Efficient Bayesian Inference in Generalized Inverse Gamma Processes for Stochastic Volatility

Roberto Leon-Gonzalez

No 14-12, GRIPS Discussion Papers from National Graduate Institute for Policy Studies

Abstract: 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 flexible tails in the distribution of volatilities, which still allows for simple and efficient calculations. Using several macroeconomic and financial 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.

Pages: 29 pages
Date: 2014-09
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ger
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Related works:
Journal Article: Efficient Bayesian inference in generalized inverse gamma processes for stochastic volatility (2019)
Working Paper: Efficient Bayesian Inference in Generalized Inverse Gamma Processes for Stochastic Volatility (2018)
Working Paper: Efficient Bayesian Inference in Generalized Inverse Gamma Processes for Stochastic Volatility (2015)
Working Paper: Efficient Bayesian Inference in Generalized Inverse Gamma Processes for Stochastic Volatility (2014)
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