Work

Conditional Heteroskedasticity Driven by Hidden Markov Chains

Year: 2001

Type: article

Abstract: We consider a generalized autoregressive conditionally heteroskedastic (GARCH) equation where the coefficients depend on the state of a nonobserved Markov chain. Necessary and sufficient conditions ensuring the existence of a stationary solution are given. In the case of ARCH regimes, the maximum likelihood estimates are shown to be consistent. The identification problem is also considered. This is illustrated by means of real and simulated data sets.

Source: Journal of Time Series Analysis

DOI: 10.1111/1467-9892.00219

Authors: Christian Francq, Michel Roussignol, Jean‐Michel Zakoian

Institutions: Université du littoral côte d'opale, Université Paris-Est Marne-la-Vallée, Université de Lille, Centre de Recherche en Économie et Statistique, Charles de Gaulle Université de Lille III

Language: English

Topic: Financial Risk and Volatility Modeling

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W2019733618

FWCI: 3.387

Cites:

Cited by: 100

Related to: 10