SOM-Based Hedge Ratio Estimation with Hierarchical Cluster Resampling

The accuracy of the traditional hedge ratio estimation is affected by the fat-tailed and leptokurtic properties observed in a financial time series. In this study, a novel method based on the self-organizing map (SOM) is proposed to modify the probability distribution of the time series and improve the optimal hedge ratio (OHR) estimation. The financial time series data are hierarchically clustered by SOM based on similar patterns described by the technical indicators. These hierarchical clusters, to which the current daypsilas data belong and share a similar pattern with the data, are used for the current OHR estimation. The OHR is estimated using the ordinary least squares (OLS) method based on the data, which are obtained via bootstrap resampling from the hierarchical clusters and a more approximate normal distribution. Taiwan weighted index, S&P 500 index, and FTSE 100 index are adopted in the empirical experiments to test the hedging effectiveness. Experiment results demonstrate that resampling on the hierarchical SOM clusters can increase the accuracy of the OHR estimation and has a better hedge effectiveness than that of the traditional OLS method.