Stochastic volatility models: Conditional normality versus heavy tailed distributions

By Roman Liesenfeld, Robert C. Jung. 2000

155 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/ae04dd9c82230ece36258f3bda8d180cb1ed764a

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In Economics and Econometrics

1. Esther Ruiz
2. Carmen Broto
3. Manabu Asai
4. Fanyin Zhou
5. L. Rogers

In Finance

1. R. Delgado
2. Esther Ruiz
3. Carmen Broto
4. Aníbal Costa
5. X. Romão
6. Manabu Asai
7. Fanyin Zhou
8. L. Rogers

Citations in the corpus

• Autoregressive Stochastic Volatility Models with Heavy-tailed Distributions: A Comparison with Multifactor Volatility Models (2008, unknown): “Liesenfeld and Jung (2000) employed the two former distributions in the SV models, while Watanabe and Asai (2003) considered all three in capturing the high kurtosis of returns.”
• Persistence and Kurtosis in GARCH and Stochastic Volatility Models (2004, unknown): “, Liesenfeld and Jung (2000) and Loudon, Watt, and Yadav (2000), among many others].”
• Periodic Stochastic Volatility and Fat Tails (2005, unknown): “For a Simulated Maximum Likelihood (SML) estimation method of heavy-tailed SV models see Liesenfeld and Jung (2000).”
• An empirical power comparison of univariate goodness-of-fit tests for normality (2010, background): “The evaluation of this distributional assumption has been addressed, for example, in [2] where the conditional normality assumption in the sample selection model applied to housing demand is examined, or in [3,4] where the normality assumption has been addressed in the context of stock market data, ”
• Estimation methods for stochastic volatility models: a survey (2004, methodology): “…heavy-tailed distributions [see, for example, Nelson (1988), Harvey et al. (1994), Barndorff-Nielsen (1997), Gallant et al. (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].”
• Extreme Downside Risk and Expected Stock Returns (2012, unknown): “Some researchers adopt leptokurtic distributions such as student’s t (Liesenfeld and Jung, 2000) and those with higher-order moments (Chung et al., 2006) to capture information endowed in the fat tails.”
• Bayesian semiparametric stochastic volatility modeling (2010, unknown): “However, parametric SVmodels have not fully captured the asymmetries and leptokurtotic behavior present in return data (see Gallant et al., 1997; Mahieu and Schotman, 1998; Liesenfeld and Jung, 2000; Meddahi, 2001; Durham, 2006).”