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Network Analysis of the Financial Sector: A Comprehensive Perspective with Adaptive Joint LASSO Method

Authors: Badics, Milán Csaba;

Network Analysis of the Financial Sector: A Comprehensive Perspective with Adaptive Joint LASSO Method

Abstract

The emergence of systemic risk from the 2008 Global Financial Crisis (GFC) and the recent energy market shocks following the COVID-19 pandemic and the onset of the Russo-Ukrainian war highlighted the importance of understanding shock spillovers for policymakers and academics alike. Specifically, shock transmissions in financial network settings warrant attention to support regulatory or policy interventions for effectively mitigating or preventing the transmission of systemic risk. The thesis focuses on this important topic from various angles to answer the following questions: • How can machine learning techniques help to improve financial network studies, the analysis of high dimensional time series? • How can structural changes in financial networks efficiently examined using event analysis framework? My thesis provides methodological and conceptual contributions to financial network analysis. The contribution of my thesis to the existing literature is threefold: 1. I propose a new regularization method, the adaptive joint least absolute shrinkage and selection operator (AJ LASSO). The innovation in this method is that it accounts for possible sparsity both in the coefficient and the covariance matrix of the estimated Vector autoregressive (VAR) model. The method is especially suitable for high-dimension network analysis, where parameter estimation is a critical issue. 2. I extend the Diebold-Yilmaz (DY) framework with an event study tool to facilitate an in-depth analysis of the contagion channels during critical local and global shocks in financial networks. Integrating a moving-block bootstrap method (MBB) into the framework, I can investigate how the observed shocks transform the financial networks. 3. My conceptual contribution is that I characterize and analyze illiquidity networks. The liquidity concerns are globally recognized and the focal point of the newly revised Basel IV regulatory guidelines; thus, intuitively and from a regulatory perspective, illiquidity spillover analysis in the financial sector is valuable. I analyze the illiquidity connectedness of financial institutions (FIs) and show that the illiquidity network better tracks the dominant shock transmitters in the system during financial turmoil than the volatility-based networks. I show that the DY framework extended with the MBB method is a powerful tool to identify troubled financial institutions and contagion channels.

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Hungary
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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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