Stochastic properties of multivariate time series equations with emphasis on ARCH
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Stochastic properties of multivariate time series equations with emphasis on ARCH. / Rahbek, Anders.
I: IFAC Proceedings Volumes (IFAC-PapersOnline), Bind 36, Nr. 16, 2003, s. 227-232.Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt
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TY - GEN
T1 - Stochastic properties of multivariate time series equations with emphasis on ARCH
AU - Rahbek, Anders
PY - 2003
Y1 - 2003
N2 - Markov chain theory is applied to the nonlinear modelling of conditional variance with focus on the in financial econometrics widely applied class of multivariate autoregressive conditional heteroscedastic (ARCH) processes. The multivariate socalled BEKK-ARCH of Engle and Kroner (1995) as well as other multivariate ARCH processes in the literature are discussed. The results show that an essential regularity condition for the existence of moments is that the largest modulus of the eigenvalues or equivalently, that the spectral radius of a certain matrix Φ parametrizing the conditional heteroscedasticity in the ARCH process is smaller than one. Due to the fact that multivariate systems are considered it is demonstrated that an important step in the derivations is based on changing the measure of size of the matrix Φ from norm to spectral radius.
AB - Markov chain theory is applied to the nonlinear modelling of conditional variance with focus on the in financial econometrics widely applied class of multivariate autoregressive conditional heteroscedastic (ARCH) processes. The multivariate socalled BEKK-ARCH of Engle and Kroner (1995) as well as other multivariate ARCH processes in the literature are discussed. The results show that an essential regularity condition for the existence of moments is that the largest modulus of the eigenvalues or equivalently, that the spectral radius of a certain matrix Φ parametrizing the conditional heteroscedasticity in the ARCH process is smaller than one. Due to the fact that multivariate systems are considered it is demonstrated that an important step in the derivations is based on changing the measure of size of the matrix Φ from norm to spectral radius.
KW - Asymptotics
KW - Drift Criteria
KW - Geometric Ergodicity
KW - Markov Chain
KW - Multivariate ARCH
KW - Nonlinear processes
KW - Spectral Radius
UR - http://www.scopus.com/inward/record.url?scp=84894225565&partnerID=8YFLogxK
U2 - 10.1016/S1474-6670(17)34767-5
DO - 10.1016/S1474-6670(17)34767-5
M3 - Conference article
AN - SCOPUS:84894225565
VL - 36
SP - 227
EP - 232
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8963
IS - 16
T2 - 13th IFAC Symposium on System Identification, SYSID 2003
Y2 - 27 August 2003 through 29 August 2003
ER -
ID: 258714809