MUZZIOLI Silvia

Green investments represent one of the main and most impactful challenges for the future sustainable growth of EU countries. Since the relevance and the sign of climate risk factors and sustainability scores in explaining future stock returns are often debated and inconsistent, it is essential to provide investors with a better understanding of the relationship between climate risks and the cross-section of stock returns, in order to encourage informed investment decisions in environmentally sustainable firms. Despite recent progress, the currently available ESG information remains limited in its ability to support long-term value creation and international climate-related objectives. In particular, ESG scores are available for only a limited number of firms, and ESG data are typically reported at a low frequency (quarterly or annually). When available, ESG scores provided by different information providers (e.g., Bloomberg, Reuters, S&P Global) often diverge significantly, creating confusion among investors. Moreover, at the individual firm level, there may be a tendency to disclose only partial information, emphasizing environmental dimensions where the company performs well while neglecting weaker areas, thus contributing to greenwashing practices. To address these gaps, the project aims to deepen the understanding of the relationship between climate risk and market returns by aggregating multiple information sources to mitigate greenwashing and by employing appropriate econometric techniques to manage data uncertainty. The objectives of the project are manifold. First, to propose an innovative theoretical framework for the relationship between climate risk and the cross-section of stock returns. Second, to measure firms’ exposure to climate risk for a large sample of European equities by combining various sources of information such as ESG ratings, firms’ exposure to climate-related news, financial statements, green bonds issued by companies, carbon footprint data, CO₂ emission reports, and potential environmental litigation records. Third, to investigate the climate risk premium by constructing portfolios that reflect different levels of exposure to climate risk. Fourth, to evaluate the properties of green and brown portfolios in terms of diversification, tail risk, and potential spillover effects. Finally, to assess the predictive power of climate risk for future stock returns using advanced machine learning techniques suitable for large datasets with mixed frequencies. The expected results of the project will have significant implications for investors, firms, and policymakers, and more broadly for the European Union, as they are crucial to effectively directing private financial resources toward activities aimed at climate change mitigation and adaptation.

Research questions are following. 1. How to obtain the risk measures (volatility, skewness, and other indices) and how to assess their usefulness. 2. Assessing the existence and sign of risk premia: investigation of CAPM and Fama-Macbeth regressions. 3. Financial connectedness

The project takes as its starting point the observation that in the EU countries there is a lack of instruments to monitor the risk of each EU financial market and the risk of the EU financial market as a whole. In fact, only a few countries (mainly from northern and central Europe, the most developed ones) adopt a volatility index traded in the internal stock market, and none of the countries adopt a more advanced index in order to measure additional tail risk (such as the Chicago Board Options Exchange SKEW index). Moreover, an aggregate EU volatility index has not yet been developed. In fact, the way volatility and skewness indices are computed is obsolete and calls for a radical change. The volatility and skewness indices have proven to have a low forecasting power on future returns and to measure a limited portion of the overall risk of a country market index (see Elyasiani et al. (forthcoming)), giving conflicting signals most of the time. On the other hand, investors, firms and regulators need a simple and single measure of risk assessment that gives a clear and unambiguous signal of market risk. The main obstacle to overcome for the construction of such indices is the limited availability of option-based data for European peripheral countries. In addition, it is important to respond to the need for new techniques accounting for uncertainty in data and data processing and the narrow focus of the above indices, which treat financial markets as compartmentalized and overlook macroeconomic variables such as inequality indexes (see Kumhof et al. 2015) and other important determinants in risk assessment. The vision behind this project is to transfer into risk assessment recent breakthroughs from machine learning (ML) methods. The project is intended to open an entirely new field of research, capable of offering the academic community and a range of stakeholders new insights not only in the present field of application, but also in other fields of research that are currently characterised by similar constraints.