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Econometrics
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Big Data in Economics and Finance
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Big Data in Economics and Finance

A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: closed (31 May 2019) | Viewed by 37984

Special Issue Editors

Prof. Dr. Massimiliano Caporin

E-Mail Website
Guest Editor
Department of Statistical Sciences, University of Padova, 35122 Padova, Italy
Interests: financial time series analysis; risk management; market risk; systemic risk; univariate and multivariate volatility models; quantitative portfolio allocation strategies; managed portfolios performance measurement; high-frequency data analysis and trading strategies; dynamic models for energy and weather applications
Special Issues, Collections and Topics in MDPI journals
Dr. Juri Marcucci

E-Mail Website
Guest Editor
Bank of Italy
Interests: financial econometrics; forecasting; empirical finance and applied econometrics; bid data econometrics and high-dimensional econometrics
Dr. J. James Reade

E-Mail Website
Guest Editor
Department of Economic, University of Reading, UK
Interests: applied econometrics; empirical macroeconomics; big data; sport economics

Special Issue Information

Dear Colleagues,

Big data is en vogue. What it actually is, however, appears to differ from field to field, and even from practitioners within fields. Ever increasing computer power and storage has enabled increasingly large datasets to be analysed. This has allowed some age old questions to be answered, but equally has made apparent the reality that standard issues of statistical inference remain writ large even with big data. It has also required the development of new methods to analyse sufficiently large datasets; indeed one definition of big data is that it must entail this.

This Special Issue focusses on big data in economics and finance. In particular, how big data applications have developed, the kinds of questions that have been better answered using big data, and the kinds of challenges that remain to be overcome. In economics, we think of large social media and public sector databases being made available, alongside the more proprietary datasets such as those collected by supermarkets on customers. In finance, big data seems to fit most naturally when dealing with trade and quotes data, which update on a millisecond basis and can be easily integrated with news and social media tweets.

Prof. Dr. Massimiliano Caporin
Dr. Juri Marcucci
Dr. J. James Reade
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Econometrics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Big data
  • econometrics
  • statistics
  • finance
  • inference

Published Papers (4 papers)

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Research

24 pages, 3892 KiB  
Article
Structural Panel Bayesian VAR Model to Deal with Model Misspecification and Unobserved Heterogeneity Problems
by Antonio Pacifico
Econometrics 2019, 7(1), 8; https://doi.org/10.3390/econometrics7010008 - 11 Mar 2019
Cited by 9 | Viewed by 7123
Abstract
This paper provides an overview of a time-varying Structural Panel Bayesian Vector Autoregression model that deals with model misspecification and unobserved heterogeneity problems in applied macroeconomic analyses when studying time-varying relationships and dynamic interdependencies among countries and variables. I discuss what its distinctive [...] Read more.
This paper provides an overview of a time-varying Structural Panel Bayesian Vector Autoregression model that deals with model misspecification and unobserved heterogeneity problems in applied macroeconomic analyses when studying time-varying relationships and dynamic interdependencies among countries and variables. I discuss what its distinctive features are, what it is used for, and how it can be analytically derived. I also describe how it is estimated and how structural spillovers and shock identification are performed. The model is empirically applied to a set of developed European economies to illustrate the functioning and the ability of the model. The paper also discusses more recent studies that have used multivariate dynamic macro-panels to evaluate idiosyncratic business cycles, policy-making, and spillover effects among different sectors and countries. Full article
(This article belongs to the Special Issue Big Data in Economics and Finance)
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45 pages, 5927 KiB  
Article
Financial Big Data Solutions for State Space Panel Regression in Interest Rate Dynamics
by Dorota Toczydlowska and Gareth W. Peters
Econometrics 2018, 6(3), 34; https://doi.org/10.3390/econometrics6030034 - 18 Jul 2018
Cited by 2 | Viewed by 7903
Abstract
A novel class of dimension reduction methods is combined with a stochastic multi-factor panel regression-based state-space model in order to model the dynamics of yield curves whilst incorporating regression factors. This is achieved via Probabilistic Principal Component Analysis (PPCA) in which new statistically-robust [...] Read more.
A novel class of dimension reduction methods is combined with a stochastic multi-factor panel regression-based state-space model in order to model the dynamics of yield curves whilst incorporating regression factors. This is achieved via Probabilistic Principal Component Analysis (PPCA) in which new statistically-robust variants are derived also treating missing data. We embed the rank reduced feature extractions into a stochastic representation for state-space models for yield curve dynamics and compare the results to classical multi-factor dynamic Nelson–Siegel state-space models. This leads to important new representations of yield curve models that can be practically important for addressing questions of financial stress testing and monetary policy interventions, which can incorporate efficiently financial big data. We illustrate our results on various financial and macroeconomic datasets from the Euro Zone and international market. Full article
(This article belongs to the Special Issue Big Data in Economics and Finance)
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19 pages, 7486 KiB  
Article
Assessing News Contagion in Finance
by Paola Cerchiello and Giancarlo Nicola
Econometrics 2018, 6(1), 5; https://doi.org/10.3390/econometrics6010005 - 03 Feb 2018
Cited by 23 | Viewed by 11527
Abstract
The analysis of news in the financial context has gained a prominent interest in the last years. This is because of the possible predictive power of such content especially in terms of associated sentiment/mood. In this paper, we focus on a specific aspect [...] Read more.
The analysis of news in the financial context has gained a prominent interest in the last years. This is because of the possible predictive power of such content especially in terms of associated sentiment/mood. In this paper, we focus on a specific aspect of financial news analysis: how the covered topics modify according to space and time dimensions. To this purpose, we employ a modified version of topic model LDA, the so-called Structural Topic Model (STM), that takes into account covariates as well. Our aim is to study the possible evolution of topics extracted from two well known news archive—Reuters and Bloomberg—and to investigate a causal effect in the diffusion of the news by means of a Granger causality test. Our results show that both the temporal dynamics and the spatial differentiation matter in the news contagion. Full article
(This article belongs to the Special Issue Big Data in Economics and Finance)
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453 KiB  
Article
Building News Measures from Textual Data and an Application to Volatility Forecasting
by Massimiliano Caporin and Francesco Poli
Econometrics 2017, 5(3), 35; https://doi.org/10.3390/econometrics5030035 - 19 Aug 2017
Cited by 25 | Viewed by 10281
Abstract
We retrieve news stories and earnings announcements of the S&P 100 constituents from two professional news providers, along with ten macroeconomic indicators. We also gather data from Google Trends about these firms’ assets as an index of retail investors’ attention. Thus, we create [...] Read more.
We retrieve news stories and earnings announcements of the S&P 100 constituents from two professional news providers, along with ten macroeconomic indicators. We also gather data from Google Trends about these firms’ assets as an index of retail investors’ attention. Thus, we create an extensive and innovative database that contains precise information with which to analyze the link between news and asset price dynamics. We detect the sentiment of news stories using a dictionary of sentiment-related words and negations and propose a set of more than five thousand information-based variables that provide natural proxies for the information used by heterogeneous market players. We first shed light on the impact of information measures on daily realized volatility and select them by penalized regression. Then, we perform a forecasting exercise and show that the model augmented with news-related variables provides superior forecasts. Full article
(This article belongs to the Special Issue Big Data in Economics and Finance)
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