S.J. Koopman (Siem Jan)
http://repub.eur.nl/ppl/10553/
List of Publicationsenhttp://repub.eur.nl/eur_signature.png
http://repub.eur.nl/
RePub, Erasmus University RepositoryForecasting interest rates with shifting endpoints
http://repub.eur.nl/pub/55440/
Fri, 01 Aug 2014 00:00:01 GMT<div>D.J.C. van Dijk</div><div>S.J. Koopman</div><div>M. van der Wel</div><div>J. Wright</div>
__Abstract__
We consider forecasting the term structure of interest rates with the assumption that factors driving the yield curve are stationary around a slowly time-varying mean or 'shifting endpoint'. The shifting endpoints are captured using either (i) time series methods (exponential smoothing) or (ii) long-range survey forecasts of either interest rates or inflation and output growth, or (iii) exponentially smoothed realizations of these macro variables. Allowing for shifting endpoints in yield curve factors provides substantial and significant gains in out-of-sample predictive accuracy, relative to stationary and random walk benchmarks. Forecast improvements are largest for long-maturity interest rates and for long-horizon forecasts.CFEnetwork: The Annals of computational and financial econometrics: 2nd issue
http://repub.eur.nl/pub/53379/
Wed, 01 Jan 2014 00:00:01 GMT<div>E.J. Kontoghiorghes</div><div>H.K. van Dijk</div><div>D. Belsley</div><div>T. Bollerslev</div><div>F.X. Diebold</div><div>J.-M. Dufour</div><div>R. Engle</div><div>A.C. Harvey</div><div>S.J. Koopman</div><div>M.H. Pesaran</div><div>P.C.B. Phillips</div><div>R. Smith</div><div>M. West</div><div>Q. Yao</div><div>A. Amendola</div><div>M. Billio</div><div>C.W.S. Chen</div><div>C. Chiarella</div><div>A. Colubi</div><div>M. Deistler</div><div>C. Francq</div><div>M. Hallin</div><div>E. Jacquier</div><div>K. Judd</div><div>G. Koop</div><div>H. Lütkepohl</div><div>J.G. MacKinnon</div><div>S. Mittnik</div><div>Y. Omori</div><div>I. Pollock</div><div>T. Proietti</div><div>J.V.K. Rombouts</div><div>O. Scaillet</div><div>W. Semmler</div><div>M.K.P. So</div><div>J. Steel</div><div>R.N. Taylor</div><div>E. Tzavalis</div><div>J.-M. Zakoian</div><div>H. Peter Boswijk</div><div>A. Luati</div><div>J. Maheu</div>
Forecasting the US term structure of interest rates using a macroeconomic smooth dynamic factor model
http://repub.eur.nl/pub/76414/
Tue, 01 Oct 2013 00:00:01 GMT<div>S.J. Koopman</div><div>M. van der Wel</div>
__Abstract__
We extend the class of dynamic factor yield curve models in order to include macroeconomic factors. Our work benefits from recent developments in the dynamic factor literature related to the extraction of the common factors from a large panel of macroeconomic series and the estimation of the parameters in the model. We include these factors in a dynamic factor model for the yield curve, in which we model the salient structure of the yield curve by imposing smoothness restrictions on the yield factor loadings via cubic spline functions. We carry out a likelihood-based analysis in which we jointly consider a factor model for the yield curve, a factor model for the macroeconomic series, and their dynamic interactions with the latent dynamic factors. We illustrate the methodology by forecasting the U.S. term structure of interest rates. For this empirical study, we use a monthly time series panel of unsmoothed Fama-Bliss zero yields for treasuries of different maturities between 1970 and 2009, which we combine with a macro panel of 110 series over the same sample period. We show that the relationship between the macroeconomic factors and the yield curve data has an intuitive interpretation, and that there is interdependence between the yield and macroeconomic factors. Finally, we perform an extensive out-of-sample forecasting study. Our main conclusion is that macroeconomic variables can lead to more accurate yield curve forecasts.The Annals of Computational and Financial Econometrics, first issue
http://repub.eur.nl/pub/52218/
Thu, 01 Nov 2012 00:00:01 GMT<div>D. Belsley</div><div>E.J. Kontoghiorghes</div><div>H.K. van Dijk</div><div>L. Bauwens</div><div>D. Belsley</div><div>E.J. Kontoghiorghes</div><div>S.J. Koopman</div><div>M.J. McAleer</div><div>H.K. van Dijk</div><div>A. Amendola</div><div>M. Billio</div><div>C. Croux</div><div>C.W.S. Chen</div><div>R. Davidson</div><div>P. Duchesne</div><div>P. Foschi</div><div>C. Francq</div><div>A.-M. Fuertes</div><div>G. Koop</div><div>L. Khalaf</div><div>M. Paolella</div><div>I. Pollock</div><div>E. Ruiz</div><div>R. Paap</div><div>T. Proietti</div><div>P. Winker</div><div>P.L.H. Yu</div><div>J.-M. Zakoian</div><div>A. Zeileis</div>
Forecasting Interest Rates with Shifting Endpoints
http://repub.eur.nl/pub/34711/
Sun, 01 Jul 2012 00:00:01 GMT<div>D.J.C. van Dijk</div><div>S.J. Koopman</div><div>M. van der Wel</div><div>J.H. Wright</div>
Many economic studies on inflation forecasting have found favorable results when inflation is modeled as a stationary process around a slowly time-varying trend. In contrast, the existing studies on interest rate forecasting either treat yields as being stationary, without any shifting endpoints, or treat yields as a random walk process. In this study we consider the problem of forecasting the term structure of interest rates with the assumption that the yield curve is driven by factors that are stationary around a time-varying trend. We compare alternative ways of modeling the time-varying trend. We find that allowing for shifting endpoints in yield curve factors can provide gains in the out-of-sample predictive accuracy, relative to stationary and random walk benchmarks. The results are both economically and statistically significant.
