Hedging futures performance with denoising and noise-assisted strategies

Abstract

Noise processing is very important to improve hedging effectiveness. However, the existing methods are mainly considered from the view of denoising strategy, and the research on noise-assisted strategy is limited. In this paper, a framework that includes both denoising and noise-assisted strategies is proposed to comprehensively analyze the impact of noise proceeding on hedging effectiveness. In detail, the EMD technology is utilized to decompose the futures and spot original returns. Then, the decomposition terms are stepwise removed or added in the opposite way to obtain the denoised and noise-assisted returns. Finally, under the minimum-CVaR framework, the dynamic hedged portfolios based on original and processed returns are constructed to test the hedging effectiveness. Based on the daily prices of CSI300, S&P500, WTI crude oil, and gold futures contract which range from February 9, 2007, to January 10, 2020, the empirical results indicate that both denoising and noise-assisted hedging strategies can decrease CVaR compare with using original return. Furthermore, denoising or adding high-intensity noise has better hedging performance than low-intensity noise, adding uncorrelated noise performs better than adding correlated noise Robustness results by changing confidence level validate the above conclusions.