WHITTLE ESTIMATION OF ARCH MODELS

By Liudas Giraitis, Peter M. Robinson. Econometric Theory, 2000

99 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/1597e1ef7a3bd873fb29e2ce6496c1bf037af23f

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Citations in the corpus

Recent Advances in ARCH Modelling (2007, unknown): “of (50) which is asymptotically β−stable; more precisely, { N [Nτ ] ∑”
STATIONARITY AND MEMORY OF ARCH(∞) MODELS (2004, methodology): “…be relevant when imposing covariance stationarity of the et 2 in practical estimation, e+g+, when estimating ARCH~`! using the Whittle estimator~see Giraitis and Robinson, 2001!+ The equivalent~19! representation of~14! could be used to show that~14! strictly implies~11!+ In fact, settingu 5 2k 5…”
A CLOSED-FORM ESTIMATOR FOR THE GARCH(1,1) MODEL (2006, unknown): “However, the estimators of Franq and Zakoïan (2000) and Gouriéroux and Jasiak (2001) require numerical optimization.”
Testing for long memory in the presence of a general trend (2001, background): “Giraitis et al. (2000b) showed that if (E ξ40 ) 1/4 ∞∑ j=1 bj < 1, (2.7) then the Yk = ηk − E ηk satisfy conditions (i), and validity of (iii) of Assumption 2.1 was established in Giraitis and Robinson (2001).”
LARCH, leverage and long memory (2003, unknown): “17) by Giraitis and Robinson (2001). More ad hoc methods include generalized methods-of-moments estimation, for example comparing ht or its Fourier transform (the cross spectrum of Xt and X(2) t ) with sample estimates. However, a more basic question concerns the direct practical usefulness of the L”
Mixing properties of ARCH and time-varying ARCH processes (2011, methodology): “l call the squared ARCH(∞) process an ARCH(∞) process. It is worth mentioning that the GARCH(p,q) process has an ARCH(∞) representation, where the aj decay geometrically with j. Giraitis and Robinson [19], Robinson and Zaﬀaroni [34] and Subba Rao [37] consider parameter estimation for the ARCH(∞) pr”
Pseudo-maximum likelihood estimation of ARCH(∞) models (2005, background): “Under stronger restrictions than ∑∞ j=1ψ0j < 1, Giraitis and Robinson [14] considered discrete-frequency Whittle estimation of ζ0, based on the squared”
Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review (2004, methodology): “Giraitis and Robinson [148] estimated the parameters of the GARCH process using the Whittle estimation technique and demonstrated that the Whittle estimator is strongly consistent and asymptotically normal, provided the GARCH process has finite 8 moment marginal distribution.”
GARCH Models (2010, unknown): “Moreover, Giraitis and Robinson (2001), Mikosch and Straumann (2002) and Straumann (2005) have shown that the consistency requires the existence of E 4 t , and that the asymptotic normality requires E 8 t <∞.”
Nonstationarities in Financial Time Series, the Long-Range Dependence, and the IGARCH Effects (2004, methodology): “We are aware only of two references on this subject: Giraitis and Robinson (2001) and Mikosch and Straumann (2002).”
Long memory time series (2003, background): “…(2000) for existence of a covariance stationary solution of (4.5) rule out long memory, though they do permit strong autocorrelation in x2t that very closely approaches it, and Giraitis and Robinson (2001) have established asymptotic properties of Whittle estimates based on squares for this model.”