A Network Analysis of Global Banking:1978-2009

Reyes, Javier A.; Minoiu, Camelia

doi:10.5089/9781455227051.001

Cited by 22 publications

(10 citation statements)

References 41 publications

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“…Although systemic risk build-up is inevitable, financial markets are allocating capital and risk more efficiently. Using similar dataset, Minoiu and Reyes (2011) analysed interconnectivity of the global banking network for 184 countries over 1984-2009. They enhanced existing literatures on network study through the use of cross-border flows data to reflect liquidity conditions and applied the weighted degree approach to capture the heterogeneity in cross-border exposures.…”

Section: Network Analysismentioning

confidence: 99%

“…Following the footsteps of Minoiu and Reyes (2011), we applied the same treatment on the bilateral interbank exposures data by retaining only positive flows in this study (corresponding to net placements or net assets), while negative flows (net liabilities) are replaced with zeros. The total claims and liabilities are not differentiated according to currency (domestic currency (i.e.…”

Section: The Seacen Centrementioning

confidence: 99%

“…The next part of their research is based on the VNA's methodological framework of Minoiu and Reyes (2011). In turn, the counterfactual simulation analysis is based on Battiston et al (2012).…”

Section: Introductionmentioning

confidence: 99%

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Cross-Border Interbank Contagion Risk Analysis

Matoušek

Rummel²

2020

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Since the early 1990s, we have observed unprecedented changes in the global financial architecture. In particular, international financial markets have become highly volatile and closely interconnected. It is evident that since 1997, cross-border financial integration in Asian emerging market economies has significantly accelerated and become rather complex. In this study, the project team members attempt to explore the effects of cross-border bank activites on contagion risk in the selected Asian-Pacific countries. The analysis then evaluates scenarios of potential triggers of cross-border interbank contagion risk in the Asia-Pacific region.

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Section: Network Analysismentioning

confidence: 99%

Section: The Seacen Centrementioning

confidence: 99%

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Cross-Border Interbank Contagion Risk Analysis

Matoušek

Rummel²

2020

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“…Minoiu and Reyes [60] have analyzed the global banking network from 1978 to 2009. They have applied network metrics such as centrality, connectivity, and clustering for analyzing financial interconnectedness.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, by applying Random Matrix Theory on bilateral locational statistics data provided by the Bank for International Settlements (BIS) [70] from 1978 until 2019, we aim to analyze the global banking network. This data includes all 'core' countries (the qualifier 'core' is used by many researchers such as [60], for countries which regularly report their financial data to BIS).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Global Banking Network by Random Matrix Theory

et al. 2021

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Since the financial crisis of 2008, the network analysis of financial systems has attracted a lot of attention. In this paper, we analyze the global banking network via the method of Random Matrix Theory. By applying that method on a cross border lending network, it is shown that while the connectivity between different parts of the network has risen and the profile of transactions has diversified, the role of hubs remains important in the weighted perspective. The largest eigenvalue of the transaction matrix as the leading mode of the system shows sharp growth since 2002. As well, it is observed that its growth has diminished since 2008. This indicates that the crisis of 2008 has left a long-lasting footprint on the financial system. Analyzing the mean value of the participation ratio reveals the fact that the role of countries in forming small modes, has increased since 2002. In our final analysis, we provide snapshots of the hubs in the network over time. We observe that the share of countries in total transactions is not equal to their share in shaping the eigenvector of the largest eigenvalue. In 2018 for example, while the United Kingdom leads the share of transactions, it is the United States that has the largest value in the leading eigenvector. The proposed technique in the paper can be useful for analyzing different types of interaction networks between countries.

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Machine Learning for Financial Stability

Alessi

Savona

2021

Data Science for Economics and Finance

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What we learned from the global financial crisis is that to get information about the underlying financial risk dynamics, we need to fully understand the complex, nonlinear, time-varying, and multidimensional nature of the data. A strand of literature has shown that machine learning approaches can make more accurate data-driven predictions than standard empirical models, thus providing more and more timely information about the building up of financial risks. Advanced machine learning techniques provide several advantages over empirical models traditionally used to monitor and predict financial developments. First, they are able to deal with high-dimensional datasets. Second, machine learning algorithms allow to deal with unbalanced datasets and retain all of the information available. Third, these methods are purely data driven. All of these characteristics contribute to their often better predictive performance. However, as “black box” models, they are still much underutilized in financial stability, a field where interpretability and accountability are crucial.

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A Network Analysis of Global Banking:1978-2009

Cited by 22 publications

References 41 publications

Cross-Border Interbank Contagion Risk Analysis

Cross-Border Interbank Contagion Risk Analysis

Analysis of the Global Banking Network by Random Matrix Theory

Machine Learning for Financial Stability

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