Limit theory for the sample autocorrelations and extremes of a GARCH (1,1) process

By Thomas Mikosch, C. Starica. 2000

420 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/235f8dcea42cd8ea32c313e61aedc64d6368bcd9

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Citations in the corpus

Probabilistic Properties of Stochastic Volatility Models (2009, background): “Moreover, the limit theory for a heavy-tailed GARCH process (Xt) is of a completely different nature, see Davis and Mikosch [10], Mikosch and Stărică [30] and Basrak et al.”
Realised power variation and stochastic volatility models (2003, background): “There is evidence that if returns do not possess fourth moments then using absolute values rather than squares would be more reliable (see, for example, the work on the distributional behaviour of the correlogram of squared returns by Davis and Mikosch (1998) and Mikosch and Starica (2000)).”
Extreme Value Theory for GARCH Processes (2009, methodology): “For ARCH(1), GARCH(1, 1) and the general GARCH(p, q) processes (Xt) and their absolute values (|Xt|) the form of the limit point process N was determined in Davis and Mikosch [7], Mikosch and Stărică [32] and Basrak et al.”
GARCH Models (2010, neutral): “See Mikosch and Stărică (2000), Borkovec and Klüppelberg (2001), Basrak, Davis and Mikosch (2002) and Davis and Mikosch (2009).”
GARCH models without positivity constraints: Exponential or log GARCH? (2012, result): “This lack of clustering is also observed for the EGARCH(1,1) model in Mikosch and Rezapour (2012), in contrast with the GARCH(1,1) model, see Mikosch and Starica (2000).”
Extremal behavior of stochastic volatility models (2006, background): “This phenomenon can have various reasons, as for instance discussed in Mikosch and Stărică [31].”
Power variation and stochastic volatility: a review and some new results (2004, background): “There is evidence that if returns do not possess fourth moments then using absolute values rather than squares would be more reliable (see, for example, the work on the distributional behaviour of the correlogram of squared returns by Davis and Mikosch (1998) and Mikosch and Starica (2000) ).”
Method of Moment Estimation in the Cogarch(1,1) Model (2007, positive): “These stylized features are modelled by the GARCH family as has been shown for the GARCH(1,1) process in detail in [14].”
Pseudo-maximum likelihood estimation of ARCH(∞) models (2005, background): “However, the literature on asymptotic theory for estimates of GARCH models is now extensive, recent references including [3, 7, 12, 16, 22, 32], along with investigations of the properties of the models themselves; see recently [2, 18, 25].”
Empirical properties of asset returns: stylized facts and statistical issues (2001, methodology): “GARCH models, Mikosch and Starica [96] show that the”