The Experts below are selected from a list of 46299 Experts worldwide ranked by ideXlab platform
Siem Jan Koopman - One of the best experts on this subject based on the ideXlab platform.
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a dynamic Yield Curve model with stochastic volatility and non gaussian interactions an empirical study of non standard monetary policy in the euro area
2014Co-Authors: Geert Mesters, Siem Jan Koopman, Bernd SchwaabAbstract:We develop an econometric methodology for the study of the Yield Curve and its interactions with measures of non-standard monetary policy during possibly turbulent times. The Yield Curve is modeled by the dynamic Nelson-Siegel model while the monetary policy measurements are modeled as non-Gaussian variables that interact with latent dynamic factors, including the Yield factors of level and slope. Yield developments during the financial and sovereign debt crises require the Yield Curve model to be extended with stochastic volatility and heavy tailed disturbances. We develop a flexible estimation method for the model parameters with a novel implementation of the importance sampling technique. We empirically investigate how the Yields in Germany, France, Italy and Spain have been affected by monetary policy measures of the European Central Bank. We model the euro area interbank lending rate EONIA by a log-normal distribution and the bond market purchases within the ECB's Securities Markets Programme by a Poisson distribution. We find evidence that the bond market interventions had a direct and temporary effect on the Yield Curve lasting up to ten weeks, and find limited evidence that purchases changed the relationship between the EONIA rate and the term structure factors.
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forecasting the u s term structure of interest rates using a macroeconomic smooth dynamic factor model
International Journal of Forecasting, 2013Co-Authors: Siem Jan Koopman, Michel Van Der WelAbstract: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.
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forecasting the u s term structure of interest rates using a macroeconomic smooth dynamic factor model
2012Co-Authors: Siem Jan Koopman, Michel Van Der WelAbstract: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.
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forecasting the u s term structure of interest rates using a macroeconomic smooth dynamic factor model
2011Co-Authors: Siem Jan Koopman, Michel Van Der WelAbstract:This discussion paper led to a publication in the International Journal of Forecasting , 2013, 29($), 676-694. See also the publication in the 'Journal of Applied Econometrics' , 2014, 29(1), 65-90. 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.
Jonathan H Wright - One of the best experts on this subject based on the ideXlab platform.
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the u s treasury Yield Curve 1961 to the present
Journal of Monetary Economics, 2007Co-Authors: Refet S Gurkaynak, Brian P Sack, Jonathan H WrightAbstract:The discount function, which determines the value of all future nominal payments, is the most basic building block of finance and is usually inferred from the Treasury Yield Curve. It is therefore surprising that researchers and practitioners do not have available to them a long history of high-frequency Yield Curve estimates. This paper fills that void by making public the Treasury Yield Curve estimates of the Federal Reserve Board at a daily frequency from 1961 to the present. We use a well-known and simple smoothing method that is shown to fit the data very well. The resulting estimates can be used to compute Yields or forward rates for any horizon. We hope that the data, which are posted on the website, and which will be updated periodically, will provide a benchmark Yield Curve that will be useful to applied economists.
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the u s treasury Yield Curve 1961 to the present
Social Science Research Network, 2006Co-Authors: Refet S Gurkaynak, Brian P Sack, Jonathan H WrightAbstract:The discount function, which determines the value of all future nominal payments, is the most basic building block of finance and is usually inferred from the Treasury Yield Curve. It is therefore surprising that researchers and practitioners do not have available to them a long history of high-frequency Yield Curve estimates. This paper fills that void by making public the Treasury Yield Curve estimates of the Federal Reserve Board at a daily frequency from 1961 to the present. We use a well-known and simple smoothing method that is shown to fit the data very well. The resulting estimates can be used to compute Yields or forward rates for any horizon. We hope that the data, which are posted on the website http://www.federalreserve.gov/pubs/feds/2006 and which will be updated periodically, will provide a benchmark Yield Curve that will be useful to applied economists.
Michel Van Der Wel - One of the best experts on this subject based on the ideXlab platform.
