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Panel Data and Factor Models in Empirical Finance
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Panel Data and Factor Models in Empirical Finance

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 (30 April 2019) | Viewed by 10025

Special Issue Editors

Prof. Dr. Jörg Breitung

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Guest Editor
Center of Econometrics and Statistics, University of Cologne, 50923 Cologne, Germany
Interests: panel data analysis; time series analysis; forecasting; financial econometrics
Prof. Dr. Robinson Kruse

E-Mail Website
Guest Editor
Institut für Ökonometrie und Statistik, University of Cologne, Cologne, Germany
Interests: time series econometrics; forecasting; empirical finance

Special Issue Information

Dear Colleagues,

Nowadays, panel data methods and factor models play an important role in empirical finance. This Special Issue deals with the analysis of multivariate financial data that is characterized by a large number of cross-section units and time periods. The availability of “big” financial data sets has spurred many new developments in empirical finance. This issue invites submissions in the broad area of panel data modelling, large dimensional data and factors models with empirical applications in financial econometrics. A wide spectrum of methods might be covered ranging from the analysis of high-dimensional covariance matrices, dynamic factor models, spatial and quantile regressions. Possible topics for applications include (but are not limited to) asset pricing, risk management, volatility forecasting, portfolio allocation, speculative bubbles and stock return prediction.

Prof. Dr. Jörg Breitung
Prof. Dr. Robinson Kruse
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

  • Panel data models
  • Factor models
  • High-dimensional data analysis
  • Dimension reduction
  • Estimation and inference
  • Asset pricing
  • Portfolio Selection
  • Time-varying volatility
  • Long memory
  • Speculative Bubbles
  • Prediction

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Published Papers (3 papers)

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Research

10 pages, 239 KiB  
Article
The Stability of Factor Sensitivities of German Stock Market Sector Indices: Empirical Evidence and Some Thoughts about Practical Implications
by Christoph Wegener and Tobias Basse
J. Risk Financial Manag. 2019, 12(3), 140; https://doi.org/10.3390/jrfm12030140 - 29 Aug 2019
Cited by 3 | Viewed by 2644
Abstract
This empirical study estimates 18 single and 18 three-factor models and then tests for structural change. Break dates are identified where possible. In general, there is some empirical evidence for parameter instabilities of the estimated beta coefficients. In most cases there is no [...] Read more.
This empirical study estimates 18 single and 18 three-factor models and then tests for structural change. Break dates are identified where possible. In general, there is some empirical evidence for parameter instabilities of the estimated beta coefficients. In most cases there is no or one break point, and in some cases, there are two structural breaks examining the three factor models. The estimated factor sensitivities of single beta models seem to be even less strongly affected by structural change. Consequently, beta factors are probably more stable than some observers might believe. The break dates that have been identified generally seem to coincide with crises or recoveries after stock market slumps. This empirical finding is compatible with the point of view that bull-markets or bear-markets could matter when estimating beta coefficients. In general, the timing of structural change often seems to coincide with either the bursting of the dot-com bubble or the recovery of stock prices thereafter. The banking industry is the most notable exception. In this sector of the German economy, the global financial meltdown and the sovereign debt crisis in Europe have been of high relevance. Consequently, the internet hype of the late 1990s and the early 2000s seems to be more important for the German stock market than the US subprime debacle and the accompanying European sovereign debt crisis. Full article
(This article belongs to the Special Issue Panel Data and Factor Models in Empirical Finance)
22 pages, 1582 KiB  
Article
On Combining Evidence from Heteroskedasticity Robust Panel Unit Root Tests in Pooled Regressions
by Martin C. Arnold and Christoph Hanck
J. Risk Financial Manag. 2019, 12(3), 117; https://doi.org/10.3390/jrfm12030117 - 12 Jul 2019
Viewed by 3085
Abstract
Volatility break robust panel unit root tests (PURTs) recently proposed by Herwartz and Siedenburg (Computational Statistics & Data Analysis 2008, 53, 137–150) and Demetrescu and Hanck (Econometrics Letters 2012, 117, 10–13) have different performances under both the null and local alternatives. Common practice [...] Read more.
Volatility break robust panel unit root tests (PURTs) recently proposed by Herwartz and Siedenburg (Computational Statistics & Data Analysis 2008, 53, 137–150) and Demetrescu and Hanck (Econometrics Letters 2012, 117, 10–13) have different performances under both the null and local alternatives. Common practice in empirical research is to apply multiple tests if none is uniformly superior. We show that this approach tends to produce contradictory evidence for the tests considered, making it unclear whether to reject the null. To address this problem, we advocate a combined testing procedure. Simulation evidence shows that the combined test has good size control and closely tracks the more powerful test. An empirical application reinvestigates whether there is a unit root in OECD inflation rates. We find evidence that inflation is stationary for long observation periods, but we cannot reject nonstationarity in most subsets of countries for the last three decades. Full article
(This article belongs to the Special Issue Panel Data and Factor Models in Empirical Finance)
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16 pages, 410 KiB  
Article
Modeling and Forecasting Realized Portfolio Diversification Benefits
by Vasyl Golosnoy, Benno Hildebrandt and Steffen Köhler
J. Risk Financial Manag. 2019, 12(3), 116; https://doi.org/10.3390/jrfm12030116 - 11 Jul 2019
Viewed by 3824
Abstract
For a financial portfolio, we suggest a realized measure of diversification benefits, which is based on intraday high-frequency returns. Our measure quantifies volatility reduction, which could be achieved by including an additional asset in the portfolio. In order to make our approach feasible [...] Read more.
For a financial portfolio, we suggest a realized measure of diversification benefits, which is based on intraday high-frequency returns. Our measure quantifies volatility reduction, which could be achieved by including an additional asset in the portfolio. In order to make our approach feasible for investors, we also provide time series modeling of both the realized diversification measure and realized portfolio weight. The performance of our approach is evaluated in-sample and out-of-sample. We find out that our approach is helpful for the purpose of portfolio variance minimization. Full article
(This article belongs to the Special Issue Panel Data and Factor Models in Empirical Finance)
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