Financial and Oil Market’s Co-Movements by a Regime-Switching Copula

IF 1.1 Q3 ECONOMICS
Manel Soury
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Abstract

Over the years, oil prices and financial stock markets have always had a complex relationship. This paper analyzes the interactions and co-movements between the oil market (WTI crude oil) and two major stock markets in Europe and the US (the Euro Stoxx 50 and the SP500) for the period from 1990 to 2023. For that, I use both the time-varying and the Markov copula models. The latter one represents an extension of the former one, where the constant term of the dynamic dependence parameter is driven by a hidden two-state first-order Markov chain. It is also called the dynamic regime-switching (RS) copula model. To estimate the model, I use the inference function for margins (IFM) method together with Kim’s filter for the Markov switching process. The marginals of the returns are modeled by the GARCH and GAS models. Empirical results show that the RS copula model seems adequate to measure and evaluate the time-varying and non-linear dependence structure. Two persistent regimes of high and low dependency have been detected. There was a jump in the co-movements of both pairs during high regimes associated with instability and crises. In addition, the extreme dependence between crude oil and US/European stock markets is time-varying but also asymmetric, as indicated by the SJC copula. The correlation in the lower tail is higher than that in the upper. Hence, oil and stock returns are more closely joined and tend to co-move more closely together in bullish periods than in bearish periods. Finally, the dependence between WTI crude oil and the SP500 stock index seems to be more affected by exogenous shocks and instability than the oil and European stock markets.
用制度转换 Copula 分析金融市场和石油市场的共同走势
多年来,石油价格和金融股票市场一直有着复杂的关系。本文分析了 1990 年至 2023 年期间石油市场(WTI 原油)与欧洲和美国两大股票市场(欧洲斯托克 50 指数和 SP500 指数)之间的相互作用和共同运动。为此,我使用了时变模型和马尔可夫 copula 模型。后者是前者的延伸,其中动态依赖参数的常数项由隐藏的两态一阶马尔科夫链驱动。它也被称为动态制度切换(RS)copula 模型。为了估计该模型,我使用了边际推断函数(IFM)方法和马尔科夫切换过程的金氏滤波器。收益率的边际由 GARCH 模型和 GAS 模型建模。实证结果表明,RS copula 模型似乎足以衡量和评估时变和非线性依赖结构。发现了高依存度和低依存度的两种持续状态。在与不稳定和危机相关的高依存度期间,这两对数据的共同运动出现了跳跃。此外,原油与美国/欧洲股票市场之间的极端依赖性是时变的,但也是不对称的,正如 SJC 协方图所示。下部尾部的相关性高于上部尾部。因此,石油和股票收益率的联系更为紧密,在看涨时期往往比看跌时期的共同走势更为紧密。最后,WTI 原油和 SP500 股指之间的依赖关系似乎比石油市场和欧洲股市更容易受到外来冲击和不稳定因素的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Econometrics
Econometrics Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
2.40
自引率
20.00%
发文量
30
审稿时长
11 weeks
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