Least squares estimation of ARCH models with missing observations

Pascal Bondon; Natalia Bahamonde

doi:10.1111/j.1467-9892.2012.00803.x

Least squares estimation of ARCH models with missing observations

Pascal Bondon, Natalia Bahamonde

INSTITUTE OF STATISTICS

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

A least squares estimator for ARCH models in the presence of missing data is proposed. Strong consistency and asymptotic normality are derived. Monte Carlo simulation results are analysed and an application to real data of a Chilean stock index is reported.

Original language	English
Pages (from-to)	880-891
Number of pages	12
Journal	Journal of Time Series Analysis
Volume	33
Issue number	6
DOIs	https://doi.org/10.1111/j.1467-9892.2012.00803.x
State	Published - Nov 2012

Keywords

ARCH models
Conditional heteroscedasticity
Least squares estimation
Martingale central limit theorem
Missing observations

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10.1111/j.1467-9892.2012.00803.x

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title = "Least squares estimation of ARCH models with missing observations",

abstract = "A least squares estimator for ARCH models in the presence of missing data is proposed. Strong consistency and asymptotic normality are derived. Monte Carlo simulation results are analysed and an application to real data of a Chilean stock index is reported.",

keywords = "ARCH models, Conditional heteroscedasticity, Least squares estimation, Martingale central limit theorem, Missing observations",

author = "Pascal Bondon and Natalia Bahamonde",

year = "2012",

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AU - Bahamonde, Natalia

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KW - ARCH models

KW - Conditional heteroscedasticity

KW - Least squares estimation

KW - Martingale central limit theorem

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DO - 10.1111/j.1467-9892.2012.00803.x

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JF - Journal of Time Series Analysis

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ER -

Least squares estimation of ARCH models with missing observations

Abstract

Keywords

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