A1301
Title: Predictability of funding rates
Authors: Emre Inan - York University (Canada) [presenting]
Abstract: The purpose is to investigate the out-of-sample predictability of perpetual futures funding rates with a particular focus on Bitcoin contracts traded on Binance and Bybit. Throughout the analysis, one-step-ahead point forecasts are generated from a set of double autoregressive models and evaluated against standard benchmarks. According to the results, model-based predictions outperform the no-change model both in terms of forecast error and directional accuracy, providing strong evidence for the predictability of the next period's funding rate levels. However, the local analysis shows that the stability of the funding rates is evolving over the evaluation period, suggesting a time-varying degree of their predictability.
