Addressing Loss and Damage from Climate Change Through Tokenized Rainfall Futures

Abstract

The objective of the study was to explore how finance to cover the cost of loss and damage from climate change can be mobilized through weather derivatives. More specifically, a futures contract was considered as the derivative to mobilize the financing. To integrate the recent advancements in finance and technology, tokenized weather derivatives can be considered. This study contributes to the literature as it proposes a pricing mechanism for the proposed loss and damage futures. The futures price should be a function of the contract size, the difference between the expected rainfall in the future, and the threshold or long-run average rainfall. This pricing approach is adopted since it allows the price to rise when excess rainfall occurs, which in turn is responsible for loss and damage. Therefore, the forecast of the rainfall should be of significant interest of the economic agent seeking to hedge the loss and damage with the futures. A Long Short-Term Memory (LSTM) model was used for forecasting. The LSTM performed better than traditional linear models such as the Autoregressive Integrated Moving Average (ARIMA) and Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) models, as it can capture the non-linear dynamics of the rainfall data.