Unified discrete-time factor stochastic volatility and continuous-time Itô models for combining inference based on low-frequency and high-frequency

Donggyu Kim, Xinyu Song and Yazhen Wang

Journal of Multivariate Analysis, 2022, vol. 192, issue C

Abstract: This paper introduces unified models for high-dimensional factor-based Itô process, which can accommodate both continuous-time Itô 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-Itô 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-Itô model in volatility prediction and portfolio allocation problems.

Keywords: Factor model; High dimensionality; POET; Quasi-maximum likelihood estimation; Stochastic volatility model (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Unified Discrete-Time Factor Stochastic Volatility and Continuous-Time Ito Models for Combining Inference Based on Low-Frequency and High-Frequency (2020)
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DOI: 10.1016/j.jmva.2022.105091

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