Accelerating score-driven time series models

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Standard

Accelerating score-driven time series models. / Blasques, F.; Gorgi, P.; Koopman, S. J.

In: Journal of Econometrics, Vol. 212, No. 2, 2019, p. 359-376.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Blasques, F, Gorgi, P & Koopman, SJ 2019, 'Accelerating score-driven time series models', Journal of Econometrics, vol. 212, no. 2, pp. 359-376. https://doi.org/10.1016/j.jeconom.2019.03.005

APA

Blasques, F., Gorgi, P., & Koopman, S. J. (2019). Accelerating score-driven time series models. Journal of Econometrics, 212(2), 359-376. https://doi.org/10.1016/j.jeconom.2019.03.005

CBE

Blasques F, Gorgi P, Koopman SJ. 2019. Accelerating score-driven time series models. Journal of Econometrics. 212(2):359-376. https://doi.org/10.1016/j.jeconom.2019.03.005

MLA

Blasques, F., P. Gorgi, and S. J. Koopman. "Accelerating score-driven time series models". Journal of Econometrics. 2019, 212(2). 359-376. https://doi.org/10.1016/j.jeconom.2019.03.005

Vancouver

Blasques F, Gorgi P, Koopman SJ. Accelerating score-driven time series models. Journal of Econometrics. 2019;212(2):359-376. https://doi.org/10.1016/j.jeconom.2019.03.005

Author

Blasques, F. ; Gorgi, P. ; Koopman, S. J. / Accelerating score-driven time series models. In: Journal of Econometrics. 2019 ; Vol. 212, No. 2. pp. 359-376.

Bibtex

@article{9a80f4420b70428dac272c2784c078a3,
title = "Accelerating score-driven time series models",
abstract = "We propose a new class of score-driven time series models that allows for a more flexible weighting of score innovations for the filtering of time varying parameters. The parameter for the score innovation is made time-varying by means of an updating equation that accounts for the autocorrelations of past innovations. We provide the theoretical foundations for this acceleration method by showing optimality in terms of reducing Kullback–Leibler divergence. The empirical relevance of this accelerated score-driven updating method is illustrated in two empirical studies. First, we include acceleration in the generalized autoregressive conditional heteroskedasticity model. We adopt the new model to extract volatility from exchange rates and to analyze daily density forecasts of volatilities from all individual stock return series in the Standard & Poor's 500 index. Second, we consider a score-driven acceleration for the time-varying mean and use this new model in a forecasting study for US inflation.",
keywords = "GARCH models, Kullback–Leibler divergence, S&P 500 stocks, Score-driven models, Time-varying parameters, US inflation",
author = "F. Blasques and P. Gorgi and Koopman, {S. J.}",
year = "2019",
doi = "10.1016/j.jeconom.2019.03.005",
language = "English",
volume = "212",
pages = "359--376",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",
number = "2",

}

RIS

TY - JOUR

T1 - Accelerating score-driven time series models

AU - Blasques, F.

AU - Gorgi, P.

AU - Koopman, S. J.

PY - 2019

Y1 - 2019

N2 - We propose a new class of score-driven time series models that allows for a more flexible weighting of score innovations for the filtering of time varying parameters. The parameter for the score innovation is made time-varying by means of an updating equation that accounts for the autocorrelations of past innovations. We provide the theoretical foundations for this acceleration method by showing optimality in terms of reducing Kullback–Leibler divergence. The empirical relevance of this accelerated score-driven updating method is illustrated in two empirical studies. First, we include acceleration in the generalized autoregressive conditional heteroskedasticity model. We adopt the new model to extract volatility from exchange rates and to analyze daily density forecasts of volatilities from all individual stock return series in the Standard & Poor's 500 index. Second, we consider a score-driven acceleration for the time-varying mean and use this new model in a forecasting study for US inflation.

AB - We propose a new class of score-driven time series models that allows for a more flexible weighting of score innovations for the filtering of time varying parameters. The parameter for the score innovation is made time-varying by means of an updating equation that accounts for the autocorrelations of past innovations. We provide the theoretical foundations for this acceleration method by showing optimality in terms of reducing Kullback–Leibler divergence. The empirical relevance of this accelerated score-driven updating method is illustrated in two empirical studies. First, we include acceleration in the generalized autoregressive conditional heteroskedasticity model. We adopt the new model to extract volatility from exchange rates and to analyze daily density forecasts of volatilities from all individual stock return series in the Standard & Poor's 500 index. Second, we consider a score-driven acceleration for the time-varying mean and use this new model in a forecasting study for US inflation.

KW - GARCH models

KW - Kullback–Leibler divergence

KW - S&P 500 stocks

KW - Score-driven models

KW - Time-varying parameters

KW - US inflation

UR - http://www.scopus.com/inward/record.url?scp=85064814141&partnerID=8YFLogxK

U2 - 10.1016/j.jeconom.2019.03.005

DO - 10.1016/j.jeconom.2019.03.005

M3 - Journal article

VL - 212

SP - 359

EP - 376

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 2

ER -