Targeting estimation of CCC-GARCH models with infinite fourth moments

TY - JOUR

T1 - Targeting estimation of CCC-GARCH models with infinite fourth moments

AU - Pedersen, Rasmus Søndergaard

PY - 2014/12/4

Y1 - 2014/12/4

N2 - As an alternative to quasi-maximum likelihood, targeting estimation is a much applied estimation method for univariate and multivariate GARCH models. In terms of variance targeting estimation, recent research has pointed out that at least finite fourth moments of the data generating process is required, if one wants to perform inference in GARCH models by relying on asymptotic normality of the estimator. Such moment conditions may not be satisfied in practice for financial returns, highlighting a potential drawback of variance targeting estimation. In this paper, we consider the large-sample properties of the variance targeting estimator for the multivariate extended constant conditional correlation GARCH model when the distribution of the data generating process has infinite fourth moments. Using nonstandard limit theory, we derive new results for the estimator stating that, under suitable conditions, its limiting distribution is multivariate stable. The rate of consistency of the estimator is slower than and depends on the tail shape of the data generating process. A simulation study illustrates the derived properties of the targeting estimator.

AB - As an alternative to quasi-maximum likelihood, targeting estimation is a much applied estimation method for univariate and multivariate GARCH models. In terms of variance targeting estimation, recent research has pointed out that at least finite fourth moments of the data generating process is required, if one wants to perform inference in GARCH models by relying on asymptotic normality of the estimator. Such moment conditions may not be satisfied in practice for financial returns, highlighting a potential drawback of variance targeting estimation. In this paper, we consider the large-sample properties of the variance targeting estimator for the multivariate extended constant conditional correlation GARCH model when the distribution of the data generating process has infinite fourth moments. Using nonstandard limit theory, we derive new results for the estimator stating that, under suitable conditions, its limiting distribution is multivariate stable. The rate of consistency of the estimator is slower than and depends on the tail shape of the data generating process. A simulation study illustrates the derived properties of the targeting estimator.

U2 - 10.1017/S0266466615000316

DO - 10.1017/S0266466615000316

M3 - Journal article

SN - 0266-4666

VL - 32

SP - 498

EP - 531

JO - Econometric Theory

JF - Econometric Theory

IS - 02

ER -