Risk Forecasting Comparisons in Decentralized Finance: An Approach in Constant Product Market Makers

Abstract

This study leverages decentralized liquidity pool data sourced from UNISWAP-V2 to forecast Value-at-Risk (VaR) and Expected Shortfall (ES) employing the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with varied error distributions and the deep learning probabilistic forecasting algorithm known as DeepAR. Performance evaluations of these distinct forecasting methodologies are conducted using an appropriate loss function. Results indicate that the GARCH model with a normal distribution consistently outperforms other models, particularly when forecasting VaR. Conversely, the DeepAR model exhibits limited effectiveness in VaR forecasting across all scenarios, except for liquidity pools involving at least one stablecoin. However, it demonstrates greater reliability in predicting most ES risk measures and associated data. Our findings underscore that in a subset of the data, providing liquidity to pairs involving at least one stablecoin entails statistically significant lower risk compared to holding an equivalent amount of crypto assets. Furthermore, this research contributes to the advancement of novel risk management tools and strategies tailored for liquidity providers.