Economics at your fingertips  

Modeling volatility using state space models with heavy tailed distributions

Frank M. de Pinho, Glaura C. Franco and Ralph S. Silva

Mathematics and Computers in Simulation (MATCOM), 2016, vol. 119, issue C, 108-127

Abstract: This article deals with a non-Gaussian state space model (NGSSM) which is attractive because the likelihood can be analytically computed. The paper focuses on stochastic volatility models in the NGSSM, where the observation equation is modeled with heavy tailed distributions such as Log-gamma, Log-normal and Weibull. Parameter point estimation can be accomplished either using Bayesian or classical procedures and a simulation study shows that both methods lead to satisfactory results. In a real data application, the proposed stochastic volatility models in the NGSSM are compared with the traditional autoregressive conditionally heteroscedastic, its exponential version, and stochastic volatility models using South and North American stock price indexes.

Keywords: Bayesian inference; Classical inference; Non-Gaussian state space model; Stochastic volatility; Stock price index (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
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:

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Series data maintained by Dana Niculescu ().

Page updated 2017-09-29
Handle: RePEc:eee:matcom:v:119:y:2016:i:c:p:108-127