Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model

By James Davidson. 2004

432 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/ec12c5af6e0e01fbbd3560a0294d741f09aa91db

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Citations in the corpus

A bootstrap-assisted spectral test of white noise under unknown dependence (2011, methodology): “Note that the IGARCH modelM3 does not admit a finite second moment and the GARCH model M2 admits a finite second moment but the fourth moment is infinite; see Davidson (2004) and Ling and McAleer (2002) for sufficient and necessary conditions on the existence of higher order moments for GARCH models”
Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performance (2018, neutral): “The long memory parameter d ∈ (0 , 1) reﬂects the persistence of shocks, which rises when d approaches zero (Davidson, 2004).”
Long memory and structural breaks in modeling the return and volatility dynamics of precious metals (2012, methodology): “The hyperbolic GARCH model or HYGARCH proposed by Davidson (2004) is viewed as a more general version of the FIGARCH model with hyperbolic convergence rates, where shock amplitude and long memory are treated separately.”
VOLATILITY AND CORRELATION FORECASTING (2006, background): “An alternative formulation which breaks the link between the conditions for second-order stationarity and long-memory dependencies have been proposed by Davidson (2004).”
Inequality Constraints in the Fractionally Integrated GARCH Model (2006, methodology): “Alternative specifications were suggested by Davidson (2004), Giraitis, Robinson, and Surgailis (2004), Karanasos, Psaradakis, and Sola (2004), and Zaffaroni (2004).”
Recent Advances in ARCH Modelling (2007, background): “The above fact holds independently of (29) and requires (30) only.”
Non-Negativity Conditions for the Hyperbolic GARCH Model (2010, positive): “Therefore, Davidson (2004) constructed the hyperbolic GARCH (HYGARCH) model in a way to overcome this drawback.”
Modeling Long Memory and Structural Breaks in Conditional Variances: an Adaptive FIGARCH Approach (2009, background): “…Engle (1993) and Dacorogna et al. (1993) led to the development of the long memory stochastic volatility models of Breidt, Crato and de Lima (1998) and Harvey (1998), and the long memory ARCH models of Baillie, Bollerslev and Mikkelsen (1996), Bollerslev and Mikkelsen (1996) and Davidson (2004).”
Volatility processes and volatility forecast with long memory (2004, background): “Therefore, the exact asymptotic results obtained by Davidson (2002) and Zaffaroni (2000) that should be applied to the present models are those relative to the geometric decaying coefficients (i.e. exponential).”
Glossary to ARCH (GARCH) (2008, methodology): “HYGARCH (Hyperbolic GARCH) The HYGARCH model proposed by Davidson (2004) nests the GARCH, IGARCH and FIGARCH models (see GARCH, IGARCH and FIGARCH).”
ON TAIL INDEX ESTIMATION FOR DEPENDENT, HETEROGENEOUS DATA (2010, background): “Davidson (2004) shows that {Xt } is L1- or L2-NED on { t } if ∑∞i=1 πi 1, which neglects IGARCH and GARCH with explosive roots.”