Portfolio strategy of International crude oil markets: A study based on multiwavelet denoising-integration MF-DCCA method

摘要

Considering the fact that crude oil markets have various noise, fluctuations and actual needs of investors in different trading cycles, in this study, we propose a multiwavelet denoising-integration MF-DCCA method by three-phase modeling to construct a portfolio among crude oil markets. On the basis of noise filtering, this method extracts the effective prediction information from different fluctuations and integrates them into the same time scale. The noise reduction and out-of-sample portfolio performance are evaluated, respectively. The robustness of out-of-sample portfolio performance is further tested by changing partial conditions The empirical results indicate that the denoising performance of multiwavelet denoising method is clearly better than the traditional wavelet denoising methods. Furthermore, in any sample period, the multiwavelet denoising-integration MF-DCCA method also performs better than the existing popular methods in terms of profitability and Sharp ratio. Besides, the long-term scales (s=60, 80) are superior to the short-term scales (s=20, 40) in most situations. Robustness results verify the above conclusions. (C) 2019 Elsevier B.V. All rights reserved.