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Research Interests

ESG and Sustainable Finance

Vine Copula  Modeling and Risk Analysis

Network Analysis and Graphical Models

Statistics and Financial Econometrics 


Do Lower ESG Rated Companies Have Higher Systemic Impact? Empirical Evidence from Europe and the United States.

With Bonaccolto, Giovanni & Paterlini, Sandra (2023)

Status: Currently under review

Available at SSRN 4147370


In the recent years, companies have increasingly been characterized by environmental, social and governance (ESG) scores and investors like academics have raised questions concerning financial performance and investment risks. Now as the EBA has acknowledged that ESG risks can have a potential impact on the financial system, the debate of systemic risk has risen. While understanding the relationship between ESG merit and systemic risk is of utmost important for the stability of the financial system, still only scarce knowledge exists. Relying on real world European and American data, we quantify the systemic risk impact by means of QL-CoVaR. Empirical analysis on the entire period from 2007-2021 show that companies with a high ESG score tend to exhibit lower QL-CoVaR values than lower rated companies indicating a positive effect of ESG scores. Such evidence is also confirmed by clustering the individual companies into ESG portfolios, and becomes clearer focusing on COVID-19.

A generalized precision matrix for t-Student distributions in portfolio optimization

With Taufer, Emanuele & Paterlini, Sandra (2022)

Status: Currently under review

Available at SSRN 4063255

In particular, when focusing on the minimum-variance portfolio, the covariance matrix or better its inverse, the so-called precision matrix, is the only input required. Technically, the precision matrix can be used to understand the conditional dependence structure of random vectors. However, the inverse of the covariance matrix might not necessarily result in a reliable and accurate picture of reality when non-Gaussian settings are analyzed. In this paper, exploiting the local dependence function, different definitions of the generalized precision matrix, which holds for a general class of distributions, are provided. In particular, we focus on the multivariate t-Student distribution and point out that the interaction in random vectors does not depend only on the inverse of the covariance matrix, but also on additional elements. We test the performance using a minimum-variance portfolio approach by considering S\&P 100 and Fama and French industry data. We show that portfolios using the generalized precision matrix often generate statistically significantly lower out-of-sample variances than state-of-art methods.

ESG, Risk, and (tail) dependence

With Sahin, Özge, Czado, Claudia & Paterlini, Sandra (2021)

Status: Currently under review

Available at SSRN 3846739

While environmental, social, and governance (ESG) trading activity has been a distinctive feature of financial markets, the debate if ESG scores can also convey information regarding a company's riskiness remains open. Regulatory authorities, such as the European Banking Authority (EBA), have acknowledged that ESG factors can contribute to risk. Therefore, it is important to model such risk dependencies and quantify what part of a company's riskiness can be attributed to the ESG scores. This paper aims to question whether ESG scores can be used to provide information on (tail) riskiness. By analyzing the (tail) dependence structure of companies with a range of ESG scores, that is within an ESG rating class, using high-dimensional vine copula modelling, we are able to show that risk can also depend on and be directly associated with a specific ESG rating class. Empirical findings on real-world data show positive not negligible ESG risks determined by ESG scores, especially during the 2008 crisis.

The pitfalls of (non-definitive) Environmental, Social, and Governance scoring methodology

With Sahin, Özge, Czado, Claudia & Paterlini, Sandra (2021)

Status: Currently under review

Available at SSRN 4020354

Evaluating companies’ sustainability embraces environmental, social, and gover-nance (ESG) activities. Data providers assign companies ESG scores as a quantitantive measure based on available information. Refinitiv (previously ASSET4) is a key data provider whose scores are used extensively by researchers. However, their ESG scoring methodology allows the ESG scores of the five most recent years to change post-publication. Such ESG scores are called non-definitive. Then, ESG research findings using the data from the same data provider might be inconsistent. By optimization and exploratory data-mining approaches, we show that it is possible to change ESG scores to exhibit stronger risk dependence. Additionally, we discuss that the initial disclosure of ESG information and an update in the published ESG information alter how ESG scores are computed in a given industry group, greatly impacting ESG research findings. Finally, our work points out the criticality that should be addressed to improve comparability within research studies relying on the same data providers.

Published Work

Sahin, Ö, Bax, K.,  Paterlini, S., & Czado, C. (2023). ESGM: ESG scores and the Missing pillar - Why does missing information matter?. Corporate Social Responsibility and Environmental Management..

DOI: 10.1002/csr.2326

Bax, K. & Paterlini, S. (2022). Environmental Social Governance Information and Disclosure from a Company Perspective: a Structured Literature Review. International Journal of Business Performance Management, 23(3), p. 304–322. DOI: 10.1504/IJBPM.2022.10047578

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