Generalized Autoregressive Score Models with Applications
http://repub.eur.nl/pub/34950/
Mon, 23 Jan 2012 00:00:01 GMT<div>D. Creal</div><div>S.J. Koopman</div><div>A. Lucas</div>
We propose a class of observation-driven time series models referred to as generalized autoregressive score (GAS) models. The mechanism to update the parameters over time is the scaled score of the likelihood function. This new approach provides a unified and consistent framework for introducing time-varying parameters in a wide class of nonlinear models. The GAS model encompasses other well-known models such as the generalized autoregressive conditional heteroskedasticity, autoregressive conditional duration, autoregressive conditional intensity, and Poisson count models with time-varying mean. In addition, our approach can lead to new formulations of observation-driven models. We illustrate our framework by introducing new model specifications for time-varying copula functions and for multivariate point processes with time-varying parameters. We study the models in detail and provide simulation and empirical evidence. Maximum likelihood estimation for dynamic factor models with missing data
http://repub.eur.nl/pub/26038/
Mon, 01 Aug 2011 00:00:01 GMT<div>B. Jungbacker</div><div>S.J. Koopman</div><div>M. van der Wel</div>
This paper concerns estimating parameters in a high-dimensional dynamic factor model by the method of maximum likelihood. To accommodate missing data in the analysis, we propose a new model representation for the dynamic factor model. It allows the Kalman filter and related smoothing methods to evaluate the likelihood function and to produce optimal factor estimates in a computationally efficient way when missing data is present. The implementation details of our methods for signal extraction and maximum likelihood estimation are discussed. The computational gains of the new devices are presented based on simulated data sets with varying numbers of missing entries. Forecasting the U.S. Term Structure of Interest Rates using a Macroeconomic Smooth Dynamic Factor Model
http://repub.eur.nl/pub/23262/
Wed, 06 Apr 2011 00:00:01 GMT<div>S.J. Koopman</div><div>M. van der Wel</div>
We extend the class of dynamic factor yield curve models for the inclusion of macro-economic factors. We benefit from recent developments in the dynamic factor literature for extracting the common factors from a large panel of macroeconomic series and for estimating the parameters in the model. We include these factors into a dynamic factor model for the yield curve, in which we model the salient structure of the yield curve by imposing smoothness restrictions on the yield factor loadings via cubic spline functions. We carry out a likelihood-based analysis in which we jointly consider a factor model for the yield curve, a factor model for the macroeconomic series, and their dynamic interactions with the latent dynamic factors. We illustrate the methodology by forecasting the U.S. term structure of interest rates. For this empirical study we use a monthly time series panel of unsmoothed Fama-Bliss zero yields for treasuries of different maturities between 1970 and 2009, which we combine with a macro panel of 110 series over the same sample period. We show that the relation between the macroeconomic factors and yield curve data has an intuitive interpretation, and that there is interdependence between the yield and macroeconomic factors. Finally, we perform an extensive out-of-sample forecasting study. Our main conclusion is that macroeconomic variables can lead to more accurate yield curve forecasts.Exponentionally weighted methods for forecasting intraday time series with multiple seasonal cycles: Comments
http://repub.eur.nl/pub/20284/
Fri, 01 Oct 2010 00:00:01 GMT<div>S.J. Koopman</div><div>M. Ooms</div>
Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson–Siegel Model With Time-Varying Parameters
http://repub.eur.nl/pub/19641/
Thu, 01 Jul 2010 00:00:01 GMT<div>S.J. Koopman</div><div>M.I.P. Mallee</div><div>M. van der Wel</div>
In this article we introduce time-varying parameters in the dynamic Nelson–Siegel yield curve model for the simultaneous analysis and forecasting of interest rates of different maturities. The Nelson–Siegel model has been recently reformulated as a dynamic factor model with vector autoregressive factors. We extend this framework in two directions. First, the factor loadings in the Nelson–Siegel yield model depend on a single loading parameter that we treat as the fourth latent factor. Second, we specify the overall volatility as a generalized autoregressive conditional heteroscedasticity (GARCH) process. We present empirical evidence of considerable increases in within-sample goodness of fit for these advances in the dynamic Nelson–Siegel model.Dynamic Factor Models with Smooth Loadings for Analyzing the Term Structure of Interest Rates
