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Financial Time Series: Methods & Models
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Financial Time Series: Methods & Models

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Mathematics and Finance".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 25669

Special Issue Editors

Prof. Dr. Massimiliano Caporin

E-Mail Website
Guest Editor
Department of Statistical Sciences, University of Padova, 35122 Padova, Italy
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giuseppe Storti

E-Mail Website
Guest Editor
Department of Economics and Statistics, University of Salerno, Via Giovanni Paolo II, 132-84084 Fisciano, Italy
Interests: time series econometrics; financial risk management; volatility modeling; time series analysis; time series; GARCH; time series forecasting
Special Issues, Collections and Topics in MDPI journals

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 monthly 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 1400 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 (7 papers)

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Editorial

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3 pages, 161 KiB  
Editorial
Financial Time Series: Methods and Models
by Massimiliano Caporin and Giuseppe Storti
J. Risk Financial Manag. 2020, 13(5), 86; https://doi.org/10.3390/jrfm13050086 - 28 Apr 2020
Viewed by 2748
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Financial Time Series: Methods & Models)

Research

Jump to: Editorial

23 pages, 565 KiB  
Article
Improving Many Volatility Forecasts Using Cross-Sectional Volatility Clusters
by Pietro Coretto, Michele La Rocca and Giuseppe Storti
J. Risk Financial Manag. 2020, 13(4), 64; https://doi.org/10.3390/jrfm13040064 - 29 Mar 2020
Cited by 3 | Viewed by 2399
Abstract
The inhomogeneity of the cross-sectional distribution of realized assets’ volatility is explored and used to build a novel class of GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models. The inhomogeneity of the cross-sectional distribution of realized volatility is captured by a finite Gaussian mixture model [...] Read more.
The inhomogeneity of the cross-sectional distribution of realized assets’ volatility is explored and used to build a novel class of GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models. The inhomogeneity of the cross-sectional distribution of realized volatility is captured by a finite Gaussian mixture model plus a uniform component that represents abnormal variations in volatility. Based on the cross-sectional mixture model, at each time point, memberships of assets to risk groups are retrieved via maximum likelihood estimation, as well as the probability that an asset belongs to a specific risk group. The latter is profitably used for specifying a state-dependent model for volatility forecasting. We propose novel GARCH-type specifications the parameters of which act “clusterwise” conditional on past information on the volatility clusters. The empirical performance of the proposed models is assessed by means of an application to a panel of U.S. stocks traded on the NYSE. An extensive forecasting experiment shows that, when the main goal is to improve overall many univariate volatility forecasts, the method proposed in this paper has some advantages over the state-of-the-arts methods. Full article
(This article belongs to the Special Issue Financial Time Series: Methods & Models)
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21 pages, 334 KiB  
Article
Analytical Gradients of Dynamic Conditional Correlation Models
by Massimiliano Caporin, Riccardo (Jack) Lucchetti and Giulio Palomba
J. Risk Financial Manag. 2020, 13(3), 49; https://doi.org/10.3390/jrfm13030049 - 04 Mar 2020
Cited by 2 | Viewed by 2397
Abstract
We provide the analytical gradient of the full model likelihood for the Dynamic Conditional Correlation (DCC) specification by Engle (2002), the generalised version by Cappiello et al. (2006), and of the cDCC model by Aielli(2013). We discuss how the gradient might be further [...] Read more.
We provide the analytical gradient of the full model likelihood for the Dynamic Conditional Correlation (DCC) specification by Engle (2002), the generalised version by Cappiello et al. (2006), and of the cDCC model by Aielli(2013). We discuss how the gradient might be further extended by introducing elements related to the conditional variance parameters, and discuss the issue arising from the estimation of constrained and/or reparametrised versions of the model. A computational simulation compares analytical versus numerical gradients, with a view to parameter estimation; we find that analytical differentiation yields more efficiency and improved accuracy. Full article
