@techreport{oai:grips.repo.nii.ac.jp:00001148,
 author = {LEON-GONZALEZ, Roberto},
 note = {https://www.grips.ac.jp/list/jp/facultyinfo/leon_gonzalez_roberto/, Second version: October, 2015 [15-17] http://doi.org/10.24545/00001286, Third version: March, 2018 [17-16] http://doi.org/10.24545/00001609, This paper develops a novel and efficient algorithm for Bayesian inference in inverse Gamma Stochastic Volatility models. It is shown that by conditioning on auxiliary variables, it is possible to sample all the volatilities jointly directly from their posterior conditional density, using simple and easy to draw from distributions. Furthermore, this paper develops a generalized inverse Gamma process with more fl‡exible tails in the distribution of volatilities, which still allows for simple and efficient calculations. Using several macroeconomic and fi<br />nancial datasets, it is shown that the inverse Gamma and Generalized inverse Gamma processes can greatly outperform the commonly used log normal volatility processes with student-t errors., JEL Classification Codes: C11, C15, 経済学 / Economics},
 title = {Efficient Bayesian Inference in Generalized Inverse Gamma Processes for Stochastic Volatility}
}