Flexible weighted dirichlet process mixture modelling and evaluation to address the problem of forecasting return distribution

DOI10.1080/10485252.2020.1836560zbMath1466.62358OpenAlexW3094595693MaRDI QIDQ4988819

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Publication date: 19 May 2021

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

Full work available at URL: https://doi.org/10.1080/10485252.2020.1836560

zbMATH Keywords

stochastic volatilityGARCHmarginal likelihoodsemiparametric Bayesian modelweighted Dirichlet process mixture

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Cites Work

• Unnamed Item
• Additive cubic spline regression with Dirichlet process mixture errors
• Bayesian analysis of stochastic volatility models with fat-tails and correlated errors
• Ferguson distributions via Polya urn schemes
• A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection
• Dual-semiparametric regression using weighted Dirichlet process mixture
• Generalized autoregressive conditional heteroscedasticity
• Bayesian semiparametric multivariate GARCH modeling
• Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
• Marginal Likelihood From the Metropolis–Hastings Output
• Bayesian Density Regression
• Order-Based Dependent Dirichlet Processes

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