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Long memory stochastic volatility : A bayesian approach
Authors:Ngai Hang Chan  Giovanni Petris
Affiliation:Department of Statistics , Carnegie Mellon University , Pittsburgh, PA, 15213-3890, USA
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:Markov chain Monte Carlo: state-space models
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