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

Peng Sun, Inyoung Kim and Kiahm Lee

Journal of Nonparametric Statistics, 2020, vol. 32, issue 4, 989-1014

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.

Date: 2020
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DOI: 10.1080/10485252.2020.1836560

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