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    Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Econometrics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Econometrics, 222, 1, 2021 DOI: 10.1016/j.jeconom.2020.07.004

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Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness

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Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness. / Barigozzi, Matteo; Hallin, Marc; Soccorsi, Stefano; von Sachs, Rainer.

In: Journal of Econometrics, Vol. 222, No. 1, 31.05.2021, p. 324-343.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Barigozzi, M, Hallin, M, Soccorsi, S & von Sachs, R 2021, 'Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness', Journal of Econometrics, vol. 222, no. 1, pp. 324-343. https://doi.org/10.1016/j.jeconom.2020.07.004

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Author

Barigozzi, Matteo ; Hallin, Marc ; Soccorsi, Stefano ; von Sachs, Rainer. / Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness. In: Journal of Econometrics. 2021 ; Vol. 222, No. 1. pp. 324-343.

Bibtex

@article{1057e7fd76aa4d3a8414e8e0800796b7,
title = "Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness",
abstract = "We propose a new time-varying Generalized Dynamic Factor Model for high-dimensional, locally stationary time series. Estimation is based on dynamic principal component analysis jointly with singular VAR estimation, and extends to the locally stationary case the one-sided estimation method proposed by Forni et al. (2017) for stationary data. We prove consistency of our estimators of time-varying impulse response functions as both the sample size and the dimension of the time series grow to infinity. This approach is used in an empirical application in order to construct a time-varying measure of financial connectedness for a large panel of adjusted intra-day log ranges of stocks. We show that large increases in long-run connectedness are associated with the main financial turmoils. Moreover, we provide evidence of a significant heterogeneity in the dynamic responses to common shocks in time and over different scales, as well as across industrial sectors.",
keywords = "Locally stationary dynamic factor models, Volatility, Financial connectedness",
author = "Matteo Barigozzi and Marc Hallin and Stefano Soccorsi and {von Sachs}, Rainer",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Journal of Econometrics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Econometrics, 222, 1, 2021 DOI: 10.1016/j.jeconom.2020.07.004",
year = "2021",
month = may,
day = "31",
doi = "10.1016/j.jeconom.2020.07.004",
language = "English",
volume = "222",
pages = "324--343",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",
number = "1",

}

RIS

TY - JOUR

T1 - Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness

AU - Barigozzi, Matteo

AU - Hallin, Marc

AU - Soccorsi, Stefano

AU - von Sachs, Rainer

N1 - This is the author’s version of a work that was accepted for publication in Journal of Econometrics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Econometrics, 222, 1, 2021 DOI: 10.1016/j.jeconom.2020.07.004

PY - 2021/5/31

Y1 - 2021/5/31

N2 - We propose a new time-varying Generalized Dynamic Factor Model for high-dimensional, locally stationary time series. Estimation is based on dynamic principal component analysis jointly with singular VAR estimation, and extends to the locally stationary case the one-sided estimation method proposed by Forni et al. (2017) for stationary data. We prove consistency of our estimators of time-varying impulse response functions as both the sample size and the dimension of the time series grow to infinity. This approach is used in an empirical application in order to construct a time-varying measure of financial connectedness for a large panel of adjusted intra-day log ranges of stocks. We show that large increases in long-run connectedness are associated with the main financial turmoils. Moreover, we provide evidence of a significant heterogeneity in the dynamic responses to common shocks in time and over different scales, as well as across industrial sectors.

AB - We propose a new time-varying Generalized Dynamic Factor Model for high-dimensional, locally stationary time series. Estimation is based on dynamic principal component analysis jointly with singular VAR estimation, and extends to the locally stationary case the one-sided estimation method proposed by Forni et al. (2017) for stationary data. We prove consistency of our estimators of time-varying impulse response functions as both the sample size and the dimension of the time series grow to infinity. This approach is used in an empirical application in order to construct a time-varying measure of financial connectedness for a large panel of adjusted intra-day log ranges of stocks. We show that large increases in long-run connectedness are associated with the main financial turmoils. Moreover, we provide evidence of a significant heterogeneity in the dynamic responses to common shocks in time and over different scales, as well as across industrial sectors.

KW - Locally stationary dynamic factor models

KW - Volatility

KW - Financial connectedness

U2 - 10.1016/j.jeconom.2020.07.004

DO - 10.1016/j.jeconom.2020.07.004

M3 - Journal article

VL - 222

SP - 324

EP - 343

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 1

ER -