Special Issue "Big Data in Economics and Finance"

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

Deadline for manuscript submissions: 31 December 2018

Special Issue Editors

Guest Editor
Prof. Dr. Massimiliano Caporin

Department of Statistical Sciences, University of Padova, Italy
Website | E-Mail
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
Guest Editor
Dr. Juri Marcucci

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

Department of Economic, University of Reading, UK
Website | E-Mail
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

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Keywords

  • Big data
  • econometrics
  • statistics
  • finance
  • inference

Published Papers (3 papers)

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Research

Open AccessArticle Financial Big Data Solutions for State Space Panel Regression in Interest Rate Dynamics
Econometrics 2018, 6(3), 34; https://doi.org/10.3390/econometrics6030034
Received: 12 February 2018 / Revised: 19 June 2018 / Accepted: 7 July 2018 / Published: 18 July 2018
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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
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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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Open AccessArticle Assessing News Contagion in Finance
Received: 29 May 2017 / Revised: 18 December 2017 / Accepted: 25 January 2018 / Published: 3 February 2018
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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
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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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Open AccessArticle Building News Measures from Textual Data and an Application to Volatility Forecasting
Econometrics 2017, 5(3), 35; https://doi.org/10.3390/econometrics5030035
Received: 5 April 2017 / Revised: 2 August 2017 / Accepted: 14 August 2017 / Published: 19 August 2017
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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
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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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