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

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 us to consider the LASSO penalty as a special case. Extensive 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. [ FROM AUTHOR] Copyright of Journal of Statistical Computation & Simulation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Do lower environmental, social, and governance (ESG) rated companies have higher systemic impact? Empirical evidence from Europe and the United States

In recent years, companies have increasingly been characterized by environmental, social, and governance (ESG) scores, and investors and academics have raised questions concerning financial performance and investment risks. Now, as the European Banking Authority has acknowledged that ESG risks can potentially impact the economic and financial system, the debate on systemic risk has gained traction. Understanding the relationship between ESG merit and systemic risk is of utmost importance for the stability of the economic and financial system, still, research is limited. Relying on real‐world European and United Stated data, we quantify systemic risk by means of QL‐CoVaR. Empirical analyses of the entire period from 2007 to 2021 show that companies with high ESG scores tend to exhibit low QL‐CoVaR values indicating a positive effect of ESG scores. Such evidence is confirmed by clustering the individual companies into ESG portfolios and focusing on COVID‐19. Additional insights using the individual pillars are also provided.

Do lower environmental, social, and governance (ESG) rated companies have higher systemic impact? Empirical evidence from Europe and the United States

In recent years, companies have increasingly been characterized by environmental, social, and governance (ESG) scores, and investors and academics have raised questions concerning financial performance and investment risks. Now, as the European Banking Authority has acknowledged that ESG risks can potentially impact the economic and financial system, the debate on systemic risk has gained traction. Understanding the relationship between ESG merit and systemic risk is of utmost importance for the stability of the economic and financial system, still, research is limited. Relying on real-world European and United Stated data, we quantify systemic risk by means of QL-CoVaR. Empirical analyses of the entire period from 2007 to 2021 show that companies with high ESG scores tend to exhibit low QL-CoVaR values indicating a positive effect of ESG scores. Such evidence is confirmed by clustering the individual companies into ESG portfolios and focusing on COVID-19. Additional insights using the individual pillars are also provided.

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

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 us to consider the LASSO penalty as a special case. Extensive 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. [ FROM AUTHOR] Copyright of Journal of Statistical Computation & Simulation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

New estimation approaches for graphical models with elastic net penalty

In the context of undirected Gaussian graphical models, three estimators based on elastic net penalty for the underlying dependence graph are introduced. The aim is to estimate a sparse precision matrix, from which to retrieve both the underlying conditional dependence graph and the partial correlations. The first estimator is derived from the direct penalization of the precision matrix in the likelihood function, while the second uses penalized regressions to estimate the precision matrix. Finally, the third estimator relies on a two stage procedure that estimates the edge set first and then the precision matrix elements. Through simulations the performances of the proposed methods are investigated on a set of well-known network structures. Results on simulated data show that in high-dimensional situations the second estimator performs relatively well, while in low-dimensional settings the two stage procedure outperforms most estimators as the sample size grows. Nonetheless, there are situations where the first estimator is also a good choice. Mixed results suggest that the elastic net penalty is not always the best choice when compared to the LASSO penalty, i.e. pure ℓ1 penalty, even if elastic net penalty tends to outperform LASSO in presence of highly correlated data from the cluster structure. Finally, using real-world data on U.S. economic sectors, dependencies are estimated and the impact of Covid-19 pandemic on the network strength is studied.

Dynamic network analysis of North American financial institutions

We propose a state-space model to estimate the dynamic network among financial institutions selected from STOXX600 North America in the period from January 2005 to May 2020. We measure the network strength and find that the spillover effect increases significantly during the 2008 financial crisis and the coronavirus pandemic. Using weekly updates of the weight matrix, we detect four time-varying communities. Three communities mostly include companies of the financial supersectors, while the remaining includes Canadian companies. Furthermore, the communities centralities peak during the 2008 financial crisis, while during the COVID-19 period lower values are estimated.