Peng-Fei Dai, Xiong Xiong, Wei-Xing Zhou
arXiv 25 Jul 2020 · Finance — Statistical Finance · publishedResources Policy (2022) · 35 citations (OpenAlex)
arXiv:2007.12838 · PDF · DOI · OpenAlex · Extracted main text
This paper aims to examine whether the global economic policy uncertainty (GEPU) and uncertainty changes have different impacts on crude oil futures volatility. We establish single-factor and two-factor models under the GARCH-MIDAS framework to investigate the predictive power of GEPU and GEPU changes excluding and including realized volatility. The findings show that the models with rolling-window specification perform better than those with fixed-span specification. For single-factor models, the GEPU index and its changes, as well as realized volatility, are consistent effective factors in predicting the volatility of crude oil futures. Specially, GEPU changes have stronger predictive power than the GEPU index. For two-factor models, GEPU is not an effective forecast factor for the volatility of WTI crude oil futures or Brent crude oil futures. The two-factor model with GEPU changes contains more information and exhibits stronger forecasting ability for crude oil futures market volatility than the single-factor models. The GEPU changes are indeed the main source of long-term volatility of the crude oil futures.
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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 | Engle, R.F., Ghysels, E., Sohn, B (2013) Stock market volatility and macroeconomic fundamentals | 0.928 | 4 | 3 | 100% |
| 2 | Asgharian, H., Hou, A., Javed, F (2013) The importance of the macroeconomic variables in forecasting stock return variance: A GARCH-MIDAS approach | 0.811 | 4 | 2 | 100% |
| 3 | Liu, Y., Han, L., Yin, L (2018) Does news uncertainty matter for commodity futures markets? Heterogeneity in energy and non-energy sectors | 0.644 | 4 | 1 | 100% |
| 4 | Agnolucci, P (2009) Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models | 0.644 | 2 | 2 | 100% |
| 5 | Dai, P.F., Xiong, X., Zhou, W.X (2020) A global economic policy uncertainty index from principal component analysis self | 0.644 | 2 | 2 | 100% |
| 6 | Ergen, I., Rizvanoghlu, I (2016) Asymmetric impacts of fundamentals on the natural gas futures volatility: An augmented GARCH approach | 0.644 | 2 | 2 | 100% |
| 7 | Bakas, D., Triantafyllou, A (2019) Volatility forecasting in commodity markets using macro uncertainty | 0.585 | 3 | 1 | 100% |
| 8 | Baker, S.R., Bloom, N., Davis, S.J (2016) Measuring economic policy uncertainty | 0.511 | 2 | 1 | 100% |
| 9 | Castelnuovo, E., Tran, T.D (2017) Google it up! A google trends-based uncertainty index for the United States and Australia | 0.511 | 2 | 1 | 100% |
| 10 | Fang, L., Chen, B., Yu, H., Qian, Y (2018) The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH-MIDAS approach | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 50 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | The impact of geopolitical risk on the international agricultural market: Empirical analysis based on the GJR-GARCH-MIDAS model | 0.405 | 1 | 1 |