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Empirical analysis of oil risk-minimizing portfolios: the DCC-GARCH-MODWT approach

机译:石油风险最小化投资组合的实证分析:DCC-GARCH-MODWT方法

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This paper strives to analyze hedging strategies between Brent oil and six other heterogeneous assets - American ten-year bonds, US dollars, gold, natural gas futures, corn futures, and Europe, Australasia and Far East exchange-traded funds (EAFE-ETFs) - observing five wavelet time horizons and considering three different risk metrics: variance, value-at-risk (VaR) and conditional value-at-risk (CVaR). We construct two-asset portfolios, whereby conditional variances and covariances are obtained via a bivariate rolling dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model. Results indicate that gold is the best combination with Brent for minimum-variance investors, while the Brent-natural gas pair produces the worst minimum-variance results due to the very high unconditional variance of gas. As for VaR and CVaR results, we find that Brent with gold gives relatively good outcomes, but the portfolio with gas heavily outperforms the portfolio with gold when one views longer time horizons. This happens because the Brent-gas portfolio has very low skewness and kurtosis on longer time horizons compared with the unhedged portfolio, and these characteristics favor good VaR and CVaR results. These findings could help global portfolio managers and investors who seek various ways to diversify their Brent oil investments, who act on different time horizons, and who target different risk-minimizing goals.
机译:本文致力于分析布伦特油与六个其他异构资产 - 美国十年债券,美元,黄金,天然气期货,玉米期货和欧洲,澳大利亚和远东交易所交易资金(EAFE-ETF)之间的对冲策略 - 观察五个小波时间视野,考虑三种不同的风险指标:方差,价值(VAR)和条件值 - 风险(CVAR)。我们构建了两种资产的组合,由此通过双变型滚动动态条件相关 - 通用自回归条件异素(DCC-GARCH)模型获得条件差异和协方差。结果表明,黄金是与最低方差投资者的布伦特的最佳组合,而布伦特天然气对由于气体的极高无条件方差而产生最差的最低差异结果。至于VAR和CVAR结果,我们发现带有黄金的布伦特给出了相对良好的结果,但是当一个观点时,瓦斯的投资组合大量优于黄金的投资组合。这种情况发生,因为布伦特 - 气体组合在与未联合的组合相比的较长时间视野上具有非常低的偏光和峰度,这些特征有利于良好的VAR和CVAR结果。这些调查结果可以帮助全球投资组合管理人员和投资者寻求各种方式,使他们的布伦特石油投资多样化,他采取不同的时间视野,以及针对不同的风险最小化目标。

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