Structural change in any time series is practically unavoidable, and thus correctly detecting breakpoints plays a pivotal role in statistical modelling. This research considers segmented autoregressive models with exogenous variables and asymmetric GARCH errors, GJR-GARCH and exponential-GARCH specifications, which utilize the leverage phenomenon to demonstrate asymmetry in response to positive and negative shocks. The proposed models incorporate skew Student-t distribution and prove the advantages of the fat-tailed skew Student-t distribution versus other distributions when structural changes appear in financial time series. We employ Bayesian Markov Chain Monte Carlo methods in order to make inferences about the locations of structural change points and model parameters and utilize deviance information criterion to determine the optimal number of breakpoints via a sequential approach. Our models can accurately detect the number and locations of structural change points in simulation studies. For real data analysis, we examine the impacts of daily gold returns and VIX on S&P 500 returns during 2007–2019. The proposed methods are able to integrate structural changes through the model parameters and to capture the variability of a financial market more efficiently.

任何时间序列的结构变化实际上都是不可避免的，因此正确检测断点在统计建模中起着关键作用。本研究考虑了具有外生变量和不对称GARCH误差，GJR-GARCH和指数GARCH规范的分段自回归模型，这些模型利用杠杆现象证明了对正向和负向冲击的不对称性。所提出的模型结合了偏斜的Student-t分布，并证明了当金融时间序列中出现结构变化时，肥尾偏斜的Student-t分布相对于其他分布的优势。我们使用贝叶斯马尔可夫链蒙特卡罗方法来推断结构变化点和模型参数的位置，并利用偏差信息准则通过顺序方法确定最佳断点数。我们的模型可以在模拟研究中准确检测结构变化点的数量和位置。对于真实数据分析，我们检查了2007–2019年期间每日黄金收益率和VIX对标普500指数收益率的影响。所提出的方法能够通过模型参数整合结构变化，并更有效地捕获金融市场的可变性。我们研究了2007–2019年期间每日黄金收益率和VIX对标普500指数收益率的影响。所提出的方法能够通过模型参数整合结构变化，并更有效地捕获金融市场的可变性。我们研究了2007–2019年期间每日黄金收益率和VIX对标普500指数收益率的影响。所提出的方法能够通过模型参数整合结构变化，并更有效地捕获金融市场的可变性。