Hao-Lin Shao, Ying-Hui Shao, Yan-Hong Yang
arXiv 6 Oct 2021 · Econometrics · publishedFluctuation and Noise Letters (2024) · 2 citations (OpenAlex)
arXiv:2110.02693 · PDF · DOI · OpenAlex · Extracted main text
This paper investigates the cointegration between possible determinants of crude oil futures prices during the COVID-19 pandemic period. We perform comparative analysis of WTI and newly-launched Shanghai crude oil futures (SC) via the Autoregressive Distributed Lag (ARDL) model and Quantile Autoregressive Distributed Lag (QARDL) model. The empirical results confirm that economic policy uncertainty, stock markets, interest rates and coronavirus panic are important drivers of WTI futures prices. Our findings also suggest that the US and China's stock markets play vital roles in movements of SC futures prices. Meanwhile, CSI300 stock index has a significant positive short-run impact on SC futures prices while S&P500 prices possess a positive nexus with SC futures prices both in long-run and short-run. Overall, these empirical evidences provide practical implications for investors and policymakers.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Baker, S.R., Bloom, N., Davis, S.J (2016) Measuring Economic Policy Uncertainty | 0.843 | 3 | 3 | 100% |
| 2 | Atri, H., Kouki, S., imen Gallali, M (2021) The impact of covid-19 news, panic and media coverage on the oil and gold prices: An ardl approach | 0.843 | 3 | 3 | 100% |
| 3 | Kyrtsou, C., Mikropoulou, C., Papana, A (2016) Does the s&p500 index lead the crude oil dynamics? a complexity-based approach | 0.843 | 3 | 3 | 100% |
| 4 | Arora, V., Tanner, M (2013) Do oil prices respond to real interest rates? | 0.737 | 3 | 2 | 100% |
| 5 | Cho, J.S., hwan Kim, T., Shin, Y (2015) Quantile cointegration in the autoregressive distributed-lag modeling framework | 0.737 | 3 | 2 | 100% |
| 6 | Mensi, W., Rehman, M.U., Al-Yahyaee, K.H (2020) Time-frequency co-movements between oil prices and interest rates: Evidence from a wavelet-based approach | 0.737 | 3 | 2 | 100% |
| 7 | Aloui, R., Gupta, R., Miller, S.M (2016) Uncertainty and crude oil returns | 0.644 | 2 | 2 | 100% |
| 8 | Lu, Q., Li, Y., Chai, J., Wang, S (2020) Crude oil price analysis and forecasting: A perspective of “new triangle” | 0.644 | 2 | 2 | 100% |
| 9 | Mensi, W., Sensoy, A., Vo, X.V., Kang, S.H (2020) Impact of covid-19 outbreak on asymmetric multifractality of gold and oil prices | 0.644 | 2 | 2 | 100% |
| 10 | Niu, Z., Liu, Y., Gao, W., Zhang, H (2021) The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from china | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 35 scored citations.