Special Issue "Financial Time Series: Methods & Models"

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
Prof. Dr. Giuseppe Storti

Department of Economics and Statistics / DISES, University of Salerno, Italy
Website | E-Mail
Interests: time series econometrics; financial risk management; volatility modeling; time series analysis; time series; GARCH; time series forecasting

Special Issue Information

Dear Colleagues,

In the last two decades, thanks to the progress in information technology, large (in the cross-section) and ultra-high-frequency financial datasets have become increasingly available to the academic community. The rich dependence structure of these data has stimulated the demand for more complex dynamic models along different research lines. On one side, the larger cross-sectional dimensions—which are easily accessible—pose challenges to the use of multivariate models, with the need of specifying appropriate estimation approaches and/or to impose data- and economically-driven parameter restrictions. On the other side, the data available at high frequency push for the development of data cleaning and data management tools as pre-requisites for time series analyses. More recently, data integration aspects have received attention, and financial time series data become a source of information for the estimation of financial networks within multidimensional time series models.

Currently, approaches that are even more flexible are needed to properly extract the relevant information from a rapidly growing amount of data, resorting, for instance, to statistical learning approaches or to functional methods.

In this perspective, the purpose of this Special Issue is to collect works that point at the development of state-of-the art methods or models which are appropriate for the analysis of financial data with a most prominent focus on the forecasting of tail risk measures.

Prof. Dr. Massimiliano Caporin
Prof. Dr. Giuseppe Storti
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. Journal of Risk and Financial Management 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 350 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

  • Financial time series
  • Point and density forecasts
  • High frequency
  • Large dimensional problems
  • Dynamic risk and quantile models
  • Realized measures
  • Finance analytics
  • Backtesting

Published Papers (2 papers)

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Research

Open AccessArticle Asymmetric Mean Reversion in Low Liquid Markets: Evidence from BRVM
J. Risk Financial Manag. 2019, 12(1), 38; https://doi.org/10.3390/jrfm12010038
Received: 31 December 2018 / Revised: 28 February 2019 / Accepted: 4 March 2019 / Published: 6 March 2019
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Abstract
This paper analyzes the mean reversion property on the west African stock market (in French, Bourse Régionale des Valeurs Mobilières BRVM). For this purpose, we use two daily indices: (i) the composite index (BRVMC) and (ii) the index of the 10 most liquid [...] Read more.
This paper analyzes the mean reversion property on the west African stock market (in French, Bourse Régionale des Valeurs Mobilières BRVM). For this purpose, we use two daily indices: (i) the composite index (BRVMC) and (ii) the index of the 10 most liquid assets (BRVM10) collected from 3 January 2005 to 29 June 2018. We estimate an asymmetric nonlinear autoregressive model with an EGARCH innovation to account for heteroskedasticity. The results suggest the existence of a mean reversion property for both indices. The half-life time is 7 days for the composite index and 2 days for the BRVM 10 index. Furthermore, using a rolling regression technique, we show that the estimated half-life time declines slightly for the composite index. Full article
(This article belongs to the Special Issue Financial Time Series: Methods & Models)
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Open AccessFeature PaperArticle Forecast Combinations for Structural Breaks in Volatility: Evidence from BRICS Countries
J. Risk Financial Manag. 2018, 11(4), 64; https://doi.org/10.3390/jrfm11040064
Received: 4 October 2018 / Revised: 16 October 2018 / Accepted: 17 October 2018 / Published: 21 October 2018
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Abstract
The aim of this paper is to investigate the relevance of structural breaks for forecasting the volatility of daily returns on BRICS countries (Brazil, Russia, India, China and South Africa). The data set used in the analysis is the Morgan Stanley Capital International [...] Read more.
The aim of this paper is to investigate the relevance of structural breaks for forecasting the volatility of daily returns on BRICS countries (Brazil, Russia, India, China and South Africa). The data set used in the analysis is the Morgan Stanley Capital International MSCI daily returns and covers the period from 19 July 1999 to 16 July 2015. To identify structural breaks in the unconditional variance, a binary segmentation algorithm with a test, which considers both the fourth order moment of the process and persistence in the variance, has been implemented. Some forecast combinations that account for the identified structural breaks have been introduced and their performance has been evaluated and compared by using the Model Confidence Set (MCS). The results give significant evidence of the relevance of the structural breaks. In particular, in the regimes identified by the structural breaks, a substantial change in the unconditional variance is quite evident. In forecasting volatility, the combination that averages forecasts obtained using different rolling estimation windows outperforms all the other combinations Full article
(This article belongs to the Special Issue Financial Time Series: Methods & Models)
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J. Risk Financial Manag. EISSN 1911-8074 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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