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Generalized dynamic factor models and volatilities: recovering the market volatility shocks

Matteo Barigozzi and Marc Hallin ()

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Decomposing volatilities into a common market-driven component and an idiosyncratic itemspecific one is an important issue in financial econometrics. This, however, requires the statistical analysis of large panels of time series, hence faces the usual challenges associated with highdimensional data. Factor model methods in such a context are an ideal tool, but they do not readily apply to the analysis of volatilities. Focusing on the reconstruction of the unobserved market shocks and the way they are loaded by the various items (stocks) in the panel, we propose an entirely non-parametric and model-free two-step general dynamic factor approach to the problem, which avoids the usual curse of dimensionality. Applied to the S&P100 asset return dataset, the method provides evidence that a non-negligible proportion of the market-driven volatility of returns originates in the volatilities of the idiosyncratic components of returns.

Keywords: Volatility; Dynamic Factor Models; Block Structure (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
Date: 2015
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Published in Econometrics Journal, 2015, 19(1), pp. C33-C60. ISSN: 1368-4221

Downloads: (external link)
http://eprints.lse.ac.uk/60980/ Open access version. (application/pdf)

Related works:
Journal Article: Generalized dynamic factor models and volatilities: recovering the market volatility shocks (2016) Downloads
Working Paper: Generalized Dynamic Factor Models and Volatilities. Recovering the Market Volatility Shocks (2014) Downloads
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