Structured volatility matrix estimation for non-synchronized high-frequency financial data

Jianqing Fan, Donggyu Kim

Research output: Contribution to journalArticle

Abstract

Several large volatility matrix estimation procedures have been recently developed for factor-based Itô processes whose integrated volatility matrix consists of low-rank and sparse matrices. Their performance depends on the accuracy of input volatility matrix estimators. When estimating co-volatilities based on high-frequency data, one of the crucial challenges is non-synchronization for illiquid assets, which makes their co-volatility estimators inaccurate. In this paper, we study how to estimate the large integrated volatility matrix without using co-volatilities of illiquid assets. Specifically, we pretend that the co-volatilities for illiquid assets are missing, and estimate the low-rank matrix using a matrix completion scheme with a structured missing pattern. To further regularize the sparse volatility matrix, we employ the principal orthogonal complement thresholding method (POET). We also investigate the asymptotic properties of the proposed estimation procedure and demonstrate its advantages over using co-volatilities of illiquid assets. The advantages of our methods are also verified by an extensive simulation study and illustrated by high-frequency data for NYSE stocks.

Original languageEnglish (US)
Pages (from-to)61-78
Number of pages18
JournalJournal of Econometrics
Volume209
Issue number1
DOIs
StatePublished - Mar 2019

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

Keywords

  • Diffusion process
  • Factor model
  • High-frequency data
  • Low-rank matrix
  • Matrix completion
  • POET
  • Sparsity

Fingerprint Dive into the research topics of 'Structured volatility matrix estimation for non-synchronized high-frequency financial data'. Together they form a unique fingerprint.

  • Cite this