Persistence and Kurtosis in GARCH and Stochastic Volatility Models

By M. A. Carnero, D. Pena, Esther Ruiz. 2004

208 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/44fba7ba9875700c7a11f5279f00bcd0e2fbaae0

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Citations in the corpus

Time-dependent series variance learning with recurrent mixture density networks (2013, methodology): “Empirical investigations were conducted with the following objectives: (1) to examine the influence of the network architecture and dynamic training on mixture density time series modeling; and (2) to compare linear GARCH with RMDN-GARCH and other recurrent density networks.”
An Introduction to Univariate GARCH Models (2006, background): “For evidence of this, see Malmsten and Terasvirta (2004) and Carnero et al. (2004) .”
MODELING MULTIPLE REGIMES IN FINANCIAL VOLATILITY WITH A FLEXIBLE COEFFICIENT GARCH(1,1) MODEL (2009, methodology): “Other models such as the GARCH and the EGARCH models are not able to reproduce adequately the previously mentioned stylized facts of financial time series; see Malmsten and Teräsvirta (2004) and Carnero, Peña, and Ruiz (2004) for comprehensive discussions.”
Stylized Facts of Financial Time Series and Three Popular Models of Volatility (2004, result): “It may be noticed, however, that the plug-in kurtosis estimate from the ARSV model is considerably higher than the corresponding estimate from the GARCH model with normal errors, a fact previously emphasized by Carnero et al. (2004), but lower than the estimate from the GARCH model with t-distribute”
Realized Volatility: A Review (2008, background): “( 2004 ), among others, most of the latent volatility models fail to describe satisfactorily several stylized facts that are observed in financial time series.”
A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries (2008, background): “However, as observed by Bollerslev (1987), Malmsten andTer̈asvirta (2004), and Carnero, Pẽna, and Ruiz (2004), among others, most of the latent volatility models have been unable to capture simultaneously several important empirical features that are inhernt in financial time series.”