Similar Search Results

Bayesian risk forecasting for long horizons

Borowska, Agnieszka; Hoogerheide, Lennart; Koopman, Siem Jan - 2019

We present an accurate and efficient method for Bayesian forecasting of two financial risk measures, Value-at-Risk and Expected Shortfall, for a given volatility model. We obtain precise forecasts of the tail of the distribution of returns not only for the 10-days-ahead horizon required by the...

Persistent link: https://www.econbiz.de/10011979983

Statistical algorithms for models in state space using SsfPack 2.2

Koopman, Siem Jan; Shephard, Neil G.; Doornik, Jurgen A. - 1998

Persistent link: https://www.econbiz.de/10000167948

State space and unobserved component models : theory and applications

Harvey, Andrew C. (ed.); Koopman, Siem Jan (contributor); … - 2004 - 1. publ.

Persistent link: https://www.econbiz.de/10009681752

Unobserved components and time series econometrics

Koopman, Siem Jan (ed.); Shephard, Neil G. (ed.) - 2015 - First published

unobserved components time series models; applications of unobserved components time series models; and time series econometrics … research directions for UC time series econometrics and will be of interest to econometricians, time series statisticians, and … students in statistics, econometrics, and engineering …

Persistent link: https://www.econbiz.de/10013537815

Spot Variance Path Estimation and its Application to High Frequency Jump Testing

Bos, Charles S.; Janus, Pawel; Koopman, Siem Jan - Tinbergen Instituut - 2009

This discussion paper resulted in an article in the <I>Journal of Financial Econometrics</I> (2012). Vol. 10, pages 354 …

Persistent link: https://www.econbiz.de/10011255584

Testing for Parameter Instability in Competing Modeling Frameworks

Calvori, Francesco; Creal, Drew; Koopman, Siem Jan; … - Tinbergen Instituut - 2014

We develop a new parameter stability test against the alternative of observation driven generalized autoregressive score dynamics. The new test generalizes the ARCH-LM test of Engle (1982) to settings beyond time-varying volatility and exploits any autocorrelation in the likelihood scores under...

Persistent link: https://www.econbiz.de/10011255854

Maximum Likelihood Estimation for Generalized Autoregressive Score Models

Blasques, Francisco; Koopman, Siem Jan; Lucas, Andre - Tinbergen Instituut - 2014

We study the strong consistency and asymptotic normality of the maximum likelihood estimator for a class of time series models driven by the score function of the predictive likelihood. This class of nonlinear dynamic models includes both new and existing observation driven time series models....

Persistent link: https://www.econbiz.de/10011256845

Maximum Likelihood Estimation for correctly Specified Generalized Autoregressive Score Models: Feedback Effects, Contraction Conditions and Asymptotic Properties

Blasques, Francisco; Koopman, Siem Jan; Lucas, André - Tinbergen Instituut - 2014

The strong consistency and asymptotic normality of the maximum likelihood estimator in observation-driven models usually requires the study of the model both as a filter for the time-varying parameter and as a data generating process (DGP) for observed data. The probabilistic properties of the...

Persistent link: https://www.econbiz.de/10011272581

Information Theoretic Optimality of Observation driven Time Series Models

Blasques, Francisco; Koopman, Siem Jan; Lucas, André - Tinbergen Instituut - 2014

We investigate the information theoretic optimality properties of the score function of the predictive likelihood as a device to update parameters in observation driven time-varying parameter models. The results provide a new theoretical justification for the class of generalized autoregressive...

Persistent link: https://www.econbiz.de/10011272597

Spot Variance Path Estimation and its Application to High Frequency Jump Testing

Bos, Charles S.; Janus, Pawel; Koopman, Siem Jan - 2009

This paper considers spot variance path estimation from datasets of intraday high frequency asset prices in the presence of diurnal variance patterns, jumps, leverage effects and microstructure noise. We rely on parametric and nonparametric methods. The estimated spot variance path can be used...

Persistent link: https://www.econbiz.de/10010325674

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