Special Issue "Econometric Analysis of Networks"

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A special issue of Journal of Risk and Financial Management (ISSN 1911-8074).

Deadline for manuscript submissions: closed (31 January 2015)

Special Issue Editor

Guest Editor
Prof. Dr. Christian Brownlees (Website)

Department of Economics and Business, Pompeu Fabra University, Ramon Trias Fargas 25-27, Office 2-E10, 08005, Barcelona, Spain
Interests: network analysis; nonlinear time series; forecasting; statistical computing; empirical finance; financial high frequency data

Special Issue Information

Dear Colleagues,

Over the last couple of years, network analysis has rapidly become an important area of research in economics and finance. Network techniques aim at providing tools to analyze the degree of interconnectedness in high dimensional multivariate systems and its implications. Following the 2007-2009 financial crisis, a number of authors have started to apply these tools to study interconnections in the financial system. One of the main objectives of this strand of the literature is to identify highly interconnected financial institutions which might pose systemic threats to entire financial system. This has been also motivated by the current financial regulation environment which focuses on identifying SIFIs (Systemically Important Financial Institution). This special issue is intended to reflect the current theoretical and empirical research on network analysis in econometrics with a focus on financial applications.

Among the general topics of research to be considered are:

  1. Network Estimation
  2. Models for Contagion
  3. Credit Risk Networks
  4. Market Risk Networks
  5. Connectedness Indices
  6. Interbank Liquidity Networks

Dr. Christian Brownlees
Guest Editor

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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. For the first couple of issues the Article Processing Charge (APC) will be waived for well-prepared manuscripts. English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

Published Papers (3 papers)

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Research

Open AccessArticle Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information
J. Risk Financial Manag. 2015, 8(2), 266-284; doi:10.3390/jrfm8020266
Received: 7 December 2014 / Revised: 27 April 2015 / Accepted: 20 May 2015 / Published: 1 June 2015
Cited by 1 | PDF Full-text (2826 KB) | HTML Full-text | XML Full-text
Abstract
Analyzing social systems, particularly financial markets, using a complex network approach has become one of the most popular fields within econophysics. A similar trend is currently appearing within the econometrics and finance communities, as well. In this study, we present a state-of-the-artmethod [...] Read more.
Analyzing social systems, particularly financial markets, using a complex network approach has become one of the most popular fields within econophysics. A similar trend is currently appearing within the econometrics and finance communities, as well. In this study, we present a state-of-the-artmethod for analyzing the structure and risk within stockmarkets, treating them as complex networks using model-free, nonlinear dependency measures based on information theory. This study is the first network analysis of the stockmarket in Shanghai using a nonlinear network methodology. Further, it is often assumed that markets outside the United States and Western Europe are inherently riskier. We find that the Chinese stock market is not structurally risky, contradicting this popular opinion. We use partial mutual information to create filtered networks representing the Shanghai stock exchange, comparing them to networks based on Pearson’s correlation. Consequently, we discuss the structure and characteristics of both the presented methods and the Shanghai stock exchange. This paper provides an insight into the cutting edge methodology designed for analyzing complex financial networks, as well as analyzing the structure of the market in Shanghai and, as such, is of interest to both researchers and financial analysts. Full article
(This article belongs to the Special Issue Econometric Analysis of Networks)
Open AccessArticle Dependency Relations among International Stock Market Indices
J. Risk Financial Manag. 2015, 8(2), 227-265; doi:10.3390/jrfm8020227
Received: 1 December 2014 / Revised: 20 April 2015 / Accepted: 29 April 2015 / Published: 29 May 2015
Cited by 4 | PDF Full-text (1302 KB) | HTML Full-text | XML Full-text
Abstract
We develop networks of international stock market indices using information and correlation based measures. We use 83 stock market indices of a diversity of countries, as well as their single day lagged values, to probe the correlation and the flow of information [...] Read more.
We develop networks of international stock market indices using information and correlation based measures. We use 83 stock market indices of a diversity of countries, as well as their single day lagged values, to probe the correlation and the flow of information from one stock index to another taking into account different operating hours. Additionally, we apply the formalism of partial correlations to build the dependency network of the data, and calculate the partial Transfer Entropy to quantify the indirect influence that indices have on one another. We find that Transfer Entropy is an effective way to quantify the flow of information between indices, and that a high degree of information flow between indices lagged by one day coincides to same day correlation between them. Full article
(This article belongs to the Special Issue Econometric Analysis of Networks)
Open AccessArticle Firm Value and Cross Listings: The Impact of Stock Market Prestige
J. Risk Financial Manag. 2015, 8(1), 150-180; doi:10.3390/jrfm8010150
Received: 27 October 2014 / Revised: 26 February 2015 / Accepted: 9 March 2015 / Published: 23 March 2015
PDF Full-text (220 KB) | HTML Full-text | XML Full-text
Abstract
This study investigates the valuation impact of a firm’s decision to cross list on a more (or less) prestigious stock exchange relative to its own domestic market. We use a network analysis methodology to derive broad market-based measures of prestige for 45 [...] Read more.
This study investigates the valuation impact of a firm’s decision to cross list on a more (or less) prestigious stock exchange relative to its own domestic market. We use a network analysis methodology to derive broad market-based measures of prestige for 45 country or regional stock exchange destinations between 1990 and 2006. We find that firms cross listing in a more prestigious market enjoy significant valuation gains over the five-year period following the listing. In contrast, firms cross listing in less prestigious markets experience a significant valuation discount over this post-listing period. The reputation of the cross-border listing destinations is therefore a useful signal of firm value going forward. Our findings are consistent with the view that cross listing in a prestigious market enhances firm visibility, strengthens corporate governance, and lowers informational frictions and capital costs. Full article
(This article belongs to the Special Issue Econometric Analysis of Networks)

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