Journal of Risk and Financial Management

2826 KiB

Open AccessArticle

Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information

by Tao You, Paweł Fiedor and Artur Hołda

J. Risk Financial Manag. 2015, 8(2), 266-284; https://doi.org/10.3390/jrfm8020266 - 01 Jun 2015

Cited by 22 | Viewed by 8122

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 [...] 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)

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1302 KiB

Open AccessArticle

Dependency Relations among International Stock Market Indices

by Leonidas Sandoval Junior, Asher Mullokandov and Dror Y. Kenett

J. Risk Financial Manag. 2015, 8(2), 227-265; https://doi.org/10.3390/jrfm8020227 - 29 May 2015

Cited by 68 | Viewed by 10057

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 from [...] 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

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918 KiB

Open AccessArticle

Interconnected Risk Contributions: A Heavy-Tail Approach to Analyze U.S. Financial Sectors

by Mauro Bernardi and Lea Petrella

J. Risk Financial Manag. 2015, 8(2), 198-226; https://doi.org/10.3390/jrfm8020198 - 07 Apr 2015

Cited by 10 | Viewed by 5195

Abstract

This paper investigates the dynamic evolution of tail risk interdependence among U.S. banks, financial services and insurance sectors. Life and non-life insurers have been considered separately to account for their different characteristics. The tail risk interdependence measurement framework relies on the multivariate Student-t [...] Read more.

This paper investigates the dynamic evolution of tail risk interdependence among U.S. banks, financial services and insurance sectors. Life and non-life insurers have been considered separately to account for their different characteristics. The tail risk interdependence measurement framework relies on the multivariate Student-t Markov switching (MS) model and the multiple-conditional value-at-risk (CoVaR) (conditional expected shortfall (CoES)) risk measures introduced in Bernardi et al. (2013), accounting for both the stylized facts of financial data and the contemporaneous multiple joint distress events. The Shapley value methodology is then applied to compose the puzzle of individual risk attributions, providing a synthetic measure of tail interdependence. Our empirical investigation finds that banks appear to contribute more to the tail risk evolution of all of the remaining sectors, followed by the financial services and the insurance sectors, showing that the insurance sector significantly contributes as well to the overall risk. We also find that the role of each sector in contributing to other sectors’ distress evolves over time according to the current predominant financial condition, implying different interdependence strength. Full article

(This article belongs to the Special Issue Financial Risk Modeling and Forecasting)

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248 KiB

Open AccessArticle

The Impact of the Basel Accord on Greek Banks: A Stress Test Study

by John Leventides and Anna Donatou

J. Risk Financial Manag. 2015, 8(2), 181-197; https://doi.org/10.3390/jrfm8020181 - 31 Mar 2015

Viewed by 4931

Abstract

In this paper, we study the impact of extreme events on the loan portfolios of the Greek banking system. These portfolios are grouped into three separate groups based on the size of the bank to which they belong, in particular, large, medium, and [...] Read more.

In this paper, we study the impact of extreme events on the loan portfolios of the Greek banking system. These portfolios are grouped into three separate groups based on the size of the bank to which they belong, in particular, large, medium, and small size. A series of extreme scenarios was performed and the increase in capital requirements was calculated for each scenario based on the standardized and internal ratings approach of the Basel II accord. The results obtained show an increase of credit risk during the crisis periods, and the differentiation of risk depending on the size of the banking organization as well as the added capital that will be needed in order to hedge that risk. The execution of the scenarios aims at studying the effects which may be brought about on the capital of the three representative banks by the appearance of adverse events. Full article

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J. Risk Financial Manag., Volume 8, Issue 2 (June 2015) – 4 articles , Pages 181-284

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