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This project is divided in two parts. In the first part we model the relationship between stock price returns and volatility of energy forms and the main indicators of economic and financial performance at aggregate level. Particular attention will be devoted to understand the links between carbon price and the prices of energy and non-energy commodities. The investigation of these relationships is carried on using modern econometric approaches, such as tail dependence, quantile regressions, Co-Valute-at-Risk and network analysis. The second part involves the EIBURS project “ESG-Credit.ue – ESG Factors and Climate Change for Credit Analysis and Rating”, funded by the European Investment Bank Institute, whose direct beneficiaries are the University of Venice, the Goethe University of Frankfurt (namely the SAFE research center) and the University of Milan-Bicocca, with Fondazione Eni Enrico Mattei playing the role of supporting partner. The aim of this project is to evaluate the effects of ESG (Environmental, Social and Governance) factors on the default risk of a sample of listed European firms, proxied by the so-called z-scores. The dataset is a cross-section at year 2019 of 1251 listed European firms and includes 590 ESG factors, in addition to controls on the economic and financial characteristics of the firms. The sizeable number of explanatory variables suggests the use of supervised machine learning techniques (eg. LASSO), in order to select the most relevant ESG regressors.

Evaluation of the relationships between stock returns and volatility of energy companies and the dynamics of international financial markets, using financial econometric techniques. Identification of the effects of the ESG factors on the credit risk of listed firms, using machine techniques on individual data.