Evaluating GARCH models

By S. Lundbergh, Timo Teräsvirta. 2002

190 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/33be86fdb205004fa63f87a24cd4b222900dee17

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Citations in the corpus

PARAMETER ESTIMATION IN NONLINEAR AR–GARCH MODELS (2011, unknown): “Examples 1 and 2, but this is needed to verify Assumption DGP with the results of Meitz and Saikkonen (2008b). Condition (a.”
A Time Series Model for an Exchange Rate in a Target Zone with Applications (2006, unknown): “We assume that the alternative to constant parameters in either the conditional mean or the conditional variance or both is that the parameters change smoothly over time, see Lin and Teräsvirta (1994) and Lundbergh and Teräsvirta (2002). The changing parameters replacing φ in (3.”
Modelling the volatility of the Dow Jones Islamic Market World Index using a fractionally integrated time-varying GARCH (FITVGARCH) model (2014, methodology): “We use the test proposed by Lundbergh and Teräsvirta (2002) as a parametric LM-type test of no ARCH in standardized errors. This test was adapted to FITVGARCH model in Ben Nasr et al. (2010) by assuming that the misspecification structure in this model has the additive form”
Stylized Facts of Financial Time Series and Three Popular Models of Volatility (2004, background): “also considered in Malmsten (2004). There seems to be plenty of variation among the series.”
MODELING MULTIPLE REGIMES IN FINANCIAL VOLATILITY WITH A FLEXIBLE COEFFICIENT GARCH(1,1) MODEL (2009, methodology): “As observed in Lundbergh and Teräsvirta (2002), the robust version of the LM test should always be preferred to the nonrobust tests.”
Evaluating Models of Autoregressive Conditional Duration (2006, methodology): “This paper has similarities with the one by Lundbergh and Teräsvirta (2002) who derived misspecification tests for GARCH models.”
Autoregressive Conditional Duration Models in Finance: A Survey of the Theoretical and Empirical Literature (2008, methodology): “Following the work of Lundbergh and Teräsvirta (2002) on GARCH models who propose a Lagrange multiplier (LM) test of no residual ARCH,22 Meitz and Teräsvirta (2006) propose a similar statistic of no residual ACD which is shown to be asymptotically equivalent to the Li and Yu (2003) test and also…”