油价动荡时期的出现
Emergence of Turbulent Epochs in Oil Prices
摘要：
石油价格数据具有复杂的多尺度结构，可能随时间变化。我们使用时频分析，以确定这些变化的主要特征，特别是政权转移。该分析是基于基于小波的分解和相关的尺度谱的分析。局部赫斯特指数和波动率的联合估计是发现和识别油价机制转换的关键。该框架特别涉及“多分数”型过程的建模，因此价格过程的粗糙度和波动性可能随时间而变化。特殊时期，然后出现作为这些自由度的结果，此外，作为所使用的特殊类型的谱估计的结果。这些特殊的时代进行了讨论，并与历史事件。其中一些是不能检测到的标准分析的基础上最大似然估计。本文提出了一种新的算法来鲁棒检测金融或其他类型的数据中的这种特殊时期和多分数行为。在金融背景下，对资产价格行为的洞察对于评估涉及资产的金融合同非常重要。
英文摘要：
Oil price data have a complicated multi-scale structure that may vary with time. We use time-frequency analysis to identify the main features of these variations and, in particular, the regime shifts. The analysis is based on a wavelet-based decomposition and analysis of the associated scale spectrum. The joint estimation of the local Hurst exponent and volatility is the key to detect and identify regime shifting and switching of the oil price. The framework involves in particular modeling in terms of a process of `multi-fractional' type so that both the roughness and the volatility of the price process may vary with time. Special epochs then emerge as a result of these degrees of freedom, moreover, as a result of the special type of spectral estimator used. These special epochs are discussed and related to historical events. Some of them are not detected by standard analysis based on maximum likelihood estimation. The paper presents a novel algorithm for robust detection of such special epochs and multi-fractional behavior in financial or other types of data. In the financial context insight about such behavior of the asset price is important to evaluate financial contracts involving the asset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
