Abstract
Shanghai crude oil futures contracts (SC) has been the third largest trading volume crude oil futures worldwide. From wavelet denoising-higher moment perspective, we investigate the relationships and portfolios between oil and Chinese stock sectors, based on the daily data of WTI, SC and Chinese sectors. Given the existence of noise and time-varying conditional higher moments in financial data, the Wavelet denoising-GARCHSK and Wavelet denoising-GARCHSK-Copula portfolio methods are proposed to model the margin of variables and construct oil-stock portfolios, respectively. The empirical results indicate that the Wavelet denoising-GARCHSK outperforms the other methods in terms of log likelihood, AIC and BIC. Then, these results also denote that there are the notable industry characteristics in the oil-stock relationships, depending on whether the sector is petroleum-dependent or petroleum-independent as well as locates upstream or downstream of petrochemical industry. In addition, most of the Chinese stock sectors have stronger upper tail dependence with SC than WTI, while the lower tail is the opposite. Finally, it is discovered that the oil-stock portfolios obtained by Wavelet denoising-GARCHSK-Copula portfolio model outperform the control groups in terms of return and ASR and that WTI is a better diversifier for investors in Chinese stock market in most cases.
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