Optimal Hedge Ratio Estimation for Bitcoin Futures using Kalman Filter

Abstract

This paper examines the hedging effectiveness of Bitcoin futures by comparing one form of the constant model, the conventional OLS method, with the time-varying model in estimating the optimal hedge ratio. For the time-varying model, we employ a powerful technique, Kalman filter, a r ecursive a lgorithm w hich h as n umerous real-time, technological applications, but has not been employed in the context of Bitcoin optimal hedge ratio analysis. Through applying the spot and futures daily settlement prices from 18th December 2017 to 30th November 2022 to the two models, we confirm that t he B itcoin futures is an effective instrument for risk hedging. Additionally, we find the dynamic model based on the Kalman filter p erforms b etter - especially in 2019 and 2020 - than the conventional OLS method in terms of risk reduction, supporting previous findings in the context of other commodity futures. We also certify that the Kalman filter s uccessfully c aptures the trend of the optimal hedge ratio, thus enabling hedgers to decide when to change their hedging strategy. Furthermore, we verify the volatile evolution of the estimated time-varying Bitcoin optimal hedge ratio, suggesting the need to further search for a better hedging instrument which achieves a less volatile time path to avoid excessive trading costs.