The Generalized Hyperbolic Skew Student’s t-Distribution

By Kjersti Aas, Ingrid Hobæk Haff. 2006

370 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/676106048391c3c43a6a2a2ce07d71f510fc57a2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

In Global and Planetary Change

In Statistics and Probability

Citations in the corpus

Modelling dependence using skew t copulas: Bayesian inference and applications (2012, neutral): “DISCUSSION Various skew t distributions have been applied to problems in econometrics and elsewhere; for example, see Aas and Haff (2006) and Panagiotelis and Smith (2008).”
Maximum likelihood estimation of skew-t copulas with its applications to stock returns (2018, background): “For more information on this type, see also Aas and Haff (2006).”
Conditional Euro Area Sovereign Default Risk (2014, neutral): “Earlier applications of the GHST distribution to financial and economic data include, for example, Mencı́a and Sentana (2005), Hu (2005), and Aas and Haff (2006).”
Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution (2016, methodology): “The GH distribution has a wider class of distribution but the parameters of the GH distribution are difficult to estimate as pointed out by Prause (1999) and Aas and Haff (2006). Nakajima and Omori (2012) also show that a wider class of the GH distribution could lead to either the inefficient MCMC s”
Generalized beta-generated distributions (2012, methodology): “Many subsequent articles apply these techniques to induce a skew into well-known symmetric distributions such as the Student t: see Aas and Haff (2006) for a review.”
Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions (2011, background): “AEPD(0, 1), (1) σ(2) t = b0 + b1σ 2 t−1 + b2σ 2 t−1(zt−1 − c)(2) = b0 + b1σ 2 t−1 + b2(rt−1 −m− cσt−1)(2).”
The Two-sided Weibull Distribution and Forecasting Financial Tail Risk (2013, methodology): “More recently, Zhu and Galbraith (2009) extended the skewed-t idea by using a generalized asymmetric Student-t conditional distribution, with separate parameters in each tail; Griffin and Steel (2006) and Jensen and Maheu (2010) employed Dirichlet process mixtures, while Aas and Haff (2006) used a g”
Multivariate stochastic volatility (2009, methodology): “Aas and Haff (2006) have employed the univariate GH distributions (normal inverse Gaussian distributions and univariate GH skew Student’s t distributions) and estimated in the analysis of the total index of Norwegian stocks (TOTX), the SSBWG hedged bond index for international bonds, the NOK/EUR exc”
Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison (2006, background): “For example, following Aas and Haff (2006), one can assume a mean-variance mixture.”