Please use this identifier to cite or link to this item:
Title: Covariance Matrix Estimation with High Frequency Financial Data
Authors: LIU CHENG
Keywords: High frequency data; Integrated covariance matrix; Microstructure noise; Quasi-maximum likelihood; Kalman filter; Asynchronous data
Issue Date: 14-May-2013
Citation: LIU CHENG (2013-05-14). Covariance Matrix Estimation with High Frequency Financial Data. ScholarBank@NUS Repository.
Abstract: Estimating the integrated covariance matrix (ICM) from high frequency financial trading data is crucial to reflect the volatilities and covariations of trading instruments. Such an objective is difficult due to contaminated data with microstructure noises, asynchronous trading records, and increasing data dimensionality. In this paper, we study a quasi-maximum likelihood (QML) approah and a QKF approach, which is a combination of QML and Kalman filter, for estimating an ICM. We demonstrate that the QML and QKF estimators are consistent to the ICM, and are asymptotically normally distributed. Efficiency gain of the two approaches is theoretically quantified, and numerically demonstrated via extensive simulation studies. An application of the QML and QKF approaches is illustrated through analyzing a high frequency financial trading data set.
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
LiuC.PDF657.17 kBAdobe PDF



Page view(s)

checked on Dec 15, 2018


checked on Dec 15, 2018

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.