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

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  • Peng Sun
  • Inyoung Kim
  • Kiahm Lee

Abstract

Forecasting volatility has been widely addressed in the fields of finance, environmetrics, and other areas involving massive time series. The important part of addressing this problem is how to specify the error term's distribution. With a weaker distribution assumption, we achieve greater model flexibility. In this paper, we present a flexible semiparametric Bayesian framework to address the problem of forecasting volatility in time series data by introducing the weighted Dirichlet process mixture (WDPM). We illustrate the advantages of WDPM using simulation data and stock return data.

Suggested Citation

  • Peng Sun & Inyoung Kim & Kiahm Lee, 2020. "Flexible weighted dirichlet process mixture modelling and evaluation to address the problem of forecasting return distribution," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 32(4), pages 989-1014, October.
    • Handle: RePEc:taf:gnstxx:v:32:y:2020:i:4:p:989-1014
    DOI: 10.1080/10485252.2020.1836560
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    Cited by:

    1. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    2. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.

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