Maximizing equity market sector predictability in a Bayesian time-varying parameter model

DOI10.1016/j.csda.2007.09.030zbMath1452.62772OpenAlexW2042286782MaRDI QIDQ1023643

Georgios Sakoulis, Lorne D. Johnson

Publication date: 12 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2007.09.030

zbMATH Keywords

asset pricingKalman filterMarkov switchingGibbs samplingbehavioral finance

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Related Items (2)

On marginal likelihood computation in change-point models ⋮ Editorial: Special issue on statistical and computational methods in finance

Cites Work

• Robust fitting of mixtures using the trimmed likelihood estimator
• A unified approach to nonlinearity, structural change, and outliers
• Variable selection in regression models using nonstandard optimisation of information criteria
• A simple and efficient method for variable ranking according to their usefulness for learning
• What is an oil shock?
• Marginal Likelihood from the Gibbs Output
• Two Methods for Examining the Stability of Regression Coefficients
• MAXIMIZING PREDICTABILITY IN THE STOCK AND BOND MARKETS
• On Gibbs sampling for state space models
• Estimating and Testing Linear Models with Multiple Structural Changes
• An Intertemporal Capital Asset Pricing Model
• Bayes Factors
• Common risk factors in the returns on stocks and bonds

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