Pricing climate change related impacts in financial markets with a focus on sovereign bonds

Climate change and the damages it brings have been recognized as a source of risk for financial institutions by central bankers, financial practitioners, and researchers. Limiting global emissions and transitioning to a net-zero economy requires a redirection of capital flows toward green investment. If this transition is conducted in a disorderly manner, it might be disruptive to the financial markets. To navigate the transition and avoid extreme climate change we need to understand how to price the risks and opportunities related to climate change. To do that, researchers are working on new pricing and risk models that would allow investors to include environmental considerations in their decision-making process. Most of the attention has been focused on pricing climate risk in equity and stress testing models, however, there is a growing literature suggesting that sovereign debt will also be affected. My work contributes to the field of climate finance through two projects.

The aim of the first project is to examine whether financial markets have been responding to events related to climate change and low-carbon transition. To do that I am building a family of indices tracking climate change coverage in news. I am collaborating with natural language processing (NLP) company Yewno. Using NLP and graph theoretical tools, I am extracting the importance of climate change concepts in the news and their relation to financial and economic concepts. Such index of climate news can then be used (i) to augment standard pricing models, (ii) as a climate risk factor in stress testing models (iii) as a basis for a hedging strategy.

Informed by the empirical study, I plan to move to a theoretical part in which I work on a methodology for pricing climate risk in sovereign debt markets. I will do that by modeling the economic trajectories of countries' key sectors and trade balances under various transition pathways to net-zero emissions. The changes in Gross Value Added of individual sectors can then be translated into the impact on fiscal revenues of a country and hence its balance sheet. This then affects the probability of default of the sovereign and hence its yields and spreads.

My work will contribute to the new strand of literature that uses NLP in the context of climate finance. Although there are a few works that analyse news, the network approach is completely new. In my methodology, I will be able to distinguish between market reactions to news about events that are the realisation of physical risks and events that are the realisation of transition risks. This will help with calibrating models that attempt to capture the impact of both types of risks on financial products. In my second project, I hope to contribute to the theoretical literature on climate risk in sovereign bonds. This area has not been widely studied so far with few works providing an empirical analysis of the link between climate change and sovereign yields and spreads.

This project falls within the following EPSRC areas: statistics and applied probability, UK climate resilience programme, International Institute for Applied System Analysis, and natural language processing.

In this work, I am collaborating with Yewno, Goldman Sachs, and Fidelity Investment. I am supervised by Doyne Farmer.