Hedging futures performance with denoising and noise-assisted strategies

Chengli Zheng, Kuangxi Su and Yinhong Yao

The North American Journal of Economics and Finance, 2021, vol. 58, issue C

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.

Keywords: Futures hedging; Noise processing; Empirical mode decomposition (EMD); Hedging performance (search for similar items in EconPapers)
JEL-codes: G11 G15 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:58:y:2021:i:c:s1062940821000899

DOI: 10.1016/j.najef.2021.101466

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