Matteo Iacopini

Matteo Iacopini
Vrije Universiteit Amsterdam | VU · Department of Econometrics and Data Science

joint PhD in Economics and Mathématiques Appliquées

About

30
Publications
2,434
Reads
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83
Citations
Additional affiliations
April 2019 - March 2020
Scuola Normale Superiore di Pisa
Position
  • PostDoc Position
April 2018 - May 2018
Università Ca' Foscari Venezia
Position
  • Research Assistant
Description
  • Teaching Assistant in Mathematics for Economics (Bachelor degree course, in Italian)
November 2017 - December 2017
Università Ca' Foscari Venezia
Position
  • Research Assistant
Description
  • Teaching Assistant in Statistics (Bachelor degree course, in English)
Education
September 2014 - July 2018
Université de Paris 1 Panthéon-Sorbonne
Field of study
  • Mathématiques Appliquées
September 2014 - July 2018
Università Ca' Foscari Venezia
Field of study
  • Economics

Publications

Publications (30)
Article
Network models represent a useful tool to describe the complex set of financial relationships among heterogeneous firms in the system. A new Bayesian semiparametric model for temporal multilayer networks with both intra- and inter-layer connectivity is proposed. A hierarchical mixture prior distribution is assumed to capture heterogeneity in the re...
Article
Full-text available
Count time series obtained from online social media data, such as Twitter, have drawn increasing interest among academics and market analysts over the past decade. Transforming Web activity records into counts yields time series with peculiar features, including the coexistence of smooth paths and sudden jumps, as well as cross‐sectional and tempor...
Article
Full-text available
The meme stock phenomenon has yet to be explored. In this note, we provide evidence that these stocks display common stylized facts for the dynamics of price, trading volume, and social media activity. Using a regime-switching cointegration model, we identify the meme stock “mementum” which exhibits a different characterization compared to other st...
Preprint
Full-text available
The meme stock phenomenon is yet to be explored. In this note, we provide evidence that these stocks display common stylized facts on the dynamics of price, trading volume, and social media activity. Using a regime-switching cointegration model, we identify the meme stock "mementum" which exhibits a different characterization with respect to other...
Article
Full-text available
Networks represent a useful tool to describe relationships among financial firms and network analysis has been extensively used in recent years to study financial connectedness. An aspect, which is often neglected, is that network observations come with errors from different sources, such as estimation and measurement errors, thus a proper statisti...
Preprint
Full-text available
Network models represent a useful tool to describe the complex set of financial relationships among heterogeneous firms in the system. In this paper, we propose a new semiparametric model for temporal multilayer causal networks with both intra- and inter-layer connectivity. A Bayesian model with a hierarchical mixture prior distribution is assumed...
Preprint
Full-text available
Count time series obtained from online social media data, such as Twitter, have drawn increasing interest among academics and market analysts over the past decade. Transforming Web activity records into counts yields time series with peculiar features, including the coexistence of smooth paths and sudden jumps, as well as cross-sectional and tempor...
Article
During the outbreak of the COVID-19, concerns related to the severity of the pandemic have played a prominent role in investment decisions. In this paper, we analyze the relationship between public attention and the financial markets using search engine data from Google Trends. Our findings show that search query volumes in Italy, Germany, France,...
Preprint
Full-text available
We propose a user-friendly graphical tool, the half-disk density strip (HDDS), for visualizing and comparing probability density functions. The HDDS exploits color shading for representing a distribution in an intuitive way. In univariate settings, the half-disk density strip allows to immediately discern the key characteristics of a density, such...
Preprint
Full-text available
This paper proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It extends the proposed score and defines a weighted version, which emphasizes regions of interest, such as the tails or the center of a variable's range. The ACPS is of general use in any situation where the decision maker h...
Preprint
Full-text available
We measure public concern in Italy, Germany, France, Great Britain, Spain and the United States during the outbreak of COVID-19 using three search-engine data sources from Google Trends: YouTube, Google News and Google Search. We find that the dynamic of public concern in Italy is a driver of that in other countries. Among the Google trends series,...
Article
This manuscript proposes a new approach for unveiling existing linkages within the international oil market across multiple driving factors beyond production. A multi-layer, multi-country network is extracted through a novel Bayesian graphical vector autoregressive model, which allows for a more comprehensive, dynamic representation of the network...
Article
Full-text available
This study measures public concern in Italy, Germany, France, Great Britain, Spain, and the United States during the outbreak of COVID-19 using three search-engine data sources from Google Trends: YouTube, Google News, and Google Search. The results show that the dynamic of public concern in Italy anticipates the level of public concern in other c...
Preprint
Full-text available
Over the last decade, big data have poured into econometrics, demanding new statistical methods for analysing high-dimensional data and complex non-linear relationships. A common approach for addressing dimensionality issues relies on the use of static graphical structures for extracting the most significant dependence interrelationships between th...
Chapter
In this paper we present a binary regression model with tensor coefficients and present a Bayesian model for inference, able to recover different levels of sparsity of the tensor coefficient. We exploit the CONDECOMP/PARAFAC (CP) representation for the tensor of coefficients in order to reduce the number of parameters and adopt a suitable hierarchi...
Chapter
In this paper we introduce the literature on regression models with tensor variables and present a Bayesian linear model for inference, under the assumption of sparsity of the tensor coefficient. We exploit the CONDECOMP/PARAFAC (CP) representation for the tensor of coefficients in order to reduce the number of parameters and adopt a suitable hiera...
Thesis
Graph theory has long been studied in mathematics and probability as a tool for describing dependence between nodes. However, only recently it has been implemented on data, giving birth to the statistical analysis of real networks.The topology of economic and financial networks is remarkably complex: it is generally unobserved, thus requiring adequ...
Article
Full-text available
The study of dependence between random variables is the core of theoretical and applied statistics. Static and dynamic copula models are useful for describing the dependence structure, which is fully encrypted in the copula probability density function. However, these models are not always able to describe the temporal change of the dependence patt...
Article
Full-text available
Invited Discussion : Bertrand Clarke - Meng Li - Peter Grunwald and Rianne de Heide Contributed Discussion : A. Philip Dawid - William Weimin Yoo - Robert L. Winkler, Victor Richmond R. Jose, Kenneth C. Lichtendahl Jr., and Yael Grushka-Cockayne - Kenichiro McAlinn, Knut Are Aastveit, and Mike West - Minsuk Shin - Tianjian Zhou - Lennart Hoogerheid...
Article
Full-text available
We propose a new Bayesian Markov switching regression for modelling dynamic multilayer networks based on the zero-inflated logit model. The original contribution is threefold. First, we propose a suitable low-rank decomposition of the tensor of regression coefficients for parsimony and avoiding over-fitting. The identification of the regression coe...
Article
Full-text available
We propose a new dynamic linear regression model for tensor variate response and covariates that encompasses univariate, multivariate (i.e. SUR, VAR, panel VAR) and matrix regression models as special cases. For dealing with the over-parametrization and overfitting issues due to the curse of dimensionality, we exploit a suitable parametrization whi...
Article
Full-text available
Discussion on "Sparse graphs using exchangeable random measures" by F. Caron and E. B. Fox. In this discussion we contribute to the analysis of the GGP model as compared to the Erdos-Renyi (ER) and the preferential attachment (AB) models, using different measures such as number of connected components, global clustering coefficient, assortativity...

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