Long memory stochastic volatility : A bayesian approach

DOI10.1080/03610920008832549zbMath1026.62118OpenAlexW2081414860MaRDI QIDQ4550616

Giovanni Petris, Ngai Hang Chan

Publication date: 10 December 2003

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920008832549

zbMATH Keywords

Markov chain Monte Carlofinancial data

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Bayesian inference (62F15)

Related Items (2)

LONG-RANGE DEPENDENT COMMON FACTOR MODELS: A BAYESIAN APPROACH ⋮ Estimation and forecasting of long memory stochastic volatility models

Cites Work

• Testing for strong serial correlation and dynamic conditional heteroskedasticity in multiple regression
• Fractionally integrated generalized autoregressive conditional heteroskedasticity
• The detection and estimation of long memory in stochastic volatility
• State space modeling of long-memory processes
• Generalized autoregressive conditional heteroscedasticity
• Markov chains for exploring posterior distributions. (With discussion)
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
• DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
• Partial non-Gaussian state space
• Multivariate Stochastic Variance Models
• Adaptive Rejection Sampling for Gibbs Sampling

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