On the Normal Inverse Gaussian Stochastic Volatility Model

By Jonas Andersson. 2001

94 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/01c932e6d96b996269de540033f3e453a25bc078

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Citations in the corpus

The Generalized Hyperbolic Skew Student’s t-Distribution (2006, methodology): “For these reasons, it has been used repeatedly for financial applications, both as the conditional distribution of a GARCH-model (Andersson, 2001; Jensen and Lunde, 2001; Forsberg and Bollerslev, 2002; Venter and de Jongh, 2002) and as the unconditional †Address for correspondence: The Norwegian…”
Persistence and Kurtosis in GARCH and Stochastic Volatility Models (2004, result): “Similar results have been found by Shephard (1996), Kim, Shephard, and Chib (1998), Hafner and Herwartz (2000), and Anderson (2001). Second, the gaussianity assumption for the innovations, «t, is adequate for ARSV(1) models, while the noise of the GARCH(1,1) specification requires a distribution wit”
Estimation methods for stochastic volatility models: a survey (2004, background): “(1997), Mahieu and Schotman (1998), Sandmann and Koopman (1998), Steel (1998), Liesenfeld and Jung (2000), Anderson (2001) and Watanabe and Asai (2001) among many others].”
American Option Pricing Using GARCH Models and the Normal Inverse Gaussian Distribution (2008, unknown): “Barndorff-Nielsen (1997), Andersson (2001), and Jensen & Lunde (2001). One particularly appealing property of the Normal Inverse Gaussian distribution is that its use can be motivated through a Mixing Distribution Hypothesis (see Clark (1973)). In particular, if asset returns are assumed to be norma”
Value at Risk with time varying variance, skewness and kurtosis--the NIG-ACD model (2009, unknown): “Among the models that account for the excess kurtosis not captured by the Gaussian GARCH (GARCH-n) model is the model of Barndor¤-Nielsen (1997) based on the Normal Inverse Gaussian (NIG) distribution.”
Stylized Facts of Financial Time Series and Three Popular Models of Volatility (2004, methodology): “Liesenfeld & Jung (2000) discussed this stylized fact in connection with the ARSV model, whereas Andersson (2001) focussed on the ARSV model based on the normal inverse Gaussian distribution. Carnero, Peña & Ruiz (2004) compared the ARSV model and the GARCH model using the kurtosisautocorrelation re”
Bridging the gap between the distribution of realized (ECU) volatility and ARCH modelling (of the Euro): the GARCH‐NIG model (2002, methodology): “The success of the ARCH/GARCH class of models at capturing volatility clustering in financial markets is well documented (see, for example, Bollerslev, Chou and Kroner, 1992). Meanwhile the inability of the ARCH/GARCH models coupled with the auxiliary assumption of conditionally normally distributed”