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The dependence and risk spillover between crude oil market and China stock market: New evidence from a variational mode decomposition-based copula method

Xiafei Li and Yu Wei

Energy Economics, 2018, vol. 74, issue C, 565-581

Abstract: This paper examines the dependence structure between crude oil market and China stock market over different investment horizons, before and after the recent financial crisis, by combining the variational mode decomposition (VMD) method with various static and time-varying copulas. Based on the decomposed time series and the copula dependence, the Value-at-Risk (VaR), conditional VaR (CoVaR) and delta CoVaR (ΔCoVaR) are quantified to analyze the upside and downside risk spillovers from oil market to China stock market in raw, short- and long-run investment horizons before and after the financial crisis. The empirical results show that, first, the recent financial crisis enhances the dependences between the crude oil market and China stock market, and the long-run dependence increases more significantly than that of short-run. For the raw return series, there are symmetric upper and lower tail dependencies in full sample and pre-crisis subsample periods, but an average dependence in post-crisis subsample period. Second, the VaR of China stock market increases heavily around the financial crisis, but the average VaR after the crisis deceases compared to the risk before the crisis. Third, the risk spillovers from crude oil price to China stock market are found in each sample periods. Before the crisis, however, it mainly exists in long-run horizon, while after the crisis, it happens in both short- and long-run horizons. Finally, the risk spillovers from oil price to China stock market display strong asymmetric features, with larger long-term, downside risk spillovers in post-crisis subsample.

Keywords: Crude oil market; China stock market; Variational mode decomposition; Copula; CoVaR (search for similar items in EconPapers)
JEL-codes: C22 C52 Q43 (search for similar items in EconPapers)
Date: 2018
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