概括
本研究使用波波变量量子格兰杰因果关系确定了美国期货市场 (黄金,石油,大豆,天然气) 的风险相关性. 调查结果揭示了显著的双向和单向风险关联,特别是在黄金和天然气之间.
科学领域:
- 金融经济学 金融经济学
- 量化金融 量化金融
- 风险管理 风险管理
背景情况:
- 期货市场是重要的金融工具,其特点是高流动性和杆.
- 这些市场容易受到各种风险的影响,包括价格波动,市场冲击和供需失衡.
- 准确识别风险相关性对于有效的市场监管和投资策略至关重要.
研究的目的:
- 用先进的计量经济学方法确定特定期货市场之间的风险相关性.
- 分析美国市场中黄金,原油,大豆和天然气期货之间的风险关联.
- 为期货市场风险相关性识别提供一种新的方法论视角.
主要方法:
- 波形变换-量子格兰杰因果关系测试的应用.
- 对美国四大期货市场的分析:黄金,原油,大豆和天然气.
- 利用从2009年1月到2023年3月的数据,考虑不同期货合约的到期日期和数量.
主要成果:
- 在不同的期货合约和量度中发现了重要的双向和单向风险相关性.
- 在黄金和天然气之间观察到最强的双向风险相关性 (为期1个月和6个月的合同).
- 原油和大豆显示出明显的双向风险关联,而天然气显示出与原油和大豆的单向联系.
结论:
- 波形变换-量子格兰杰因果关系测试有效地识别了期货市场中复杂的风险相关性.
- 调查结果为监管机构,投资者和风险经理在应对期货市场波动方面提供了宝贵的见解.
- 该研究强调了风险转移的动态性质,受合同到期和价格波动水平的影响.
相关概念视频
Quantifying and Rejecting Outliers: The Grubbs Test
1.6K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.6K
Correlation and Causation
37.7K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
37.7K
Quantitative Analysis
308
Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
308
Wald-Wolfowitz Runs Test I
655
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
The test works...
655
Drug Concentration Versus Time Correlation
802
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
802
Wald-Wolfowitz Runs Test II
246
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
246


