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Observation-driven filters for timeseries with stochastic trends and mixed causal non-causal dynamics

Blasques, Francisco; Koopman, Siem Jan; Mingoli, Gabriele - 2023

consistent with financial theory, for a decomposition of the time-series in trend and bubble components, and for meaningful real …

Persistent link: https://www.econbiz.de/10014380706

Models with time-varying mean and variance : a robust analysis of US industrial production

Bos, Charles S.; Koopman, Siem Jan - 2010

Persistent link: https://www.econbiz.de/10003973299

A dynamic multivariate heavy-tailed model for time-varying volatilities and correlations

Creal, Drew; Koopman, Siem Jan; Lucas, André - 2010

Persistent link: https://www.econbiz.de/10003973316

Long memory dynamics for multivariate dependence under heavy tails

Janus, Paweł; Koopman, Siem Jan; Lucas, André - 2011

Persistent link: https://www.econbiz.de/10009720703

Forecasting volatility with Copula-based time series models

Sokolinskiy, Oleg; Dijk, Dick van - 2011

Persistent link: https://www.econbiz.de/10009720755

Monte Carlo maximum likelihood estimation for generalized long-memory time series models

Mesters, G.; Koopman, Siem Jan; Ooms, Marius - 2011

Persistent link: https://www.econbiz.de/10009720782

Stationarity and ergodicity of univariate generalized autoregressive score processes

Blasques, Francisco; Koopman, Siem Jan; Lucas, André - 2012

Persistent link: https://www.econbiz.de/10009722625

Predicting time-varying parameters with parameter-driven and observation-driven models

Koopman, Siem Jan; Lucas, André; Scharth, Marcel - 2012

Persistent link: https://www.econbiz.de/10009722696

Leverage and feedback effects on multifactor wishart stochastic volatility for option pricing

Asai, Manabu; McAleer, Michael - 2013

Persistent link: https://www.econbiz.de/10009724148

Joint Bayesian analysis of parameters and states in nonlinear, non-Gaussian state space models

Barra, István; Hoogerheide, Lennart; Koopman, Siem Jan; … - 2014

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 these proposal densities are used in an independent...

Persistent link: https://www.econbiz.de/10010399681