Time Series Mean Level and Stochastic Volatility Modeling by Smooth Transition Autoregressions: A BAYESIAN Approach

Hedibert Freitas Lopes, Esther Salazar

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

In this paper, we propose a Bayesian approach to model the level and the variance of (financial) time series by the special class of nonlinear time series models known as the logistic smooth transition autoregressive models, or simply the LSTAR models. We first propose a Markov Chain Monte Carlo (MCMC) algorithm for the levels of the time series and then adapt it to model the stochastic volatilities. The LSTAR order is selected by three information criteria: the well-known AIC and BIC, and by the deviance information criteria, or DIC. We apply our algorithm to a synthetic data and two real time series, namely the canadian lynx data and the SP500 returns.

Original languageEnglish (US)
Title of host publicationEconometric Analysis of Financial and Economic Time Series
EditorsThomas Fomby, Dek Terrell
Pages225-238
Number of pages14
DOIs
StatePublished - 2006
Externally publishedYes

Publication series

NameAdvances in Econometrics
Volume20 PART 2
ISSN (Print)0731-9053

ASJC Scopus subject areas

  • Economics and Econometrics

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