Whittle estimation in a heavy-tailed GARCH(1,1) model

By Thomas Mikosch, D. Straumann. 2002

51 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/e0579d5dfb412e33bb96c7838743be5713723357

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Thomas Mikosch
2. Remigijus Leipus
3. Oliver B. Linton
4. Ctlin Stric
5. Liudas Giraitis
6. Donatas Surgailis
7. C. Starica
8. Hui Wang

In Finance

1. Thomas Mikosch
2. Donatas Surgailis
3. Liudas Giraitis
4. Remigijus Leipus
5. Oliver B. Linton
6. Ctlin Stric
7. Hui Wang
8. C. Starica

Citations in the corpus

• Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review (2004, background): “Moreover, Mikosch and Straumann (2002) showed that the Whittle estimator is consistent as long as the 4th moment is finite and inconsistent when the 4th moment is infinite.”
• GARCH Models (2010, unknown): “1), whereas the usual estimators have nonstandard asymptotic distributions under (ii) (see Berkes and Horváth, 2003b; Hall and Yao, 2003; Mikosch and Straumann, 2002), which causes difficulties for inference.”
• 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).”
• Recent Advances in ARCH Modelling (2007, result): “Let us note, finally, the result of Mikosch and Straumann (2002), who showed some non-standard properties of the Whittle estimator in a heavytailed GARCH(1, 1) model.”
• Changes of structure in financial time series and the GARCH model (2004, methodology): “The Whittle parameter estimators of a GARCH process are consistent if EX 4 < ∞, and so are the Gaussian quasi maximum likelihood estimators; see Giraitis and Robinson [22] and Mikosch and Straumann [33] for the former case and Berkes et al. [5] for the latter case.”
• QUASI-MAXIMUM LIKELIHOOD ESTIMATION OF SEMI-STRONG GARCH MODELS (2009, unknown): “Hall and Yao (2003) and Mikosch and Straumann (2002) discussed the case where E[ε4 t ] = ∞; see also Linton et al. (2007). Lee and Hansen (1994) assumed that τt = E[ε4 t | Ft−1] ≤ C < ∞ a.s. Under constant conditional kurtosis, i.e., τt = E[ε4 t | Ft−1] ≡ τ, I = (τ −1)J , and the asymptotic variance”
• ESTIMATION FOR A NONSTATIONARY SEMI-STRONG GARCH(1,1) MODEL WITH HEAVY-TAILED ERRORS (2009, methodology): “Mikosch and Straumann (2002) adapted Whittle estimation to a heavy-tailed GARCH(1,1) model where Xt has a Pareto-like tail with tail index κ > 4.”