Special Issue "Time Series Econometrics"

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

Deadline for manuscript submissions: closed (15 June 2020).

Special Issue Editors

Prof. Dr. Zhongjun Qu
Website
Guest Editor
Department of Economics, Boston University, 270 Bay State Road, Boston, MA, 02215 USA
Interests: econometrics; quantitative macroeconomics; empirical finance
Prof. Dr. Pierre Perron
Website
Guest Editor
Department of Economics, Boston University, Boston, MA, USA
Interests: econometrics; theoretical and applied time series analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue welcomes contributions pertaining to theoretical and applied issues in time series methods, especially as they relate to innovative financial and macroeconomic applications, broadly defined. We are paticularly interested in papers that propose new ideas or develop methods related to the identification, computation, estimation, and forecating of time series models. Both theoretical and empirical papers are welcomed, especially those that deal with both aspects. Time series methods developed in econometrics (and other fields) have been at the forefrunt of tools used to address important issues in finance, risk management, international finance, and macroeconomics, among many other fields. Such tools are still important now, and new ones are constantly being proposed to analyze new issues or revisit important topics. Still, there is scope for improvements in methods, analyses of the properties of existing procedures, and novel applications. The aim is to provide contributions that follow up on what has been done and/or offer new perspectives on such issues and related ones. This Special Issue aims to provide state-of-the-art advances, and will be published in printed book format if more than seven papers are accepted for publication.

Prof. Dr. Zhongjun Qu
Prof. Dr. Pierre Perron
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 papers will be 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 1000 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

  • Time Series econometrics
  • Identification
  • Computation
  • Estimation
  • Forecasting

Published Papers (2 papers)

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Research

Open AccessArticle
Competition, Debt Maturity, and Adjustment Speed in China: A Dynamic Fractional Estimation Approach
J. Risk Financial Manag. 2020, 13(5), 106; https://doi.org/10.3390/jrfm13050106 - 23 May 2020
Abstract
The purpose of this study was to investigate the capital structure adjustment rate in different levels of product market competitions. We classified Chinese non-financial listed firms into highly, moderately, and less competitive firms and applied an unbiased dynamic panel fractional estimator on unbalanced [...] Read more.
The purpose of this study was to investigate the capital structure adjustment rate in different levels of product market competitions. We classified Chinese non-financial listed firms into highly, moderately, and less competitive firms and applied an unbiased dynamic panel fractional estimator on unbalanced panel data of 10,941 firm-year observations during the period of 1998 to 2015. We find that the adjustment rate of highly and less competitive firms towards long-term target capital structure is higher (28.2–29.1%) as compared to the adjustment rate towards short-term target capital structure (18.8–18.9%). On the other hand, the adjustment rate of moderately competitive firms towards long-term target capital structure is slower (22.3%) as compared to the adjustment rate towards short-term target capital structure (25.3%). Further, the adjustment rate of highly and less competitive firms differs significantly between long-term and short-term target capital structure, while the adjustment rate of moderately competitive firms remains steady. Highly competitive large firms follow the limited liability model to adjust their target capital structure and support trade-off theory, while both small and large firms follow the limited liability and predation models in moderately and less competitive environments, respectively. Full article
(This article belongs to the Special Issue Time Series Econometrics)
Open AccessArticle
Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions
J. Risk Financial Manag. 2020, 13(4), 84; https://doi.org/10.3390/jrfm13040084 - 24 Apr 2020
Cited by 1
Abstract
In this study, we enhance the dynamic connectedness measures originally introduced by Diebold and Yılmaz (2012, 2014) with a time-varying parameter vector autoregressive model (TVP-VAR) which predicates upon a time-varying variance-covariance structure. This framework allows to capture possible changes in the underlying structure [...] Read more.
In this study, we enhance the dynamic connectedness measures originally introduced by Diebold and Yılmaz (2012, 2014) with a time-varying parameter vector autoregressive model (TVP-VAR) which predicates upon a time-varying variance-covariance structure. This framework allows to capture possible changes in the underlying structure of the data in a more flexible and robust manner. Specifically, there is neither a need to arbitrarily set the rolling-window size nor a loss of observations in the calculation of the dynamic measures of connectedness, as no rolling-window analysis is involved. Given that the proposed framework rests on multivariate Kalman filters, it is less sensitive to outliers. Furthermore, we emphasise the merits of this approach by conducting Monte Carlo simulations. We put our framework into practice by investigating dynamic connectedness measures of the four most traded foreign exchange rates, comparing the TVP-VAR results to those obtained from three different rolling-window settings. Finally, we propose uncertainty measures for both TVP-VAR-based and rolling-window VAR-based dynamic connectedness measures. Full article
(This article belongs to the Special Issue Time Series Econometrics)
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