Long memory stochastic volatility : A bayesian approach
Authors:
- 1 January 2000
- journal article
- research article
- Published by Taylor & Francis in Communications in Statistics - Theory and Methods
- Vol. 29 (5-6) , 1367-1378
Abstract
We propose a simulation-based Bayesian approach to the analysis of long memory stochastic volatility models, stationary and nonstationary. The main tool used to reduce the likelihood function to a tractable form is an approximate state-space representation of the model, A data set of stock market returns is analyzed with the proposed method. The approach taken here allows a quantitative assessment of the empirical evidence in favor of the stationarity, or nonstationarity, of the instantaneous volatility of the data.Keywords
This publication has 15 references indexed in Scilit:
- Stochastic Volatility: Likelihood Inference and Comparison with ARCH ModelsReview of Economic Studies, 1998
- State space modeling of long-memory processesThe Annals of Statistics, 1998
- The detection and estimation of long memory in stochastic volatilityJournal of Econometrics, 1998
- Fractionally integrated generalized autoregressive conditional heteroskedasticityJournal of Econometrics, 1996
- Multivariate Stochastic Variance ModelsReview of Economic Studies, 1994
- DATA AUGMENTATION AND DYNAMIC LINEAR MODELSJournal of Time Series Analysis, 1994
- A long memory property of stock market returns and a new modelJournal of Empirical Finance, 1993
- Adaptive Rejection Sampling for Gibbs SamplingJournal of the Royal Statistical Society Series C: Applied Statistics, 1992
- Generalized autoregressive conditional heteroskedasticityJournal of Econometrics, 1986
- Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom InflationEconometrica, 1982