Modeling Multivariate Financial Time Series and Computing

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

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