Please use this identifier to cite or link to this item:
http://hdl.handle.net/1893/36959
Appears in Collections: | Accounting and Finance Journal Articles |
Peer Review Status: | Refereed |
Title: | The Predictive Power of the Oil Variance Risk Premium |
Author(s): | McMillan, David Ziadat, Salem |
Contact Email: | david.mcmillan@stir.ac.uk |
Keywords: | Oil VRP Predictability Output Stocks |
Issue Date: | Apr-2025 |
Date Deposited: | 17-Mar-2025 |
Citation: | McMillan D & Ziadat S (2025) The Predictive Power of the Oil Variance Risk Premium. <i>Resources Policy</i>, 103, Art. No.: 105550. https://doi.org/10.1016/j.resourpol.2025.105550 |
Abstract: | This paper examines the ability of the oil market variance risk premium (VRP) to predict both financial and key macroeconomic series. Interest in understanding the movement of such variables increasingly involves considering measures of investor risk, for which the VRP, that incorporates both implied and realised variance, has recently come to the fore. It is well established that oil price movement impacts both the stock market and wider economy and thus, we examine whether this is also true of the oil VRP. Using monthly US data over the period from 2009 to 2021, we demonstrate the nature of oil VRP predictive power for oil and stock returns, as well as output growth, unemployment, and inflation. Of notable interest, while predictability from the oil VRP series dominates at the one-month horizon and (largely) wanes at over longer time periods, the reverse is found for the stock VRP. These results are robust to the inclusion of additional, established, predictor variables. This indicates that the impact of oil market risk has a more immediate effect on both the stock market and economy, with stock market risk reflecting longer term considerations. A simple out-of-sample exercise supports the view that the inclusion of oil VRP improves forecasts over alternative models that exclude this series. |
DOI Link: | 10.1016/j.resourpol.2025.105550 |
Rights: | This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article. |
Licence URL(s): | http://creativecommons.org/licenses/by/4.0/ |
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1-s2.0-S0301420725000923-main.pdf | Fulltext - Published Version | 1.18 MB | Adobe PDF | View/Open |
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