Least squares estimation of ARCH models with missing observations

Pascal Bondon; Natalia Bahamonde

doi:10.1111/j.1467-9892.2012.00803.x

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Least squares estimation of ARCH models with missing observations

Pascal Bondon Natalia Bahamonde

Year: 2012 Journal: Journal of Time Series Analysis Vol: 33 (6)Pages: 880-891 Publisher: Wiley

DOI: 10.1111/j.1467-9892.2012.00803.x

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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:

Mathematics Asymptotic distribution Missing data Estimator Statistics Arch Consistency (knowledge bases) Strong consistency Monte Carlo method Least-squares function approximation Econometrics Applied mathematics Geography

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Financial Risk and Volatility Modeling

Social Sciences → Economics, Econometrics and Finance → Finance

Monetary Policy and Economic Impact

Social Sciences → Economics, Econometrics and Finance → General Economics, Econometrics and Finance

Complex Systems and Time Series Analysis

Social Sciences → Economics, Econometrics and Finance → Economics and Econometrics

Least squares estimation of ARCH models with missing observations

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