A multivariate regime-switching GARCH model with an application to global stock market and real estate equity returns
A multivariate regime-switching GARCH model with an application to global stock market and real estate equity returns
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DOI：
10.1515/snde-2016-0019
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发表时间：
2018-06-01
影响因子：
0.8
通讯作者：
Liu, Ji-Chun
中科院分区：
文献类型：
--
作者：
Haas, Markus;Liu, Ji-Chun
我们考虑了一个多变量马尔可夫转换的GARCH模型，该模型允许特定于政权的波动动态，杠杆效应和相关性结构。平稳的条件和表达式的时刻的过程中推导出来。提出了一种防止区域内相关动力学误设定的拉格朗日乘子检验方法，并推导了多步前条件协方差矩阵的简单递推公式。我们使用这种方法来模拟全球股票市场和真实的房地产股票收益率的联合分布的动态。实证分析强调了条件分布在马尔可夫切换时间序列模型中的重要性。具有学生t创新的规格在样本内和样本外都占主导地位。主导规格似乎是一个两个政权的学生的t过程的相关性是较高的动荡（高波动性）政权。
We consider a multivariate Markov-switching GARCH model which allows for regime-specific volatility dynamics, leverage effects, and correlation structures. Conditions for stationarity and expressions for the moments of the process are derived. A Lagrange Multiplier test against misspecification of the within-regime correlation dynamics is proposed, and a simple recursion for multi-step-ahead conditional covariance matrices is deduced. We use this methodology to model the dynamics of the joint distribution of global stock market and real estate equity returns. The empirical analysis highlights the importance of the conditional distribution in Markov-switching time series models. Specifications with Student's t innovations dominate their Gaussian counterparts both in-and out-of-sample. The dominating specification appears to be a two-regime Student's t process with correlations which are higher in the turbulent (high-volatility) regime.