A new dynamic hedging model with futures: the Kalman filter error-correction model

This paper proposes a new econometric model for the estimation of optimal hedge ratios (HRs): the Kalman filter error-correction model (KF–ECM). This paper proposes a new econometric model for the estimation of optimal hedge ratios (HRs): the Kalman filter error-correction model (KF–ECM). When we construct a hedging model involving a stock index and its futures, the KF state-space model is used to extract the single best latent common trend between the stock index and its futures. After the best common trend has been obtained, we substitute it into an ECM to estimate the HR; therefore, the KF–ECM combines the merits of error correction and state-space hedge models. This paper uses daily data from the Taiwan Capitalization Weighted Stock Index and its futures from July 21, 1998 to March 27, 2019 to evaluate the hedging performance of the KF–ECM. The study uses a hedging effectiveness (HE) index to compare the performance of the KF–ECM with that of other hedging models, including ordinary least squares, generalized autoregressive conditional heteroscedasticity and cointegrating ECMs. The empirical results of testing both in-sample and out-of-sample data show the KF–ECM to be more effective than other hedging models. The results also demonstrate that, regardless of the financial crisis, the HE of the KF–ECM remains the highest among all the models.