Dynamic Factor Volatility Modeling: A Bayesian Latent Threshold Approach

Abstract

We discuss dynamic factor modeling of financial time series using a latent threshold approach to factor volatility. This approach models time-varying patterns of occurrence of zero elements in factor loadings matrices, providing adaptation to changing relationships over time and dynamic model selection. We summarize Bayesian methods for model fitting and discuss analyses of FX and commodities time series. Empirical results show that interpretable, datadriven dynamic sparsity can reduce estimation uncertainties, improve predictions and portfolio performance.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 40 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.