Structured Volatility Matrix Estimation for Non-Synchronized High-Frequency Financial Data

35 Pages Posted: 13 Dec 2017

See all articles by Jianqing Fan

Jianqing Fan

Princeton University - Bendheim Center for Finance

Donggyu Kim

KAIST College of Business

Date Written: November 30, 2017

Abstract

Recently several large volatility matrix estimation procedures have been developed for factor-based Ito 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 financial data for constituents of the S&P 500 index.

Keywords: Diffusion process, factor model, high-frequency data, low-rank matrix, matrix completion, POET, sparsity

JEL Classification: C13, C32, C55

Suggested Citation

Fan, Jianqing and Kim, Donggyu, Structured Volatility Matrix Estimation for Non-Synchronized High-Frequency Financial Data (November 30, 2017). Available at SSRN: https://ssrn.com/abstract=3085737 or http://dx.doi.org/10.2139/ssrn.3085737

Jianqing Fan

Princeton University - Bendheim Center for Finance ( email )

26 Prospect Avenue
Princeton, NJ 08540
United States
609-258-7924 (Phone)
609-258-8551 (Fax)

HOME PAGE: http://orfe.princeton.edu/~jqfan/

Donggyu Kim (Contact Author)

KAIST College of Business ( email )

85 Hoegiro Dongdaemun-Gu
Seoul 130-722, 130-722
Korea, Republic of (South Korea)

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