(This article belongs to the Special Issue Financial Time Series: Methods & Models)
20 pages, 1343 KiB  
Article
Capital Markets Integration and Cointegration: Testing for the Correct Specification of Stock Market Indices
by Maria-Eleni K. Agoraki, Dimitris A. Georgoutsos and Georgios P. Kouretas
J. Risk Financial Manag. 2019, 12(4), 186; https://doi.org/10.3390/jrfm12040186 - 09 Dec 2019
Cited by 8 | Viewed by 2936
Abstract
In this paper we develop a comprehensive Vector Autoregression Model consisting of five variables; the stock market and price indices of pairs of countries, as well as their bilateral nominal exchange rate. Then, we show that under certain long-run restrictions, our approach encompasses [...] Read more.
In this paper we develop a comprehensive Vector Autoregression Model consisting of five variables; the stock market and price indices of pairs of countries, as well as their bilateral nominal exchange rate. Then, we show that under certain long-run restrictions, our approach encompasses a large number of specifications encountered in the voluminous literature on testing for capital integration with cointegration techniques. This approach minimizes the risk of accepting the null of no cointegration between the equity price indices because of the introduction of additional stochastic trends through the transformation of those indices on a “real or nominal US dollar” basis. Furthermore, other interesting long run specifications emerge either with I(1) only stochastic shocks or with the presence of some I(2) disturbances characterizing the system. We apply the testing methodology on monthly data for the US, UK, Germany, and Japan for the period January 1980–May 2019. The main findings provide partial support in favor of cointegration, and therefore for capital markets integration, among stock market indices when proper attention is given to issues like the identification and temporal stability of the cointegration vectors as well as the choice of units that the stock indices are expressed in. Full article
(This article belongs to the Special Issue Financial Time Series: Methods & Models)
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15 pages, 639 KiB  
Article
Financial Structure, Misery Index, and Economic Growth: Time Series Empirics from Pakistan
by Nianyong Wang, Muhammad Haroon Shah, Kishwar Ali, Shah Abbas and Sami Ullah
J. Risk Financial Manag. 2019, 12(2), 100; https://doi.org/10.3390/jrfm12020100 - 14 Jun 2019
Cited by 8 | Viewed by 5773
Abstract
This study empirically analyzes the impact of the financial structure and misery index on economic growth in Pakistan. We adopted Autoregressive-Distributed Lag (ARDL) for a co-integration approach to the data analysis and used time series data from 1989 to 2017. We used GDP [...] Read more.
This study empirically analyzes the impact of the financial structure and misery index on economic growth in Pakistan. We adopted Autoregressive-Distributed Lag (ARDL) for a co-integration approach to the data analysis and used time series data from 1989 to 2017. We used GDP as the dependent variable; the Financial Development index (FDI) and misery index as the explanatory variables; and remittances, real interest, and trade openness as the control variables. The empirical results indicate the existence of a long-term relationship among the included variables in the model and the FD index, misery index, interest rate, trade openness, and remittances as the main affecting variables of GDP in the long run. The government needs appropriate reform in the financial sector and external sector in order to achieve a desirable level of economic growth in Pakistan. The misery index is constructed based on unemployment and inflation, which has a negative implication on the economic growth, and the government needs policies to reduce unemployment and inflation. Full article
(This article belongs to the Special Issue Financial Time Series: Methods & Models)
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19 pages, 1354 KiB  
Article
Asymmetric Mean Reversion in Low Liquid Markets: Evidence from BRVM
by Nathaniel Gbenro and Richard Kouamé Moussa
J. Risk Financial Manag. 2019, 12(1), 38; https://doi.org/10.3390/jrfm12010038 - 06 Mar 2019
Cited by 8 | Viewed by 4377
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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13 pages, 399 KiB  
Article
Forecast Combinations for Structural Breaks in Volatility: Evidence from BRICS Countries
by Davide De Gaetano
J. Risk Financial Manag. 2018, 11(4), 64; https://doi.org/10.3390/jrfm11040064 - 21 Oct 2018
Cited by 7 | Viewed by 2694
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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