Sparsity-Promoting Bayesian Dynamic Linear Models
This work addresses the need for flexible sparsity modeling in time series applications like finance, though it appears incremental as it builds on existing Bayesian sparse regression methods.
The authors tackled the problem of modeling dynamic sparsity patterns in time series data, where sparsity is not fixed over time, by proposing a new class of Bayesian dynamic linear models. They demonstrated the model's effectiveness in a financial application, accurately representing patterns in stock and derivative data and detecting major events.
Sparsity-promoting priors have become increasingly popular over recent years due to an increased number of regression and classification applications involving a large number of predictors. In time series applications where observations are collected over time, it is often unrealistic to assume that the underlying sparsity pattern is fixed. We propose here an original class of flexible Bayesian linear models for dynamic sparsity modelling. The proposed class of models expands upon the existing Bayesian literature on sparse regression using generalized multivariate hyperbolic distributions. The properties of the models are explored through both analytic results and simulation studies. We demonstrate the model on a financial application where it is shown that it accurately represents the patterns seen in the analysis of stock and derivative data, and is able to detect major events by filtering an artificial portfolio of assets.