Hedging Climate Risk: A Global and Local Solution

Abstract

While the question of the trillions of dollars required to both mitigate and adapt to climate change is more pressing now than ever, only a financial mechanism can scale up capital to such an extent. However, climate risk represents the inability to predict the global mean surface temperature (GMST) evolution and to anticipate its impact on the global economy. Financial solutions exist, called Climate Derivatives, that directly transfer climate risks from the product issuers to the long-term investors by directly modelling the dynamics of the GMST. The evaluation of climate risk requires a model incorporating the three main sources of uncertainties in climate prediction: 1) the model, 2) the scenarios (corresponding to the estimation of the future external forcing applied to the climate system) which are highly unpredictable, 3) the climate internal variability composed of different cyclic processes at different timescales. We choose a two-box model with efficacy factor to capture the deterministic part of the behaviour of the climate, model the forcings with a three dimensional polynomial function, and include stochastic processes representing additional risk. We obtain a bivariate Ornstein-Uhlenbeck process and calibrate the instantaneous volatility of the model to the historical observations of the GMST, capturing both the trend of the model and its internal variabilities. Since the uncertainty on the forcing scenarios corresponds to a qualitative and objective choice, we let the parties take a view on the scenarios. We then assume incomplete market and estimate the price of the climate derivatives by considering the indifference pricing method. We obtain a matrix of indifference prices representing all possible trades where the rows and columns correspond to the different views of the parties. We can then properly analyse climate risk and provide a financial mechanism which can be operated globally or locally, from regions to municipalities or cities.