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ASYMPTOTIC THEORY FOR A FACTOR GARCH MODEL

Christian Matthias Hafner · Arie Preminger

46 citations0/0 files storedEconometric Theory

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Complex Systems and Time Series Analysis

Abstract

This paper investigates the asymptotic theory for a factor GARCH (generalized autoregressive conditional heteroskedasticity) model. Sufficient conditions for asymptotic stability and existence of moments are established. These conditions allow for volatility spillover and integrated GARCH. We then show the strong consistency and asymptotic normality of the quasi–maximum likelihood estimator (QMLE) of the model parameters. The results are obtained under the finiteness of the fourth-order moment of the innovations.

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References

2002Dynamic Conditional Correlation 1995Multivariate Simultaneous Generalized ARCH 1990Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized Arch Model 1992Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances 1988A Capital Asset Pricing Model with Time-Varying Covariances n.d.Multivariate GARCH models: a survey 2002A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model With Time-Varying Correlations 2000Dynamic Conditional Correlation - a Simple Class of Multivariate GARCH Models

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2015Estimation and inference in univariate and multivariate log-GARCH-X models when the conditional density is unknown 2009Sequential conditional correlations: Inference and evaluation 2013Proximity-Structured Multivariate Volatility Models 2019Multivariate GARCH models for large-scale applications: A survey

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