ESG and Sustainable Finance
Vine Copula Modeling and Risk Analysis
Network Analysis and Graphical Models
Statistics and Financial Econometrics
Spillovers in Europe: the Role of ESG
With Bonaccolto, Giovanni & Paterlini, Sandra (2023)
Status: Currently under review
This paper explores the relationship between environmental, social and governance (ESG) information and systemic risk, an increasingly important issue for both regulators and investors. While ESG ratings are widely used to assess a company's non-financial performance, the impact of these factors on financial stability and systemic risk is still under debate. By extending the Forecast Error Variance Decomposition (FEVD) method with a double regularization on both the underlying vector autoregressive (VAR) parameters and the covariance matrix of the VAR residuals, we are able to address the curse of dimensionality within each estimation. This allows us to examine how vulnerable a company is and how much systemic impact a company has given its specific ESG. Looking at a larger sample of European stocks over the period 2007-2022, we empirically show that both the best and worst ESG performers have the largest impact on the financial system in normal times. However, during a crisis, companies with the best ESG ratings generate significant spillovers throughout the system. These findings highlight the importance of incorporating ESG factors into systemic risk assessments and monitoring companies' ESG performance to ensure financial stability. Policymakers can benefit from this research by supporting investment in high ESG companies to mitigate relevant spillovers during stressed market conditions, when such companies are more interconnected.
A generalized precision matrix for t-Student distributions in portfolio optimization.
With Taufer, Emanuele & Paterlini, Sandra (2023)
Status: Currently under review
The Markowitz model remains the foundation of modern portfolio theory. Specifically, when aiming to construct a minimum-variance portfolio, only the covariance matrix - or its inverse, the precision matrix - is needed as input. However, little attention has been given to the limitations of this approach in accurately capturing dependence structures in non-Gaussian settings. This study introduces a Generalized Precision Matrix (GPM) that is applicable to a broader class of distributions, including the multivariate t, multivariate skew-normal, and multivariate skew-t distributions. The effectiveness of the proposed GPM is then evaluated using simulated and real-world financial data. Notably, the multivariate skew-t model demonstrates superior fitting ability during crisis periods.
Vine Copula based portfolio level conditional risk measure forecasting
With Sommer, Emanuel and Czado, Claudia (2023)
Status: Currently under review
Accurate estimation of different risk measures for financial portfolios is of utmost importance equally for financial institutions as well as regulators, however, many existing models fail to incorporate any high dimensional dependence structures adequately. To overcome this problem and capture complex cross-dependence structures, we use the flexible class of vine copulas and introduce a conditional estimation approach focusing on a stress factor. Furthermore, we compute conditional portfolio level risk measure estimates by simulating portfolio level forecasts conditionally on a stress factor. We then introduce a quantile-based approach to observe the behavior of the risk measures given a particular state of the conditioning asset or assets. In particular, this can generate valuable insights in stress testing situations. In a case study on Spanish stocks, we show, using different stress factors, that the portfolio is quite robust against strong market downtrends in the American market. At the same time, we find no evidence of this behavior with respect to the European market. The novel algorithms presented are combined in the R package portvine , which is publically available on CRAN.
Which ESG dimension matters most to private investors?
An experimental study on financial decisions
With Benuzzi, Matteo and Klaser, Klaudijo (2023)
Status: Working Paper
The UN list of Sustainable Development Goals (SDGs) has made it widely recognized that sustainability is not exclusively an environmental concept but it rather has multiple dimensions. In the same vein, different non financial information and actions, specifically environmental, social, and governance (ESG), have been provided and undertaken by companies. However, since their introduction, many scholars have questioned whether these three ESG dimensions are equally important under different perspectives. For example, especially since the process of climate change is rapidly evolving, recently, the environmental dimension (E-Pillar) has received more attention than other sustainability factors. In this paper, we focus on the investment decision-making process of retail investors when ESG information is provided. Moreover, in addition to considering the E, S, and G dimensions, we introduce an F-dimension, which encompasses actions that aim to directly impact future generations. In order to explore the mentioned relationship, we follow an experimental approach that allows us to disentangle how these four dimensions (E, S, G, and F) interact with different risk-return configurations and, therefore, with investors' preferences. Preliminary analyses indicate that investors trade-off financial returns with pro-social objectives, i.e. they invest in sustainable companies in which they would not have invested if they had considered only the personal financial consequences. Furthermore, our results show that the subjects attribute different weightings to the ESGF dimensions: when adopting a purely sustainable perspective, the E-Pillar ranks first, while when adopting a financial perspective, the S-Pillar is the most relevant. Finally, the F-pillar does not seem to play a significant role in the decision, because it is perceived as a factor complementary to the other three.
Bax, K., Sahin, Ö., Czado, C. & Paterlini, S. (2023). ESG, Risk, and (Tail) Dependence. International Review of Financial Analysis. 87(May 2023).
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).
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.
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).
Sahin, Ö, Bax, K., Paterlini, S., & Czado, C. (2022). The pitfalls of (non-definitive) Environmental, Social, and Governance scoring methodology. Global Finance Journal.
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.
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