http://repub.eur.nl/pub/16299/
Fri, 01 May 2009 00:00:01 GMT<div>B. Jungbacker</div><div>S.J. Koopman</div><div>M. van der Wel</div>
We propose a new approach to the modelling of the term structure of interest rates. We consider the general dynamic factor model and show how to impose smoothness restrictions on the factor loadings. We further present a statistical procedure based on Wald tests that can be used to find a suitable set of such restrictions. We present these developments in the context of term structure models, but they are also applicable in other settings. We perform an empirical study using a data set of unsmoothed Fama-Bliss zero yields for US treasuries of different maturities. The general dynamic factor model with and without smooth loadings is considered in this study together with models that are associated with Nelson-Siegel and arbitrage-free frameworks. These existing models can be regarded as special cases of the dynamic factor model with restrictions on the model parameters. For all model candidates, we consider both stationary and nonstationary autoregressive processes (with different numbers of lags) for the latent factors. Finally, we perform statistical hypothesis tests to verify whether the restrictions imposed by the models are supported by the data. Our main conclusion is that smoothness restrictions can be imposed on the loadings of dynamic factor models for the term structure of US interest rates but that the restrictions implied by a number of popular term structure models are rejected.Dynamic Factor Analysis in The Presence of Missing Data
http://repub.eur.nl/pub/14942/
Fri, 06 Feb 2009 00:00:01 GMT<div>B. Jungbacker</div><div>S.J. Koopman</div><div>M. van der Wel</div>
We develop a new model representation for high-dimensional dynamic multi-factor models. It allows the Kalman filter and related smoothing methods to produce optimal estimates in a computationally efficient way in the presence of missing data. We discuss the model in detail together with the implementation of methods for signal extraction and parameter estimation. The computational gains of the new devices are presented based on simulated data-sets with varying numbers of missing entriesConstructing seasonally adjusted data with time-varying confidence intervals
http://repub.eur.nl/pub/13718/
Wed, 11 Dec 2002 00:00:01 GMT<div>S.J. Koopman</div><div>Ph.H.B.F. Franses</div>
Seasonal adjustment methods transform observed time series data into estimated data, where these estimated data are constructed such that they show no or almost no seasonal variation. An advantage of model–based methods is that these can provide confidence intervals around the seasonally adjusted data. One particularly useful time series model for seasonal adjustment is the basic structural time series (BSM) model. The usual premise of the BSM is that the variance of each of the components is constant. In this paper we address the possibility that the variance of the trend component in a macroeconomic time series in some way depends on the business cycle. One reason for doing so is that one can expect that there is more uncertainty in recession periods. We extend the BSM by allowing for a business–cycle dependent variance in the level equation. Next we show how this affects the confidence intervals of seasonally adjusted data. We apply our extended BSM to monthly US unemployment and we show that the estimated confidence intervals for seasonally adjusted unemployment change with past changes in the oil price.Constructing seasonally adjusted data with time-varying confidence intervals
http://repub.eur.nl/pub/1667/
Mon, 29 Jan 2001 00:00:01 GMT<div>S.J. Koopman</div><div>Ph.H.B.F. Franses</div>
Seasonal adjustment methods transform observed time series data into estimated data, where these estimated data are constructed such that they show no or almost no seasonal variation. An advantage of model-based methods is that these can provide confidence intervals around the seasonally adjusted data. One particularly useful time series model for seasonal adjustment is the basic structural time series [BSM] model. The usual premise of the BSM is that the variance of each of the components is constant. In this paper we address the possibility that the variance of the trend component in a macro-economic time series in some way depends on the business cycle. One reason for doing so is that one can expect that there is more uncertainty in recession periods. We extend the BSM by allowing for a business-cycle dependent variance in the level equation. Next we show how this affects the confidence intervals of seasonally adjusted data. We apply our extended BSM to monthly US unemployment and we show that the estimated confidence intervals for seasonally adjusted unemployment change with past changes in the oil price.