top of page

Research Interest

ESG and Sustainable Finance

Vine Copula  Modeling and Risk Analysis

Statistics and Financial Econometrics

Large Language Models and Textual Analysis

Working Papers

Chasing ESG Performance: Revealing the Impact of Refinitiv's Scoring System

With Benuzzi, Matteo, Taufer, Emanuele and Paterlini, Sandra (2024)

Status: Under Review

Available at SSRN 4662257

ESG metrics, pivotal to sustainable finance, face intensified scrutiny for precision and representativity. Recent concerns stem from potential retroactive score adjustments and data aggregation, risking misrepresentation of company performance. We find that Refinitiv’s rating methodology may obscure genuine company progress by artificially inflating high-ranking firms’ scores with new, lower-performing entrants while diminishing actual advancements due to peer competition. Our analysis, spanning 2012-2021 across three key sectors, indicates only 41% of score variation relates to company disclosures. To rectify these issues, we propose replacing Refinitiv’s percentile ranking with the ’performance ratio’ scoring method. Our analysis reveals a significant correlation between Refinitiv’s current approach and our performance ratio method. However, the latter is less affected by new entrants and peer comparison, maintaining robust- ness against outliers. Our findings underscore the necessity for a refined ESG scoring system aligning more accurately with corporate sustainability efforts and actual representation of the underlying data.

A generalized precision matrix for non-Gaussian multivariate distributions with applications to portfolio optimization

With Taufer, Emanuele and Paterlini, Sandra (2023)

Status: Under Review

Available at SSRN 4063255

We introduce the concept of Generalized Precision Matrix (GPM), based on a general measure of dependence, which might be valid for any statistical distribution. Beside showing that in the Gaussian case, the GPM coincides with the inverse of the covariance matrix, we derive the GPM analytically for the multivariate t, multivariate skew-normal and multivariate skew-t distributions, moving beyond Gaussianity. Therefore, we argue that using the derived GPMs might be preferable when data show asymmetry and heavy tails, supporting our claim through simulation analysis. As financial times series are leptokurtic, we propose then an application to the Markowitz minimum variance portfolio, which exhibits superior fitting of the multivariate skew-t model during crisis periods.


Published Papers

Bax, K., Bonaccolto, G. & Paterlini, S. (2024). Spillovers in Europe: the Role of ESG. Journal of Financial Stability 72(2024, 101221).

DOI: 10.1016/j.jfs.2024.101221

Bax, K., Broccardo, E. & Paterlini, S. (2024). Environmental, social, and governance factor and financial returns: what is the relationship? Investigating environmental, social, and governance factor models. Current Opinion in Environmental Sustainability 66 (2024, 101398).

DOI: 10.1016/j.cosust.2023.101398

Benuzzi, M., Klaser, K. & Bax, K. (2023). Which ESG+F dimension matters most to private investors? An experimental study on financial decisions and the future generations. Journal of Behavioral and Experimental Finance 21(2024, 100882).

DOI: 10.1016/j.jbef.2023.100882

Bax, K., Müller, S. & Paterlini, S. (2023). Sustainability Transmission through Focal Nodes in Supply Chain Networks. Finance Research Letters 58(December 2023).

DOI: 10.1016/

Sommer, E., Bax, K. & Czado, C. (2023). Vine Copula based Portfolio Level Conditional Risk Measure Forecasting. Econometrics and Statistics.

DOI: 10.1016/j.ecosta.2023.08.002

Bax, K., Sahin, Ö., Czado, C. & Paterlini, S. (2023). ESG, Risk, and (Tail) Dependence. International Review of Financial Analysis. 87(May 2023).

DOI: 10.1016/j.irfa.2023.102513

Bax, K. (2023). Do diverse and inclusive workplaces benefit investors?  An Empirical Analysis on Europe and the United States. Finance Research Letters 53(March 2023).

DOI: 10.1016/

Bax, K., Bonaccolto, G., & Paterlini, S. (2022). Do Lower ESG Rated Companies Have Higher Systemic Impact? Empirical Evidence from Europe and the United States. Corporate Social Responsibility and Environmental Management 30(3), p. 1406-1420.

DOI: 10.1002/csr.2427

Czado, C., Bax, K., Sahin, Ö., Nagler, T., Min, A., & Paterlini, S. (2022). Vine copula based dependence modeling in sustainable finance. Journal of Finance and Data Science (JFDS) 8(November 2022).

DOI: 10.1016/j.jfds.2022.11.003

Sahin, Ö, Bax, K.,  Paterlini, S., & Czado, C. (2022). The pitfalls of (non-definitive) Environmental, Social, and Governance scoring methodology. Global Finance Journal 56(May 2023).

DOI: 10.1016/j.gfj.2022.100780

Sahin, Ö, Bax, K.,  Paterlini, S., & Czado, C. (2022). ESGM: ESG scores and the Missing pillar - Why does missing information matter?. Corporate Social Responsibility and Environmental Management 29(5), p. 1782-1798.

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.2022.123824

bottom of page