Università degli studi di Modena e Reggio Emilia

In this paper, we revise the asset pricing model of Lubo ˇ s Pˇ astor et al. (2021) by incorpo- ´ rating a penalty component into investors’ utility functions when they invest in firms with lower ESG compliance (brown firms). Our model highlights the dual behavior of investors who gain utility from investing in green firms (fully ESG-compliant) while incurring disutility from holding brown firms. We introduce a formulation where firms’ green characteristics are represented by a vector of ESG scores, with 1 for full compliance and 0 for non-compliance. The penalty is defined as a function of the deviation from full ESG compliance, adjusted for each investor’s ESG preferences. This leads to a modified CAPM equation that reflects both the non-pecuniary benefits of green investments and the penalties for brown investments.

Climate change has a significant impact on the global economy and financial markets, making climate risk and uncertainty central to asset pricing decisions. These risks include potential economic losses due to extreme weather events or gradual changes and can impact business redundancy, infrastructure stability, and approval channels. We review the main theoretical models that incorporate climate risk in asset pricing and the empirical methods to assess the existence of a climate risk premium.

The aim of this study is to examine the literature on climate risk definition and measures and the impact of climate risk on stock returns. We review how asset pricing models (and their testable implications) consider climate risk as a residual systemic risk driver in excess of either standard market risk factors or latent factors identified with business and financial cycles. Firms less exposed to transition risk, in equilibrium, should face a lower cost of equity financing, given an expected return lower than the one associated with pollutant firms. The existence of a recent outperformance of realized returns on green stocks can be reconciled with unexpected shifts in investors tastes for green assets. Finally, we identify some issues regarding the empirical approach and suggest several potential areas for future research.

ESG (Environmental, Social, and Governance) ratings are becoming increasingly significant in guiding financial investment decisions. However, numerous studies have highlighted discrepancies in ESG ratings across different data providers, primarily due to variations in the methodologies they employ. To address this issue, we provide a summary of the key ESG data providers, focusing on two distinct aspects: ESG ratings for firms and ESG ratings for countries. These two topics are essential in empirical analysis to attempt to integrate both dimensions— firm and country—into the evaluation process for assessing whether they play a significant role in defining climate risk. Moreover, this integration aids in understanding the implications for climate risk premiums in the stock market. By considering both the firm and country dimensions, we can better capture the multifaceted nature of climate risk and its impact on investment outcomes. The purpose of this report is twofold: first, we retrieve information from websites concerning the ESG criteria and methodologies of major climate indicator data providers, evaluating their transparency and accountability, and highlighting the differences between public and private sources. Second, we focus on alternative measures of climate risk that should be used in empirical analysis. Indeed, ESG ratings at both the company and country levels are not the only measures of climate risk exposures. In particular, while ESG ratings or solely the Environmental dimension are often used to assess transition climate risk, they are less frequently applied to assess physical risk.

We investigate how the application of advanced predictive models could help investors to assess and manage climate risk in their portfolios, contributing to the development of more sustainable and resilient investment practices. We highlight the possible applications of predictive analytics as a key tool in climate finance. It emerges how emerging technologies (blockchain and Artificial Intelligence) can improve transparency, efficiency, and climate risk analysis in sustainable investments. Further lines of research are highlighted, focusing on how investors and portfolio managers can develop strategies to manage the risks associated with climate events and the integration of climate risks into the management of Supply Chain Finance to ensure greater resilience and sustainability.

The relationship between prices and volatility of energy assets (primarily oil and gas) is of paramount importance for investors and policy makers. We construct a volatility index for the European oil and gas market based on a model-free approach to obtain a European counterpart of US volatility indices for the energy market, such as the CBOE Crude Oil Volatility Index (OVX). Given that investors are averse to volatility of losses, but appreciate volatility of gains, we also derive risk measures that focus on positive and negative returns and their imbalance. We assess whether the constructed indices have predictive power on future returns. We show that in the medium term all the risk indices behave as market greed indicators, whereas in the short term they behave as fear indicators since rises in risk indices are linked with negative returns. The implications for investors and policy-makers are outlined.