This paper introduces a volatility model with a component structure allowing for a realized measure based on high-frequency data (e.g realized variance) to drive the short-run volatility dynamics. In a joint model of the daily return and the realized measure, the conditional variance of the daily return has a multiplicative component structure: the first component traces long-run (secular) volatility trends, while the second component captures short-run (transitory) movements in volatility. Despite being a fixed-parameter model, its component structure implies time-varying parameters, which are data-driven to capture changing volatility regimes. We discuss the model properties and estimation by maximum likelihood. The empirical analysis reveals strong out-of-sample performance compared to benchmark models. This is demonstrated using unconditional and conditional predictive ability tests, and also using the model confidence set.

中文翻译：

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使用基于实测值的组件模型进行波动率预测*

本文介绍了一种具有组件结构的波动率模型，该模型允许基于高频数据（例如，实现的方差）进行实际测量，以驱动短期波动动态。在日收益率和已实现的度量的联合模型中，日收益率的条件方差具有可乘的成分结构：第一个成分追踪长期（长期）波动趋势，而第二个成分捕捉短期（暂时）波动趋势波动率的波动。尽管是固定参数模型，但其组件结构仍包含随时间变化的参数，这些参数是由数据驱动的以捕获变化的波动状态。我们讨论模型的性质和最大似然估计。经验分析显示，与基准模型相比，样本外性能强。