Time-varying forecasts by variational approximation of sequential Bayesian inference

DOI10.1080/14697688.2015.1034759zbMath1469.62342OpenAlexW1721861140MaRDI QIDQ5001109

Hui `Fox' Ling, Douglas B. Stone

Publication date: 16 July 2021

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697688.2015.1034759

zbMATH Keywords

Kalman filtervariational BayesBayesian filteringsequential Bayesian inferencetime-varying variancetime-varying regressionstate space modelling of time-seriestime-varying forecasts

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) Sequential estimation (62L12)

Related Items (1)

Online learning of time-varying stochastic factor structure by variational sequential Bayesian factor analysis

Uses Software

• StFinMetrics
• PRMLT

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters
• Volatility forecast comparison using imperfect volatility proxies
• Particle filters and Bayesian inference in financial econometrics
• Linear and non-linear filtering in mathematical finance: a review
• Modeling Financial Time Series with S-PLUS®
• Handbook of Financial Time Series
• Realized Volatility
• Particle Filtering
• Variational Bayesian Filtering
• Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations
• The marginalized particle filter – analysis, applications and generalizations
• Linear Statistical Inference and its Applications

This page was built for publication: Time-varying forecasts by variational approximation of sequential Bayesian inference