Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Siem Jan Koopman"'
Publikováno v:
Journal of Econometrics, 221(2), 542-568. Elsevier BV
Blasques, F, Gorgi, P & Koopman, S J 2021, ' Missing observations in observation-driven time series models ', Journal of Econometrics, vol. 221, no. 2, pp. 542-568 . https://doi.org/10.1016/j.jeconom.2020.07.043
Blasques, F, Gorgi, P & Koopman, S J 2021, ' Missing observations in observation-driven time series models ', Journal of Econometrics, vol. 221, no. 2, pp. 542-568 . https://doi.org/10.1016/j.jeconom.2020.07.043
We argue that existing methods for the treatment of missing observations in time-varying parameter observation-driven models lead to inconsistent inference. We provide a formal proof of this inconsistency for a Gaussian model with time-varying mean.
Publikováno v:
Journal of Econometrics, 227(2), 325-346. Elsevier BV
Blasques, F, van Brummelen, J, Koopman, S J & Lucas, A 2022, ' Maximum likelihood estimation for score-driven models ', Journal of Econometrics, vol. 227, no. 2, pp. 325-346 . https://doi.org/10.1016/j.jeconom.2021.06.003
Blasques, F, van Brummelen, J, Koopman, S J & Lucas, A 2022, ' Maximum likelihood estimation for score-driven models ', Journal of Econometrics, vol. 227, no. 2, pp. 325-346 . https://doi.org/10.1016/j.jeconom.2021.06.003
We establish strong consistency and asymptotic normality of the maximum likelihood estimator for stochastic time-varying parameter models driven by the score of the predictive conditional likelihood function. For this purpose, we formulate primitive
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9802dc3e6b015fc9e1ff2d9678abe28f
https://research.vu.nl/en/publications/f002f9e8-1670-4a24-b059-44dda23f9aff
https://research.vu.nl/en/publications/f002f9e8-1670-4a24-b059-44dda23f9aff
Publikováno v:
Borowska, A, Hoogerheide, L, Koopman, S J & van Dijk, H K 2020, ' Partially censored posterior for robust and efficient risk evaluation ', Journal of Econometrics, vol. 217, no. 2, pp. 335-355 . https://doi.org/10.1016/j.jeconom.2019.12.007
Journal of Econometrics, 217(2), 335-355. Elsevier BV
Journal of Econometrics, 217(2), 335-355. Elsevier BV
A novel approach to inference for a specific region of the predictive distribution is introduced. An important domain of application is accurate prediction of financial risk measures, where the area of interest is the left tail of the predictive dens
Publikováno v:
Journal of Financial Econometrics, 17(1), 1-32. Oxford University Press
Gorgi, P, Hansen, P R, Janus, P & Koopman, S J 2019, ' Realized wishart-garch : A score-driven multi-Asset volatility model ', Journal of Financial Econometrics, vol. 17, no. 1, pp. 1-32 . https://doi.org/10.1093/jjfinec/nby007
Gorgi, P, Hansen, P R, Janus, P & Koopman, S J 2019, ' Realized Wishart-GARCH : A Score-driven Multi-Asset Volatility Model ', Journal of Financial Econometrics, vol. 17, no. 1, pp. 1-32 . https://doi.org/10.1093/jjfinec/nby007
Gorgi, P, Hansen, P R, Janus, P & Koopman, S J 2019, ' Realized wishart-garch : A score-driven multi-Asset volatility model ', Journal of Financial Econometrics, vol. 17, no. 1, pp. 1-32 . https://doi.org/10.1093/jjfinec/nby007
Gorgi, P, Hansen, P R, Janus, P & Koopman, S J 2019, ' Realized Wishart-GARCH : A Score-driven Multi-Asset Volatility Model ', Journal of Financial Econometrics, vol. 17, no. 1, pp. 1-32 . https://doi.org/10.1093/jjfinec/nby007
We propose a novel multivariate GARCH model that incorporates realized measures for the covariance matrix of returns. The joint formulation of a multivariate dynamic model for outer-products of returns, realized variances, and realized covariances le
Publikováno v:
SSRN Electronic Journal.
A novel approach to inference for a specific region of the predictive distribution is introduced. An important domain of application is accurate prediction of financial risk measures, where the area of interest is the left tail of the predictive dens
Publikováno v:
Journal of Applied Econometrics. 32:1003-1026
Summary We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear, non-Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when th
Publikováno v:
Journal of Financial Econometrics, 16(3), 384-424. Oxford University Press
Barra, I, Borowska, A & Koopman, S J 2018, ' Bayesian Dynamic Modeling of High-Frequency Integer Price Changes ', Journal of Financial Econometrics, vol. 16, no. 3, pp. 384-424 . https://doi.org/10.1093/jjfinec/nby010
Barra, I, Borowska, A & Koopman, S J 2018, ' Bayesian dynamic modeling of high-frequency integer price changes ', Journal of Financial Econometrics, vol. 16, no. 3, pp. 384-424 . https://doi.org/10.1093/jjfinec/nby010
Barra, I, Borowska, A & Koopman, S J 2018, ' Bayesian Dynamic Modeling of High-Frequency Integer Price Changes ', Journal of Financial Econometrics, vol. 16, no. 3, pp. 384-424 . https://doi.org/10.1093/jjfinec/nby010
Barra, I, Borowska, A & Koopman, S J 2018, ' Bayesian dynamic modeling of high-frequency integer price changes ', Journal of Financial Econometrics, vol. 16, no. 3, pp. 384-424 . https://doi.org/10.1093/jjfinec/nby010
We investigate high-frequency volatility models for analyzing intradaily tick by tick stock price changes using Bayesian estimation procedures. Our key interest is the extraction of intradaily volatility patterns from high-frequency integer price cha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8be69eb131f6fff94a7a07f450d1fbb2
https://research.vu.nl/en/publications/12a2fa94-98e3-4fea-8d98-282abdf580f5
https://research.vu.nl/en/publications/12a2fa94-98e3-4fea-8d98-282abdf580f5
Publikováno v:
Journal of Applied Econometrics. 29:65-90
SUMMARY We consider the dynamic factor model and show how smoothness restrictions can be imposed on factor loadings by using cubic spline functions. We develop statistical procedures based on Wald, Lagrange multiplier and likelihood ratio tests for t
Autor:
Siem Jan Koopman, Istvan Barra
Publikováno v:
SSRN Electronic Journal.
We investigate high-frequency volatility models for analyzing intra-day tick by tick stock price changes using Bayesian estimation procedures. Our key interest is the extraction of intra-day volatility patterns from high-frequency integer price chang
Publikováno v:
Econometric Reviews, 35(4), 659-687. Taylor and Francis Ltd.
Mesters, G, Koopman, S J & Ooms, M 2016, ' Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models ', Econometric Reviews, vol. 35, no. 4, pp. 659-687 . https://doi.org/10.1080/07474938.2015.1031014
Mesters, G, Koopman, S J & Ooms, M 2016, ' Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models ', Econometric Reviews, vol. 35, no. 4, pp. 659-687 . https://doi.org/10.1080/07474938.2015.1031014
An exact maximum likelihood method is developed for the estimation of parameters in a non-Gaussian nonlinear density function that depends on a latent Gaussian dynamic process with long-memory properties. Our method relies on the method of importance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f55f252142c81ad70250e65b139d8566
https://research.vu.nl/en/publications/cd05c4b6-08bb-477b-95b8-2137d816a135
https://research.vu.nl/en/publications/cd05c4b6-08bb-477b-95b8-2137d816a135