Robust and Sparse Banking Network Estimation

Torri, Gabriele; Giacometti, Rosella; Paterlini, Sandra

doi:10.2139/ssrn.3024537

Cited by 3 publications

(3 citation statements)

References 40 publications

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“…We deal with the curse of dimensionality using a well-known variable selection tool, namely, the Least Absolute Shrinkage and Selection Operator (LASSO) introduced by Tibshirani (1996) and widely employed in economic and financial applications (e.g. Torri et al, 2018;Pun and Wong, 2019;Cui et al, 2020).…”

Section: Penalized Cares-x Model and Network Estimationmentioning

confidence: 99%

Dynamic large financial networks via conditional expected shortfalls

Bonaccolto

Caporin

Maillet

2022

European Journal of Operational Research

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In this article, we first generalize the Conditional Auto-Regressive Expected Shortfall (CARES) model by introducing the loss exceedances of all (other) listed companies in the Expected Shortfall related to each firm, thus proposing the CARES-X model (where the 'X', as usual, stands for eXtended in the case of large-dimensional problems). Second, we construct a regularized network of US financial companies by introducing the Least Absolute Shrinkage and Selection Operator in the estimation step. Third, we also propose a calibration approach for uncovering the relevant edges between the network nodes, finding that the estimated network structure dynamically evolves through different market risk regimes. We ultimately show that knowledge of the extreme risk network links provides useful information, since the intensity of these links has strong implications on portfolio risk. Indeed, it allows us to design effective risk management mitigation allocation strategies.

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Section: Penalized Cares-x Model and Network Estimationmentioning

confidence: 99%

Dynamic large financial networks via conditional expected shortfalls

Bonaccolto

Caporin

Maillet

2022

European Journal of Operational Research

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“…Therefore, it is not properly unexpected that several studies deal with systemic risk problems in the framework of complex networks (see e.g. [5], [13], [15], [23], [24] [32], [35]).…”

Section: Introductionmentioning

confidence: 99%

Systemic risk assessment through high order clustering coefficient

2020

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In this article we propose a novel measure of systemic risk in the context of financial networks.To this aim, we provide a definition of systemic risk which is based on the structure, developed at different levels, of clustered neighbours around the nodes of the network. The proposed measure incorporates the generalized concept of clustering coefficient of order l of a node i introduced in [10]. Its properties are also explored in terms of systemic risk assessment. Empirical experiments on the time-varying global banking network show the effectiveness of the presented systemic risk measure and provide insights on how systemic risk has changed over the last years, also in the light of the recent financial crisis and the subsequent more stringent regulation for globally systemically important banks.

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“…Moreover, in absence of granular bilateral data, other works aim to estimate the network structure using the market prices of traded securities. Examples are Billio et al (2012) that use Granger-causality networks to identify and forecast financial instability periods, Diebold and Yılmaz (2014) that consider variance decomposition to track the interconnectedness of major US companies, and Anufriev and Panchenko (2015) and Torri et al (2018) that rely on partial correlation networks to characterize the structure of the banking systems. The adoption of network models is high on the agenda of regulators: the Basel Committee on Banking Supervision (in the following BCBS) has identified the "excessive interconnectedness among systemically important banks" as one of the weakness of the European financial system and it has made addressing the systemic risk created by this interconnectedness a cornerstone of Basel III.…”

Section: Introductionmentioning

confidence: 99%

Risk attribution and interconnectedness in the EU via CDS data

Giacometti

Torri

Farina³

et al. 2020

Comput Manag Sci

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The global financial crisis in 2008, and the European sovereign debt crisis in 2010, highlighted how credit risk in banking sectors cannot be analysed from a uniquely micro-prudential perspective, focused on individual institutions, but it has instead to be studied and regulated from a macro-prudential perspective, considering the banking sector as a complex system. Traditional risk management tools often fail to account for the complexity of the interactions in a financial system, and rely on simplistic distributional assumptions. In recent years machine learning techniques have been increasingly used, incorporating tools such as text mining, sentiment analysis, and network models in the risk management processes of financial institutions and supervisors. Network theory applications in particular are increasingly popular, as they allow to better model the intertwined nature of financial systems. In this work we set up an analytical framework that allows to decompose the credit risk of banks and sovereign countries in the European Union according to systematic (system-wide and regional) components. Then, the non-systematic components of risk are studied using a network approach, and a simple stress-test framework is set up to identify the potential transmission channels of distress and risk spillovers. Results highlight a relevant component of credit risk that is not explained by common factors, but can still be a potential vehicle for the transmission of shocks. We also show that due to the properties of the network structure, the transmission of shocks applied to different institutions is quite diversified, both in terms of breadth and speed. Our work is useful to both regulators and financial institutions, thanks to its flexibility and its requirement of data that can be easily available.

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Robust and Sparse Banking Network Estimation

Cited by 3 publications

References 40 publications

Dynamic large financial networks via conditional expected shortfalls

Dynamic large financial networks via conditional expected shortfalls

Systemic risk assessment through high order clustering coefficient

Risk attribution and interconnectedness in the EU via CDS data

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