Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models

DOI10.1177/1471082x211034048MaRDI QIDQ6669919

Lennart Oelschläger, Timo Adam

Publication date: 22 January 2025

Published in: Statistical Modelling (Search for Journal in Brave)

zbMATH Keywords

hidden Markov modelsstate-space modelstemporal resolutiontime series modellingdecoding market behaviour

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Stylized facts of financial time series and hidden semi-Markov models
• The hierarchical hidden Markov model: Analysis and applications
• Multi-scale modeling of animal movement and general behavior data using hidden Markov models with hierarchical structures
• Empirical evidence on Student-\(t\) log-returns of diversified world stock indices
• A Markov-switching regression model with non-Gaussian innovations: estimation and testing
• Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters
• Dynamic portfolio optimization across hidden market regimes
• A dynamic analysis of stock markets using a hidden Markov model
• Nonparametric identification and maximum likelihood estimation for hidden Markov models
• Hidden Markov Models for Time Series
• Markov-Switching MIDAS Models

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