Modeling Multivariate Financial Time Series and Computing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 150

Special Issue Editors


E-Mail Website
Guest Editor
Department of Economics and Statistics, University of Salerno, 84084 Fisciano, Italy
Interests: volatility; mixed-frequency methods; tail risk measures; financial time series

E-Mail Website
Guest Editor
Department of Economics and Statistics, University of Salerno, 84084 Fisciano, Italy
Interests: financial econometrics; time series analysis; volatility; tail risk forecasts

Special Issue Information

Dear Colleagues,

Accurate prediction of time-varying covariances is an important problem in multivariate financial data modelling. The use of highly correlated and high-dimensional time series data introduces many complications and challenges. Methods and theories to solve these problems constitute the content of time series analysis in many respects.

The development of data cleaning and management tools as prerequisites for time series analyses, the definition of appropriate estimation approaches, and the imposition of data-driven and economics-driven parameter restrictions are leading to the development of more flexible approaches for capturing, reproducing, and synthesizing the main dynamics of the series.

The Special Issue “Modeling Multivariate Financial Time Series and Computing” aims to promote articles presenting theoretical developments and/or applied analyses in the context of multivariate financial time series. Articles on the estimation and prediction of the conditional covariance matrix of financial assets using existing methods or new econometric approaches are strongly encouraged. Additionally, papers dealing with new computational tools that help reduce the impact of the so-called curse of dimensionality, which usually affects multivariate time series analysis, are welcome. Finally, articles that adopt or propose methods for modeling variables at different frequencies in the field of covariance matrices are highly appreciated.

The topics of interest are (but not limited to):

  • Multivariate analysis of financial time series;
  • Estimation, prediction and evaluation of conditional covariance matrices of financial assets;
  • Mixed-frequency models for multivariate time series analysis;
  • Computational aspects of the multivariate time series analysis;
  • Portfolio selection;
  • Multivariate financial tail risk measures.

Dr. Vincenzo Candila
Dr. Antonio Naimoli
Guest Editors

Manuscript Submission Information

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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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • conditional covariance matrix
  • multivariate financial time series analysis
  • computational methods for multivariate time series analysis
  • spillovers
  • risk measures
  • tail risk measures

Published Papers

This special issue is now open for submission, see below for planned papers.

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Accurate prediction of time-varying covariances is an important problem in multivariate financial data modelling. The use of highly correlated and high-dimensional time series data introduces many complications and challenges. Methods and theories to solve these problems constitute the content of time series analysis in many respects. The development of data cleaning and management tools as prerequisites for time series analyses, the definition of appropriate estimation approaches, and the imposition of data-driven and economics-driven parameter restrictions are leading to the development of more flexible approaches for capturing, reproducing, and synthesizing the main dynamics of the series. The Special Issue “Modeling Multivariate Financial Time Series and Computing” aims to promote articles presenting theoretical developments and/or applied analyses in the context of multivariate financial time series.
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