Simulating Financial Contagion Dynamics in Random Interbank Networks

Leventides, John; Loukaki, Kalliopi; Papavassiliou, V.

doi:10.2139/ssrn.3243061

Cited by 4 publications

(4 citation statements)

References 30 publications

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“…Recent studies on spillover effects and contagion (e.g., Apergis et al, 2019 ; Bayona & Peia, 2020 ; Bhanot et al, 2014 ; Bucci, Torre La, Liuzzi, & Marsiglio, 2019 ; Kalbaska & Gatkowski, 2012 ; Leventides, Loukaki, & Papavassiliou, 2019 ; Piccotti, 2017 ; Reboredo et al, 2016 ) tested various modifications of covolatility approaches to check for cross-country spillover effects. In the same framework, studies as Dungey et al (2020) , Guidolin, Hansen, and Pedio (2019) , Collet and Ielpo (2018) , Alter and Beyer (2014) , Claeys and Vasicek (2014) , and Jiang, Konstantinidi, and Skiadopoulos (2012) focused on VAR models to conclude for financial contagion.…”

Section: Literature Reviewmentioning

confidence: 99%

Covid-19 pandemic and spillover effects in stock markets: A financial network approach

Samitas

Kampouris

Polyzos

2022

International Review of Financial Analysis

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This paper examines the impact of the COVID-19 pandemic on 51 major stock markets, both emerging and developed. We isolated the countries susceptible to shock transmissions, and evaluated countries with immunity, during the lockdown. Specifically, using dependence dynamics and network analysis on a bivariate basis, we identify volatility and contagion risk among stock markets during the COVID-19 pandemic. The empirical findings add to the existing body of literature, given that previous work has not placed emphasis on network topologic metrics when it comes to financial networks, specifically during the COVID-19. The evidence shows instant financial contagion a result of the lockdown and the spread of the novel coronavirus. The methodological framework outlines important information for investors and policymakers on using financial networks to improve portfolio selection, by placing an emphasis on assets according to centrality.

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Section: Literature Reviewmentioning

confidence: 99%

Covid-19 pandemic and spillover effects in stock markets: A financial network approach

Samitas

Kampouris

Polyzos

2022

International Review of Financial Analysis

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“…Simulations show financial networks have a "robust-yet-fragile tendency": though the chance of contagion is low, the effects of a problem can be substantial (Gai and Kapadia 2010). Leventides et al (2019) conclude that heterogeneity in bank sizes and interbank exposures play a significant role in the stability of the financial system since they enhance the system's ability to absorb shocks. In addition, the degree of interconnectedness of the system has a significant impact on its resilience, particularly in case of smaller and highly interconnected interbank networks.…”

Section: Financial Contagionmentioning

confidence: 97%

A Deep Learning Approach to Dynamic Interbank Network Link Prediction

Zhang

2022

IJFS

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Lehman Brothers’ failure in 2008 demonstrated the importance of understanding interconnectedness in interbank networks. The interbank market plays a significant role in facilitating market liquidity and providing short-term funding for each other to smooth liquidity shortages. Knowing the trading relationship could also help understand risk contagion among banks. Therefore, future lending relationship prediction is important to understand the dynamic evolution of interbank networks. To achieve the goal, we apply a deep learning framework model of interbank lending to an electronic trading interbank network for temporal trading relationship prediction. There are two important components of the model, which are the Graph convolutional network (GCN) and the Long short-term memory (LSTM) model. The GCN and LSTM components together capture the spatial–temporal information of the dynamic network snapshots. Compared with the Discrete autoregressive model and Dynamic latent space model, our proposed model achieves better performance in both the precrisis and the crisis period.

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“…A couple of them are discussed in this paragraph. Firstly, Leventides et al (2019) evaluated the resilience of financial systems to exogenous shocks, deploying the techniques from the theory of complex networks. The fragility of several network topologies was investigated by means of Monte Carlo simulation using a simple default model of contagion applied on interbank networks of varying sizes.…”

Section: Empirical Literaturementioning

confidence: 99%

Examining the Effect of Financial Markets Shocks on Financial Stability in South Africa

Hlongwane

Sheefeni²

2022

IJEFI

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The paper analyzed the impact of financial market shocks on financial market stability. The goal was achieved by employing quarterly time-series data spanning from 2003:Q1 to 2020:Q4. The study used various econometric techniques such as stationarity, determining optimal lag length, cointegration analysis, estimating a vector error correction model, impulse response functions and forecast error variance decomposition. Following this, the long run relationship amongst the variables was established. The findings revealed that inflation has a negative impact on financial stability in both the short and long run. Lastly, it was only the shocks in economic activities that was found to have a significant impact on financial stability.

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Simulating Financial Contagion Dynamics in Random Interbank Networks

Cited by 4 publications

References 30 publications

Covid-19 pandemic and spillover effects in stock markets: A financial network approach

Covid-19 pandemic and spillover effects in stock markets: A financial network approach

A Deep Learning Approach to Dynamic Interbank Network Link Prediction

Examining the Effect of Financial Markets Shocks on Financial Stability in South Africa

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