Kurtosis of GARCH and stochastic volatility models with non-normal innovations

By Xuezheng Bai, Jeffrey R. Russell, G. Tiao. 2003

181 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/793786b8fd8de2416dce723855e9570ed0fccbbc

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Citations in the corpus

Asymptotic Properties of GARCH-X Processes (2015, methodology): “For example, Bollerslev (1987) advocates the use of innovations with the t-distribution, and Bai et al. (2003) considers innovations following a mixture of two normal distributions.”
Conditional (2019, unknown): “The effectiveness of these methods heavily depends on the degree of disentanglement between identity and attributes, which is often unsatisfactory.”
The Volatility of Realized Volatility (2005, methodology): “Note that these extensions can also be adopted in the ARFIMA framework and have, in part, been considered in Baillie et al. (1996), who propose an ARFIMA–GARCH model to analyze inflation.”
Persistence and Kurtosis in GARCH and Stochastic Volatility Models (2004, background): “Furthermore, even if the persistence of the volatility is high, high kurtosis is associated with values of r2(1) larger than the values often observed in practice [see also Teräsvirta (1996) and Bai, Russell, and Tiao (2003)].”
GARCH option pricing: A semiparametric approach (2008, background): “Bai et al. (2003) showed that the returns kurtosis implied by a normal GARCH model is typically much smaller than the sample kurtosis observed for most returns time series.”
Bayesian Inference Methods for Univariate and Multivariate GARCH Models: A Survey (2014, unknown): “Therefore, it is common to assume a Student-t distribution, see Bollerslev (1987), He & Teräsvirta (1999) and Bai et al. (2003), among others.”
Estimation methods for stochastic volatility models: a survey (2004, background): “Bai et al. (2003) show that the contribution to the overall kurtosis of returns of the kurtosis of the innovations "t and the kurtosis due to the volatility clustering is symmetric.”