A Bayesian Learning Method for Financial Time-Series Analysis

This article develops a sequential Bayesian learning method to estimate the parameters and recover the state variables for generalized autoregressive conditional heteroscedasticity (GARCH) models, which are commonly used in the financial time-series analysis. This simulation-based method combines pa...

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Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.38959-38966
Main Authors: Zhu, Fumin, Quan, Wei, Zheng, Zunxin, Wan, Shaohua
Format: Article
Language:English
Subjects:
Algorithms
Analytical models
Autoregressive models
Autoregressive processes
Bayes methods
Bayesian analysis
Biological system modeling
Computational modeling
Computer simulation
GARCH models
Machine learning
Markov chain Monte Carlo
Markov chains
Maximum likelihood estimation
Numerical models
Parameter estimation
particle filtering
Sequential Bayesian learning
sparse recovery
State variable
Stochastic models
Time series
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Summary:This article develops a sequential Bayesian learning method to estimate the parameters and recover the state variables for generalized autoregressive conditional heteroscedasticity (GARCH) models, which are commonly used in the financial time-series analysis. This simulation-based method combines particle-filtering technology with a Markov chain Monte Carlo algorithm when the model is non-linear and the number of observed variables is relatively sparse. We compare the performance of the sequential Bayesian learning approach with the numerical maximum likelihood estimation (NMLE) in estimating models based on S&P 500 return rates. Our research concludes that the sequential parameter learning approach performs more robustly and accurately than the NMLE, by taking into account the uncertainty of the model. We also carry out simulation studies to confirm that the sequential Bayesian learning method is extremely reliable for GARCH models.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2853998