非对称长记忆GARCH：二维马尔可夫链中依赖符号的核注入
Asymmetric Long-Memory GARCH: Sign-Dependent Kernel Injection in a Two-Dimensional Markov Chain
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中文总结 AI 辅助
本文提出非对称长记忆GARCH模型ALM-GARCH，通过正负新息的不同注入幅度和核偏移定义水平与记忆通道，实证表明联合对称性被拒绝，记忆通道在部分市场中有效，样本外表现与基准相当。
中文摘要 AI 辅助
我们引入了ALM-GARCH，一种非对称长记忆GARCH模型，其中正负新息以不同的注入幅度和核偏移进入条件方差。这些偏离定义了一个相对于嵌套对称基准的可检验的水平和记忆通道。在Foster-Lyapunov条件下，内部配置的正Harris重现性成立。在五个股票指数和比特币中，联合对称性在整个过程中被拒绝，主要由水平通道驱动。记忆通道在日经225、韩国综合股价指数和比特币中得到支持，但在正分支几乎不活跃时识别较弱。样本外表现与标准基准大致相当。
英文摘要
We introduce ALM-GARCH, an asymmetric long-memory GARCH model in which positive and negative innovations enter conditional variance with different injection amplitudes and kernel offsets. These departures define testable level and memory channels relative to a nested symmetric benchmark. Positive Harris recurrence holds for interior configurations under a Foster-Lyapunov condition. Across five equity indices and Bitcoin, joint symmetry is rejected throughout, driven primarily by the level channel. The memory channel is supported for the Nikkei 225, KOSPI, and Bitcoin but is weakly identified when the positive branch is nearly inactive. Out-of-sample performance is broadly comparable to standard benchmarks.
发表机构
- Department of Statistics, Yeungnam University（荣州大学统计系）
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