Exact Likelihood for Inverse Gamma Stochastic Volatility Models

Roberto Leon-Gonzalez and Blessings Majoni

Working Paper series from Rimini Centre for Economic Analysis

Abstract: We obtain a novel analytic expression of the likelihood for a stationary inverse gamma Stochastic Volatility (SV) model. This allows us to obtain the Maximum Likelihood Estimator for this non linear non Gaussian state space model. Further, we obtain both the filtering and smoothing distributions for the inverse volatilities as mixture of gammas and therefore we can provide the smoothed estimates of the volatility. We show that by integrating out the volatilities the model that we obtain has the resemblance of a GARCH in the sense that the formulas are similar, which simplifies computations significantly. The model allows for fat tails in the observed data. We provide empirical applications using exchange rates data for 7 currencies and quarterly inflation data for four countries. We find that the empirical fit of our proposed model is overall better than alternative models for 4 countries currency data and for 2 countries inflation data.

Keywords: Hypergeometric Function; Particle Filter; Parallel Computing; Euler Acceleration (search for similar items in EconPapers)
JEL-codes: C32 C58 (search for similar items in EconPapers)
Date: 2023-07
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://rcea.org/RePEc/pdf/wp23-11.pdf

Related works:
Working Paper: Exact Likelihood for Inverse Gamma Stochastic Volatility Models (2024)
Working Paper: Exact Likelihood for Inverse Gamma Stochastic Volatility Models (2023)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:23-11

Access Statistics for this paper

More papers in Working Paper series from Rimini Centre for Economic Analysis Contact information at EDIRC.
Bibliographic data for series maintained by Marco Savioli ().