Semiparametric Volatility Model with Varying Frequencies
容纳不同观测频率的半参数波动率模型AI 解读 ↓
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容纳不同观测频率的半参数波动率模型
- 研究问题
- 当协变量与目标变量的观测频率不同，如何避免向低频聚合造成的信息损失，并将高频信息纳入均值或波动率建模？
- 方法与思路
- 作者提出 VF-ARMA 与 VF-GARCH，分别把高频协变量以非参数函数加入均值方程和方差方程，并利用模型可加性进行混合估计。
- 材料陈述的贡献
- 摘要称模拟中两类模型比聚合协变量的 GARCH 和 GJR 具有更好的预测表现，且 VF-ARMA 在所考察情景中更稳健。
- 阅读关注点
- 应核对不同频率观测的时间对齐、非参数函数形式和混合估计步骤，检查预测对象、基准调参与所谓稳健性的具体含义。
- 阅读边界
- 所据为 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未说明频率组合、理论条件或优势能否推广到真实数据。
原文摘要
In extracting time series data from various sources, it is inevitable to compile variables measured at varying frequencies as this is often dependent on the source. Modeling from these data can be facilitated by aggregating high frequency data to match the relatively lower frequencies of the rest of the variables. This however, can easily loss vital information that characterizes the system ought to be modelled. Two semiparametric volatility models are postulated to account for covariates of varying frequencies without aggregation of the data to lower frequencies. First is an extension of the autoregressive integrated moving average with explanatory variable (ARMAX) model, it integrates high frequency data into the mean equation (VF-ARMA). Second is an extension of the Glosten, Jagannathan and Rankle (GJR) model that incorporates the high frequency data into the variance equation (VF-GARCH). In both models, high frequency data was introduced as a nonparametric function in the model. Both models are estimated using a hybrid estimation procedure that benefits from the additive nature of the models. Simulation studies illustrate the advantages of postulated models in terms of predictive ability compared to generalized autoregressive conditionally heteroscedastic (GARCH) and GJR models that simply aggregates high frequency covariates to the same frequency as the output variable. Furthermore, VF-ARMA is superior to VF-GARCH since it exhibits some degree of robustness in a wide range of scenarios.