Conditional Likelihood Estimators for Hidden Markov Models and Stochastic Volatility Models

DOI10.1111/1467-9469.00332zbMath1053.62088OpenAlexW2138167381MaRDI QIDQ4828200

Catherine Larédo, Valentine Genon-Catalot, Thierry Jeantheau

Publication date: 24 November 2004

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/1467-9469.00332

zbMATH Keywords

stochastic volatilityhidden Markov modeldiffusion processdiscrete time observationsconditional likelihoodparametric inference

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Applications of statistics to actuarial sciences and financial mathematics (62P05) Markov processes: estimation; hidden Markov models (62M05)

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Cites Work

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• Volume, volatility, and leverage: A dynamic analysis
• Maximum-likelihood estimation for hidden Markov models
• Limit theorems for discretely observed stochastic volatility models
• Estimation of stochastic volatility models with diagnostics
• Asymptotic normality of the maximum-likelihood estimator for general hidden Markov models
• Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics
• Complete Models with Stochastic Volatility
• Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
• On White Noises Driven by Hidden Markov Chains

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