similarly to exact simulation-based methods, while remaining computationally extremely simple. We illustrate empirically the advantages in employing score-driven models as misspecified filters rather than purely predictive processes.Comment: 33 pages, 5 figures, 6 table

La microfinanza in Europa

So far, the lack of consistent and reliable data on microfinance in Europe prevented the conduct a comprehensive analysis of the performance of Microfinance institutions (MFIs), especially their capacity to meet policy expectation in terms of social objectives and financial sustainability in the long term. We constructed an original longitudinal dataset of 357 European MFIs observed between 2006 and 2015, based on raw data from the European Microfinance Network (EMN) overview survey. We use this data to carry out the first in-depth analysis of trends in European microfinance and the interaction between social and financial performance during the period 2006-2013. It emerges that microfinance in Europe is a highly segmented and heterogeneous industry, both in terms of providers’ institutional characteristics and regional business models

Microcredito al femminile, ma non in Italia

Dalla Grameen Bank in poi, il microcredito è uno strumento innovativo che nel mondo beneficia soprattutto le donne, altrimenti penalizzate dall'esclusione finanziaria. Non è così in Italia, dove le "microimprenditrici" ricevono meno di un terzo dei prestiti totali in questo settore

“La destra populista in Europa: una prospettiva economica”

L’analisi economica dei fattori alla base della domanda elettorale di populismo è ancora a uno stadio iniziale, nonostante i significativi progressi determinati da un numero crescente di studi. In questo lavoro ci proponiamo di fornire una prima rassegna dei contributi chiave forniti dalla letteratura economica sul populismo e di analizzare, tramite una analisi multidimensionale, le principali caratteristiche del profilo dell’elettore populista, alla luce delle opinioni pubbliche fornite dall’indagine Eurobarometro nel 2017.The economic analysis of the factors underlying the electoral demand for populism is still at an early stage, despite the significant progress made by a growing number of studies. In this paper we aim to provide a first overview of the key contributions by the economic literature on populism, and to analyze, through a multidimensional analysis, the main characteristics of the populist voter profile, in light of the public opinions provided by the Eurobarometer survey in 2017

Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interfaces

We propose a fusion approach that combines features from simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of non-invasive BCIs

Il sindaco rosso. Valenzi e il futuro di Napoli

[Italiano]:Maurizio Valenzi, primo sindaco comunista nella storia di Napoli, governò la città per 2.900 giorni, dal 1975 al 1983, senza mai avere la maggioranza in Consiglio comunale e tra enormi problemi, dalla disoccupazione alle emergenze del terremoto, della criminalità e del terrorismo. Nel periodo in cui l'Europa era divisa in due blocchi prima della caduta del Muro di Berlino, il Partito comunista acquisì in Italia consensi tali da portarlo alla guida delle maggiori città, compresa Napoli, la città già monarchica e laurina che appena pochi anni prima era stata ritenuta la "capitale morale della Destra". Furono anni di forte partecipazione politica e impegno sociale che cambiarono profondamente il volto della città e disegnarono il suo futuro, con scelte urbanistiche decisive e iniziative come Estate a Napoli e Scuola aperta, frutto di visione culturale e massima cura nel trattare la scuola e l'istruzione, collanti in grado di pervadere e tenere coesa l'intera comunità cittadina. Tre giornalisti, testimoni di quel periodo, raccontano ognuno da un'angolazione diversa gli eventi e i protagonisti, ricostruendo fatti, episodi e aneddoti relativi al "Sindaco Rosso" che fu definito dal Presidente Emerito della Repubblica italiana Giorgio Napolitano "un uomo che ha dato, col suo lungo impegno nelle condizioni più diverse e difficili, esempio di nobiltà della politica"./[English]:Maurizio Valenzi, the first communist mayor in the history of Naples, ruled the city for 2.900 days, from 1975 to 1983. He never achieved the majority in the City council and among huge issues, from unemployment to earthquake, crime and terrorism. In the days of the world divided into two blocs before the fall of the Berlin Wall, the Communist Party acquired so much popularity in Italy that it took the guide of the major cities, including Naples, already monarchic and “laurina” that just a few years before had been considered as the "moral capital of the political Right". These were years of active political participation and social commitment that deeply changed the face of the city and shaped its future, with very important urban planning choices and initiatives such as Summer in Naples and Open School, the result of cultural vision and strong attention for school and education, a glue able to pervading and keeping the whole city community cohesive. Three journalists, witnesses of that period, illustrate the events and protagonists from different points of view, reconstructing events, episodes and anecdotes about the "Red Mayor", who was defined by the former President of the Republic Giorgio Napolitano "a man who has given, with his long commitment in the most difficult conditions, an example of the nobility of politics"

Network-based brain computer interfaces: principles and applications

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user s mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability

Winter presence of Stone Curlew, Burhinus oedicnemus, in Grosseto province monitored by playback

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