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Dynamic Factor Volatility Modeling: A Bayesian Latent Threshold Approach

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  • Jouchi Nakajima
  • Mike West

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 several FX, commodities, and stock price index time series. Empirical results show that the latent threshold approach can define interpretable, data-driven, dynamic sparsity, leading to reduced estimation uncertainties, improved predictions, and portfolio performance in increasingly high-dimensional dynamic factor models. Copyright The Author, 2012. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.

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  • Jouchi Nakajima & Mike West, 2012. "Dynamic Factor Volatility Modeling: A Bayesian Latent Threshold Approach," Journal of Financial Econometrics, Oxford University Press, vol. 11(1), pages 116-153, December.
    • Handle: RePEc:oup:jfinec:v:11:y:2012:i:1:p:116-153
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbs013
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    Cited by:

    1. Jouchi Nakajima & Mike West, 2013. "Bayesian Analysis of Latent Threshold Dynamic Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 151-164, April.
    2. Víctor Peña & Kaoru Irie, 2022. "On the Relationship between Uhlig Extended and beta‐Bartlett Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 147-153, January.

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