The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Geopolitical risk and oil volatility: A new insight
Abstract
Abstract Motivated by the importance of geopolitical risk and its possible predictive power for oil volatility, this paper aims to quantitatively investigate the role of geopolitical risk (GPR), especially serious geopolitical risk (GPRS), in forecasting oil volatility. For research purposes, the GARCH-MIDAS model is extended by incorporating GPR and GPRS. Then, the new extensions are examined from the perspectives of both statistical and economic significance. In-sample results show that GPR and GPRS lead to oil market fluctuations, while the out-of-sample results strongly confirm that the GARCH-MIDAS-GPRS model with serious GPR significantly outperforms the GARCH-MIDAS model. Moreover, both GPR and GPRS help gain higher economic returns. In particular, serious geopolitical risk contains useful information for the recent future oil volatility and can provide the best economic gains. Oil market investors and government policymakers should pay more attention to extreme geopolitical events and serious geopolitical risk in the context of risk management and portfolio allocation.
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Cites
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THE ROLE OF INVENTORIES AND SPECULATIVE TRADING IN THE GLOBAL MARKET FOR CRUDE OIL
Kilian, L., & Murphy, D. P. (2014) · Journal of Applied Econometrics · doi:10.1002/jae.2322 · Cited by 1,472
Stock Market Volatility and Macroeconomic Fundamentals
Engle, R. F., Ghysels, E., & Sohn, B. (2013) · Review of Economics and Statistics · doi:10.1162/rest_a_00300 · Cited by 1,117
The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes
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Predicting volatility: getting the most out of return data sampled at different frequencies
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Manager sentiment and stock returns
Jiang, F., Lee, J., Martin, X., & Zhou, G. (2019) · Journal of Financial Economics · doi:10.1016/j.jfineco.2018.10.001 · Cited by 724
Short interest and aggregate stock returns
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Cited by
Geopolitical risk and dynamic connectedness between commodity markets
Gong, X., & Xu, J. (2022) · Energy Economics · doi:10.1016/j.eneco.2022.106028 · Cited by 354
Oil price shocks, geopolitical risks, and green bond market dynamics
Lee, C.-C., Lee, C.-C., & Li, Y.-Y. (2021) · The North American Journal of Economics and Finance · doi:10.1016/j.najef.2020.101309 · Cited by 341
Metadata
- Authors
- Liu, J., Ma, F., Tang, Y., & Zhang, Y.
- Venue
- Energy Economics
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 84
- Pages
- 104548
- ISSN
- 0140-9883 (ISSN-L)
- OpenAlex
- W2980564922
- Cited by
- 300
- References
- 38
- FWCI
- 27.74
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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