Estimating multivariate ARCH parameters by two-stage least-squares method

Authors:
Saman Mousazadeh;Mahmood Karimi
Affiliations:
Department of Electrical Engineering, Shiraz University, Shiraz, Iran and Iran Telecommunication Research Center (ITRC), Iran;Department of Electrical Engineering, Shiraz University, Shiraz, Iran
Venue:
Signal Processing
Year:
2009

Citing 3
Cited 2

Speech spectral modeling and enhancement based on autoregressive conditional heteroscedasticity models

Signal Processing
Underwater noise modeling and direction-finding based on heteroscedastic time series

EURASIP Journal on Applied Signal Processing
A Soft Voice Activity Detection Using GARCH Filter and Variance Gamma Distribution

IEEE Transactions on Audio, Speech, and Language Processing

Order selection criteria for vector autoregressive models

Signal Processing
Two dimensional noncausal AR-ARCH model: Stationary conditions, parameter estimation and its application to anomaly detection

Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

This paper discusses the asymptotic properties of the two-stage least-squares (TSLS) estimator of the parameters of multivariate autoregressive conditional heteroscedasticity (ARCH) model. The estimator is easy to obtain since it involves solving sets of linear equations. It will be shown that, under some conditions, this TSLS estimator is asymptotically consistent and its rate of convergence is the same as that of the quasi maximum likelihood estimator (QMLE). At the same time, the computational load of the TSLS estimator is extremely lower than that of the QMLE. The performance of the TSLS estimator will be evaluated and compared with QMLE using simulations. Simulation results show that the performances of the two estimators are comparable, even for small data records.