![]() ![]() If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.įor technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. You can help adding them by using this form. ![]() We have no bibliographic references for this item. It also allows you to accept potential citations to this item that we are uncertain about. This allows to link your profile to this item. If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. See general information about how to correct material in RePEc.įor technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact. When requesting a correction, please mention this item's handle: RePEc:eee:jrpoli:v:80:y:2023:i:c:s0301420722005906. You can help correct errors and omissions. Suggested CitationĪll material on this site has been provided by the respective publishers and authors. Overall, this paper provides a more realistic approach for crude oil risk managers to hedge crude oil price risk, some corresponding implications are also concluded. We also test the robustness of the proposed method with respect to the baseline model, quantile, and evaluation window. Furthermore, the superiority of the proposed method is robust to different market conditions, including significant rising or falling trends, large basis, and COVID-19 pandemic. We apply the DM test to make a statistical inference and discover that while the mean and the ratio of mean to variance of returns are increasing, the variance and hedging effectiveness of the hedged portfolio based on the modified methods are not significantly affected. Empirical results of crude oil futures markets indicate that the proposed state-dependent hedging model is superior to the commonly used models in terms of three criteria including mean of returns, variance, and ratio of mean to variance of returns for measuring hedging effect. Last but not least, we propose the hedge ratio adjustment criteria based on the identified state, and adjust the ratio driven by GARCH-type models of futures in accordance with the market state. Then, we develop a novel and tractable method to identify the market state based on the index of consistency intensity, in which the index portrays the synchronous degree of stock price movements in the energy sector. First, without loss of generality, we forecast crude oil spot and futures volatility using 10 GARCH-type models, including three linear models and seven nonlinear models, to obtain the ex-ante hedging ratio under the minimum variance framework. Based on the proposed synchronous movement intensity index, this paper aims to improve the hedging performance by adjusting the model-driven hedge ratio and realize the trade-off between return and risk in futures hedging. Risk and return are two fundamentals that have an impact on an investor’s or hedger’s investing choices.
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