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

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.

Keywords:

• GARCH
• marginal likelihood
• semiparametric Bayesian model
• stochastic volatility
• weighted dirichlet process mixture

2010 Mathematics Subject Classification:

• 62G07

Acknowledgements

The research of Ki-Ahm Lee is supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) : NRF-2020R1A2C1A01006256. The authors are also grateful to the associate editor and reviewers for their valuable suggestion and constructive input.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The research of Ki-Ahm Lee is supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) : NRF-2020R1A2C1A01006256.