Asymptotic Theory for Extended Asymmetric Multivariate GARCH Processes

Manabu Asai and Michael McAleer

No 16-071/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: The paper considers various extended asymmetric multivariate conditional volatility models, and derives appropriate regularity conditions and associated asymptotic theory. This enables checking of internal consistency and allows valid statistical inferences to be drawn based on empirical estimation. For this purpose, we use an underlying vector random coefficient autoregressive process, for which we show the equivalent representation for the asymmetric multivariate conditional volatility model, to derive asymptotic theory for the quasi-maximum likelihood estimator. As an extension, we develop a new multivariate asymmetric long memory volatility model, and discuss the associated asymptotic properties.

Keywords: Multivariate conditional volatility; Vector random coefficient autoregressive process; Asymmetry; Long memory; Dynamic conditional correlations; Regularity conditions; Asymptotic properties (search for similar items in EconPapers)
JEL-codes: C13 C32 C58 (search for similar items in EconPapers)
Date: 2016-09-05
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Related works:
Working Paper: Asymptotic Theory for Extended Asymmetric Multivariate GARCH Processes (2016)
Working Paper: Asymptotic Theory for Extended Asymmetric Multivariate GARCH Processes (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20160071

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