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Activity Number: 30 - SPEED: Statistics and Econometrics
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #328936 Presentation
Title: Factor GARCH-Ito Models for High-Frequency Data with Application to Large Volatility Matrix Prediction
Author(s): Donggyu Kim* and Jianqing Fan
Companies: KAIST and Princeton University
Keywords: factor model; GARCH; low-rank; POET; QMLE; sparsity

Several novel large volatility matrix estimation methods have been developed based on the high-frequency financial data. They often employ the approximate factor model that leads to a low-rank plus sparse structure for the integrated volatility matrix and facilitates estimation of large volatility matrices. However, for predicting future volatility matrices, these nonparametric estimators do not have a dynamic structure to implement. In this paper, we introduce a novel Ito diffusion process based on the approximate factor models and call it a factor GARCH-Ito model. We then investigate its properties and propose a quasi-maximum likelihood estimation method for the parameter of the factor GARCH-Ito model. We also apply it to estimating conditional expected large volatility matrices and establish their asymptotic properties. Simulation studies are conducted to validate the finite sample performance of the proposed estimation methods.

Authors who are presenting talks have a * after their name.

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