基于多小波降噪—分形标幅双集成法的股指期货套期保值比率估计研究

doi:10.16381/j.cnki.issn1003-207x.2024.1454

Abstract

Abstract:

The high volatility of Chinese stock market presents significant challenges to its sustainable development. Futures-spot hedging strategies are widely recognized as an effective solution for mitigating stock price risks. Therefore， it seeks to develop a novel and effective model for estimating futures-spot hedge ratios， thereby enabling investors to reduce risks and enhance returns. Considering the presence of substantial noise and multifractal characteristics in futures and spot markets， a multi-wavelet denoising and fractal scale-amplitude dual integration method is introduced for estimating stock futures hedge ratios. This method addresses market noise while fully leveraging the values of multiple time scales and fluctuation amplitudes. It begins by reducing data noise by a multi-wavelet approach and then employs the MF-DCCA （Multifractal Detrended Cross-Correlation Analysis） method to capture multifractal characteristics in futures-spot dependence structure. This process calculates hedging ratios across both volatility amplitudes and time scales. Using a swarm intelligence optimization algorithm， with return maximization and variance minimization as multi-objective functions， this method further integrates the hedging ratios across multiple scales and multiple fluctuation amplitudes sequentially to obtain the dual-integrated hedging ratio. Drawing on the prices from January 3， 2017， to June 14， 2024， totaling 1，808 trading days， this novel method is applied to estimate hedge ratios for the CSI 300 futures and spot markets. Empirical results indicate that， compared to single-wavelet methods， the proposed multi-wavelet denoising approach is more effective at reducing noise and demonstrates superior stability. The findings underscore the critical importance of addressing noise in hedging modeling process. Moreover， the CSI 300 futures and spot markets exhibit significantly multifractal characteristics， with notable variations across different time scales and fluctuation amplitudes. Additionally， the proposed model surpasses control methods in most cases， achieving higher returns， variance reduction ratios， and return-to-variance ratios， thereby delivering the optimal hedging effectiveness. The current paper provides a novel hedging theoretical methodology and offers a new risk-management insight.

Key words: hedging ratio, chinese stock futures, wavelet approach, multifractal model, multi-wavelet denoising and fractal scale-amplitude dual integration method

CLC Number:

F830.9

Pengfei Zhu,Tuantuan Lu,Yu Wei, et al. Research on Estimating Hedging Ratio in Stock Futures Using a Multi-Wavelet Denoising and Fractal Scale-Amplitude Dual Integration Method[J]. Chinese Journal of Management Science, 2026, 34(5): 21-34.

Figures/Tables 20

References 38

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类别	均值	标准差	偏度	峰度	JB	Q(5)	Q(10)	ADF
期货	9.26E-5	0.005	-1.299	35.441	67787.15^***	2193.90^***	2305.90^***	-12.816^***
现货	8.89E-5	0.004	-0.709	14.899	9189.98^***	3085.10^***	3238.20^***	-12.846^***

指标	1	2	3	4	5	6	7	8	9	10
AAR	4.13E-4	3.99E-4	4.01E-4	3.86E-4	3.99E-4	4.17E-4	3.38E-4	5.11E-4	4.05E-4	5.15E-4#
VRR	0.920	0.945	0.946	0.938	0.945	0.953	0.896	0.948	0.954	0.956#
RVR	0.166	0.192	0.196	0.175	0.192	0.218	0.119	0.253	0.213	0.277#

指标	1	2	3	4	5	6	7	8	9	10
AAR	1.08E-4	7.30E-5	4.69E-5	7.33E-5	7.31E-5	6.97E-5	1.15E-4	3.24E-5	-7.88E-5	1.23E-4#
VRR	0.941	0.949	0.949	0.945	0.949	0.953#	0.916	0.943	0.917	0.948
RVR	0.049	0.035	0.023	0.034	0.035	0.035	0.043	0.015	NA	0.059#

指标	1	2	3	4	5	6	7	8	9	10
AAR	2.84E-4	2.52E-4	2.53E-4	2.43E-4	2.52E-4	2.63E-4	2.14E-4	3.22E-4#	2.94E-4	2.71E-4
VRR	0.924	0.942	0.943	0.935	0.942	0.950	0.893	0.937	0.947	0.958#
RVR	0.118	0.120	0.122	0.109	0.120	0.135	0.075	0.147	0.147	0.152#

指标	1	2	3	4	5	6	7	8	9	10
AAR	3.49E-4	2.98E-4	3.01E-4	2.93E-4	3.00E-4	3.12E-4	2.55E-4	4.08E-4	3.51E-4	4.31E-4#
VRR	0.914	0.935	0.937	0.932	0.937	0.942	0.890	0.920	0.944#	0.942
RVR	0.138	0.136	0.139	0.130	0.138	0.150	0.089	0.167	0.171	0.206#

Research on Estimating Hedging Ratio in Stock Futures Using a Multi-Wavelet Denoising and Fractal Scale-Amplitude Dual Integration Method

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指标	多小波降噪-分形标幅双集成法-FPA	多小波降噪-分形标幅双集成法-MOFA
AAR	5.24E-4#	5.15E-4
VRR	0.953	0.956#
RVR	0.273	0.277#

保证金	1	2	3	4	5	6	7	8	9	10
保证金8%	4.10E-4	3.96E-4	3.99E-4	3.84E-4	3.97E-4	4.14E-4	3.36E-4	5.07E-4	3.99E-4	5.12E-4#
保证金10%	4.10E-4	3.96E-4	3.98E-4	3.83E-4	3.96E-4	4.14E-4	3.36E-4	5.06E-4	3.99E-4	5.11E-4#
保证金20%	4.07E-4	3.93E-4	3.95E-4	3.80E-4	3.93E-4	4.11E-4	3.34E-4	5.02E-4	3.97E-4	5.08E-4#

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