Modelling financial time series using GARCH-type models with a skewed student distribution for the innovations
By Philippe Lambert, Sébastien-Yves Laurent. 2001
158 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/247b7badf198f9768559ead650a538dc2cb1d762
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Stavros Degiannakis *
- G. Ali
- Kris Boudt
- P. Giot
- Sébastien-Yves Laurent
- Brian G. Peterson
- Evdokia Xekalaki
- Christophe Croux
Citations in the corpus
- Modelling daily value-at-risk using realized volatility and arch type models (2001, unknown): “See Lambert and Laurent (2001) for a discussion of the link between these two parameters and the skewness and the kurtosis.”
- American Option Pricing Using GARCH Models and the Normal Inverse Gaussian Distribution (2008, unknown): “In addition to these leptokurtic distributions various skewed distributions, like e.g. the Skewed Student’s t-distribution, 2 have also been used (see e.g. Lambert & Laurent (2001) which also lists a number of different contributions in this area).”
- EGARCH, GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH and APARCH Models for Pathogens at Marine Recreational Sites (2013, methodology): “Lambert and Laureen [13] used it in the GARCH framework.”
- Estimating stock market volatility using asymmetric GARCH models (2008, methodology): “In fact, Lambert and Laurent (2001) found that the skewed Student-t density is more appropriate for modelling the NASDAQ index than symmetric densities.”
- Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review (2004, background): “Lambert and Laurent [192, 193] extended the skewed Student t density proposed by Fernandez and Steel [127] to the ARCH framework, in the following density function:”
- Value-at-Risk for long and short trading positions (2003, background): “In order to keep in the ARCH ‘tradition’, Lambert and Laurent (2001) re-expressed the skewed Student density in terms of the mean and the variance, i.e. re-parameterize this density in such a way that the innovation process has zero mean and unit variance.”
- Estimation and Decomposition of Downside Risk for Portfolios with Non-Normal Returns (2008, methodology): “In Appendix D we recall Lambert and Laurent (2001)’s expression for the skewness, kurtosis and quantile function of this skewed Student t distribution.”