Robust and sparse banking network estimation

Torri, Gabriele; Giacometti, Rosella; Paterlini, Sandra

doi:10.1016/j.ejor.2018.03.041

Cited by 49 publications

(25 citation statements)

References 61 publications

(97 reference statements)

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“…The lack of bilateral financial exposure data has also led to simulations of financial networks using several assumptions. Examples are entropy maximisation methods (Castrén and Rancan 2014;Mistrulli 2011), correlations or Granger causality methods based on stock price data (see Billio et al 2012;Diebold and Yılmaz 2014;Hautsch et al 2015) and sparse network reconstruction models (Torri et al 2018;Anand et al 2015;Mahdavi Ardekani et al 2020). Simaan et al (2020) proposes a quite novel approach to estimate hidden networks from the interbank market using correlation-based statistical filtering methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Early warning of systemic risk in global banking: eigen-pair R number for financial contagion and market price-based methods

et al. 2021

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We analyse systemic risk in the core global banking system using a new network-based spectral eigen-pair method, which treats network failure as a dynamical system stability problem. This is compared with market price-based Systemic Risk Indexes, viz. Marginal Expected Shortfall, Delta Conditional Value-at-Risk, and Conditional Capital Shortfall Measure of Systemic Risk in a cross-border setting. Unlike paradoxical market price based risk measures, which underestimate risk during periods of asset price booms, the eigen-pair method based on bilateral balance sheet data gives early-warning of instability in terms of the tipping point that is analogous to the R number in epidemic models. For this regulatory capital thresholds are used. Furthermore, network centrality measures identify systemically important and vulnerable banking systems. Market price-based SRIs are contemporaneous with the crisis and they are found to covary with risk measures like VaR and betas.

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Section: Literature Reviewmentioning

confidence: 99%

Early warning of systemic risk in global banking: eigen-pair R number for financial contagion and market price-based methods

et al. 2021

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“…For scale-free, random, band, cluster and hub we use the R package huge (setting v = 0.3, u = 0.1) that allows to directly generate precision matrices with a given sparsity structure. Instead, for the small-world and core-periphery, we use the algorithms in [31] and [30] respectively to generate the sparsity pattern, then we utilize the procedure suggested in huge package to produce the precision matrices. Given a precision matrix Θ, we use it to generate n vectors of observations from the following four multivariate distributions:…”

Section: Simulation Set-upmentioning

confidence: 99%

“…For example, financial networks have received a renewed attention in the aftermath of the recent global financial crisis. Several empirical studies have focused their attention on the estimation and analysis of these networks; see, for example, Barigozzi and Brownlees [5], Bilio et al [7], Dermier et al [11] and Torri et al [30]. In this paper, we utilize 2Stelnet to estimate the relationships among a large set of important European banks, for the period 2018-2020.…”

Section: Introductionmentioning

confidence: 99%

A 2-stage elastic net algorithm for estimation of sparse networks with heavy tailed data

Bernardini¹,

Paterlini²,

Taufer³

2021

Preprint

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We propose a new 2-stage procedure that relies on the elastic net penalty to estimate a network based on partial correlations when data are heavy-tailed. The new estimator allows to consider the lasso penalty as a special case. Using Monte Carlo simulations, we test the performance on several underlying network structures and four different multivariate distributions: Gaussian, t-Student with 3 and 20 degrees of freedom and contaminated Gaussian. Simulation analysis shows that the 2-stage estimator performs best for heavy-tailed data and it is also robust to distribution misspecification, both in terms of identification of the sparsity patterns and numerical accuracy. Empirical results on real-world data focus on the estimation of the European banking network during the Covid-19 pandemic. We show that the new estimator can provide interesting insights both for the development of network indicators, such as network strength, to identify crisis periods and for the detection of banking network properties, such as centrality and level of interconnectedness, that might play a relevant role in setting up adequate risk management and mitigation tools.

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“…Its occurrence has a sizable direct impact on the economic system as well as on portfolio management. In the aftermath of the subprime mortgage crisis (2007)(2008)(2009) and the European credit risk crisis (autumn-winter 2011), systemic risk is receiving increased attention in financial literature (Acharya, Pedersen, Philippon, & Richardson, 2017;Ahnert & Georg, 2017;Black, Correa, Huang, & Zhou, 2016;Tasca, Battiston, & Deghi, 2017;Torri, Giacometti, & Paterlini, 2018) and among regulators (ESRB, 2018;IMF, 2015). Thus far, the literature has focused on understanding how to capture systemic risk stemming from the interconnectedness between financial institutions' primary banks, which are seen as "too interconnected to fail" (Gofman, 2017;Merkose, Giansante, & Shaghaghi, 2012;Roukney, Battiston, & Stiglitz, 2018).…”

Section: Introductionmentioning

confidence: 99%

Forecasting systemic risk in portfolio selection: The role of technical trading rules

Kouaissah

Hocine

2020

Journal of Forecasting

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This paper proposes and implements methods for determining whether incorporating technical trading rules accurately forecasts systemic risk and improves the performance of out-of-sample portfolios. The proposed methodology considers various trading rules for forecasting and addressing potential systemic risk in portfolio selection problems. The method incorporates major trading rules as early warning systems or alarm rules to detect market failure within diverse reward-risk measures. Methodologically, the alarm rules are integrated into portfolio selection strategies that predict returns using multifactor models. Therefore, the portfolio strategies combine the predictive ability of both technical trading rules and multifactor models. Empirical analyses validate the suggested approaches and evaluate the impacts of different technical trading rules on portfolio selection problems. This paper compares the ex ante sample paths of several portfolio strategies aiming to maximize portfolio wealth using either reward-risk or drawdown-based performance measures. The results show that the proposed methodologies outperform the classic approach in terms of out-of-sample performance.

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Robust and sparse banking network estimation

Cited by 49 publications

References 61 publications

Early warning of systemic risk in global banking: eigen-pair R number for financial contagion and market price-based methods

Early warning of systemic risk in global banking: eigen-pair R number for financial contagion and market price-based methods

A 2-stage elastic net algorithm for estimation of sparse networks with heavy tailed data

Forecasting systemic risk in portfolio selection: The role of technical trading rules

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