Dirichlet Mixed Process Integrated Bayesian Estimation for Individual Securities

Phan Dinh Khoi, Thai Minh Trong and Christopher Gan ()
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Phan Dinh Khoi: Faculty of Finance and Banking, College of Economics, Can Tho University, Can Tho City 90000, Vietnam
Thai Minh Trong: College of Natural Sciences, Can Tho University, Can Tho City 90000, Vietnam
Christopher Gan: Faculty of Agribusiness and Commerce, Lincoln University, Christchurch 7647, New Zealand

JRFM, 2025, vol. 18, issue 6, 1-16

Abstract: Bayesian nonparametric methods, particularly the Dirichlet process (DP), have gained increasing popularity in both theoretical and applied research, driven by advances in computing power. Traditional Bayesian estimation, which often relies on Gaussian priors, struggles to dynamically integrate evolving prior beliefs into the posterior distribution for decision-making in finance. This study addresses that limitation by modeling daily security price fluctuations using a Dirichlet process mixture (DPM) model. Our results demonstrate the DPM’s effectiveness in identifying the optimal number of clusters within time series data, leading to more accurate density estimation. Unlike kernel methods, the DPM continuously updates the prior density based on observed data, enabling it to better capture the dynamic nature of security prices. This adaptive feature positions the DPM as a superior estimation technique for time series data with complex, multimodal distributions.

Keywords: Dirichlet process; Bayesian estimation; Gaussian priors; posterior distribution; Dirichlet process mixture model; securities (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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