论文标题
统一的离散时间因子随机波动率和连续的时间ITO模型，用于基于低频和高频结合推理
Unified Discrete-Time Factor Stochastic Volatility and Continuous-Time Ito Models for Combining Inference Based on Low-Frequency and High-Frequency
论文作者
论文摘要
本文介绍了基于高维因子的ITO过程的统一模型，该模型可以通过将离散的SV模型嵌入连续的瞬时因子波动率过程中，从而适应连续的时间ITO扩散和离散时间随机波动率（SV）模型。我们称其为SV-ITO模型。基于每日集成因子波动率矩阵估计器的系列，我们提出了准最大最大可能性和最小二乘估计方法。建立了它们的渐近特性。我们采用提出的方法来预测研究渐近行为的未来大量波动矩阵。进行了仿真研究，以检查提出的估计和预测方法的有限样本性能。进行了经验分析，以证明SV-ITO模型在波动性预测和投资组合分配问题中的优势。
This paper introduces unified models for high-dimensional factor-based Ito process, which can accommodate both continuous-time Ito diffusion and discrete-time stochastic volatility (SV) models by embedding the discrete SV model in the continuous instantaneous factor volatility process. We call it the SV-Ito model. Based on the series of daily integrated factor volatility matrix estimators, we propose quasi-maximum likelihood and least squares estimation methods. Their asymptotic properties are established. We apply the proposed method to predict future vast volatility matrix whose asymptotic behaviors are studied. A simulation study is conducted to check the finite sample performance of the proposed estimation and prediction method. An empirical analysis is carried out to demonstrate the advantage of the SV-Ito model in volatility prediction and portfolio allocation problems.
