Abstract

The transition to a low-carbon economy generates new regulatory, technological, market and reputational risks for the financial sector. These climate transition risks are mainly analysed by portfolio managers through bottom-up fundamental approaches such as prospective scenario analysis and company scores. In order to overcome the difficulty of linking climate transition risks with financial risks and the lack of data, recent research has investigated the impact of these risks on market prices by constructing dedicated factors. This paper contributes to this literature in two ways. First, we propose a new climate transition factor that captures both the sectoral and intra-sectoral dimension of the transition to a low-carbon economy. Second, instead of trying to add this factor to a multi-factor model, we propose to disentangle the effect of climate transition risks from traditional risks. Our approach thus enables investors to quantify and optimise the amount of risk coming from their exposure to transition sensitive instruments.

Key takeaways:

• We propose a climate transition factor that captures both the sectoral and intra-sectoral dimension of the transition to a low-carbon economy by relying on the climate-policy relevant sector classification and on GHG emissions intensity.
• Consistent with the literature, we do not find that the addition of such a factor significantly improves the power of an asset pricing model. However, we present an approach that enables us to disentangle the risk attributed to financial risk from those stemming from climate transition risks.
• Over the recent period (2017-2020), this risk associated with the transition factor already represents a significant part of the active risk of some funds.

Introduction

Through the Paris agreement, the international community has committed to keep global average warming below 2°C, along with a more ambitious objective of 1.5°C. In addition to the physical effects of climate change, the economic transformations required to reach this objective will affect (positively or negatively) certain economic sectors more than others (IPCC, 2022). From an investor perspective, these transformations will generate new transition risks and it is therefore necessary to identify the companies that best anticipate regulatory, technological and market developments to manage them.

Transition risks are difficult to estimate using fundamental approaches. First, despite reinforced regulatory requirements1 and recommendations2, persistent gaps in climate-related data remain (NGFS, 2022). Secondly, the radical uncertainties associated with transition scenarios are difficult to incorporate into fundamental valuation models (Bolton et al., 2020). As a result, transition risk metrics display a significant degree of diversity (Bingler, Senni and Monnin, 2021).

Against this backdrop, academics have sought to measure transition risks directly from market prices, which reduces the data and model barriers mentioned above. So far, the effort has focused on building climate transition (CT) factors. These factors are designed on the same principle as traditional factors (e.g., size, value): they are portfolios constructed in such a way that their price changes are representative of the dynamics of the stocks affected by the transition risks. This approach relies on the assumption that markets integrate information related to transition risks. However, the literature presents contrasting results regarding the current integration of transition risks, both on the significance and the direction (e.g. Bolton & Kacperczyk, 2021; Alessi, Ossola and Panzica, 2021). Thus, our approach does not assume that prices already incorporate transition risks but that prices will do so over time.

The methodology we present aims to contribute to this literature on price-based analysis of transition risks by addressing two main conceptual issues. The first one is related to the design of a CT factor. While some papers rely solely on carbon intensity, i.e., the greenhouse gas (GHG) emissions of a companydivided by its revenues, others use up to 10 metrics to build their representative portfolio (Görgen et al., 2020). The type and number of metrics raises questions regarding their current availability, quality, and their relevance to assess long-term transition risks. Our approach departs from previous attempts at producing a CT factor based solely on individual company characteristics. Instead, we utilise what is likely to be the most robust information regarding a company’s exposure to transition risks: its industrial sector. We introduce a new CT factor that relies on i) the climate-policy relevant industrial sectors (CPRS) classification developed by Battiston et al. (2017), and ii) the carbon intensity to differentiate companies within these CPRS sectors.

The second issue of price-based analysis of transition risks is related to the use of a CT factor in a risk model. Investors have started considering transition risks relatively recently: 2015 was a pivotal year with the Paris Agreement and the warning by Bank of England Governor Mark Carney (Carney, 2015). Because the traditional tests to validate the relevance of a factor rely on long timeframes, CT factors usually do not pass these tests and are therefore not qualified as “proper” risk factors (Amenc, Esakia & Goltz, 2021; Görgen et al., 2020). We propose a different approach that focuses on the practical management of transition risks by disentangling the links between a portfolio’s exposure to the CT factor and the traditional ones.

Our goal is to give priority to the long-term robustness and to avoid what we call the “Don’t Look-Up” syndrome. In this movie, the discovery of a world-killing comet serves as a metaphor for the (lack of ) reaction of our society to climate change. What if this comet was not to destroy the world, but only one city? How would you design a “comet” factor? As in the first part of the movie, if the comet’s trajectory is known only to scientists, the effect on market prices will be negligible. However, this effect will increase dramatically once the public becomes aware of the comet’s trajectory and believes it to be true. The risk is therefore real, but its impact on prices is not observable for a long time; testing the validity of such a “comet” factor on historical prices is not relevant. In this case, the factor validation should focus on the inclusion of the most robust information about the comet: where it will crash. Therefore, we believe that the use of industrial sectors in the construction of a CT factor is crucial.

Authors

Benjamin Herzog
Chief Executive Officer,
Scientific Portfolio

Vincent Bouchet
Director of ESG & Climate research,
Scientific Portfolio

Benoit Vaucher
Director of Research,
Scientific Portfolio

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