Abstract
The statistical analysis of financial time series is a rich and diversified research field whose inherent complexity requires an interdisciplinary approach, gathering together several disciplines, such as statistics, economics, and computational sciences. This special issue of the Journal of Risk and Financial Management on “Financial Time Series: Methods & Models” contributes to the evolution of research on the analysis of financial time series by presenting a diversified collection of scientific contributions exploring different lines of research within this field.
Highlights
In recent years, the evolution of research on the analysis of financial time series has been characterized by some distinguishable trends: an increase in the diversity of topics covered, with richer and stronger connections with other disciplines; a focus on increasingly complex models; and a renewed attention to the role of computational techniques
More theoretically-oriented papers focus on issues related to multivariate modeling of returns and volatility, paying specific attention to computational issues, modeling of common trends in stock market returns and integration with machine learning techniques
The forecasting model is globally multivariate, conditionally on the group structure of the cross-sectional distribution of stock volatilities, it can be represented as a set of univariate specifications
Summary
The evolution of research on the analysis of financial time series has been characterized by some distinguishable trends: an increase in the diversity of topics covered, with richer and stronger connections with other disciplines; a focus on increasingly complex models; and a renewed attention to the role of computational techniques. This special issue presents a collection of papers addressing a diversified set of topics of interest for the analysis of financial time series, evenly balanced between empirical and theoretical contributions.
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