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forecasting the u s term structure of interest rates using a macroeconomic smooth dynamic factor model
International Journal of Forecasting, 2013Co-Authors: Siem Jan Koopman, Michel Van Der WelAbstract: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.
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forecasting the u s term structure of interest rates using a macroeconomic smooth dynamic factor model
2012Co-Authors: Siem Jan Koopman, Michel Van Der WelAbstract: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.
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forecasting the u s term structure of interest rates using a macroeconomic smooth dynamic factor model
2011Co-Authors: Siem Jan Koopman, Michel Van Der WelAbstract:This discussion paper led to a publication in the International Journal of Forecasting , 2013, 29($), 676-694. See also the publication in the 'Journal of Applied Econometrics' , 2014, 29(1), 65-90. 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.
Diana Tunaru - One of the best experts on this subject based on the ideXlab platform.
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gaussian estimation and forecasting of the u k Yield Curve with multi factor continuous time models
International Review of Financial Analysis, 2017Co-Authors: Diana TunaruAbstract:In this paper we will estimate the term structure of daily U.K. interest rates using a range of more flexible continuous-time models. A multivariate framework is employed for the dynamic estimation and forecasting of four classic models over the eventful period of 2000–2013. The extensions are applied in two stages to four- and five-factor formulations, allowing us to assess the potential benefit of gradually increasing the model-flexibility. The Gaussian estimation methods for dynamic continuous-time models Yield insightful comparative results concerning the two different segments of the Yield Curve, short- and long-term, respectively. In terms of in-sample performance the newly extended multi-factor general model is superior to all other restricted models. When compared to benchmark discrete-time models, the out-of-sample performance of the extended continuous-time models seems to be consistently superior with regards to the short-term segment of the Yield Curve.
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gaussian estimation and forecasting of the uk Yield Curve with multi factor continuous time models
Social Science Research Network, 2015Co-Authors: Diana TunaruAbstract:In this paper we estimate the term structure of daily UK interest rates using more flexible continuous time models. The multivariate CKLS framework is employed for dynamic estimation and forecasting of four classical models over the eventful period of 2000-2013. The extensions are applied in two stages to four and five factor formulations, allowing us to assess the potential benefit of gradually increasing the model-flexibility. The Gaussian estimation methods for dynamic continuous time models Yield insightful comparative results concerning the two different segments of the Yield Curve, short-term and long-term, respectively. In terms of in-sample performance the multi-factor general CKLS model is superior to all the other restricted models. When compared to benchmark discrete time models, the out-of-sample performance of the extended continuous time models seem to be consistently superior with regards only to the short-term segment of the Yield Curve.
Sebastian Schich - One of the best experts on this subject based on the ideXlab platform.
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how stable is the predictive power of the Yield Curve evidence from germany and the united states
The Review of Economics and Statistics, 2003Co-Authors: Arturo Estrella, Anthony P Rodrigues, Sebastian SchichAbstract:Empirical research over the last decade has uncovered predictive relationships between the slope of the Yield Curve and subsequent real activity and inflation. Some of these relationships are highly significant, but their theoretical motivations suggest that they may not be stable over time. We use recent econometric techniques for break testing to examine whether the empirical relationships are in fact stable. We consider continuous models, which predict either economic growth or inflation, and binary models, which predict either recessions or inflationary pressure. In each case, we draw on evidence from Germany and the United States. Models that predict real activity are somewhat more stable than those that predict inflation, and binary models are more stable than continuous models. The model that predicts recessions is stable over our full sample period in both Germany and the United States.
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how stable is the predictive power of the Yield Curve evidence from germany and the united states
2000Co-Authors: Arturo Estrella, Anthony P Rodrigues, Sebastian SchichAbstract:Empirical research over the last decade has uncovered predictive relationships between the slope of the Yield Curve and subsequent real activity and inflation. Some of these relationships are highly significant, but their theoretical motivations suggest that they may not be stable over time. We use recent econometric techniques for break testing to examine whether the empirical relationships are in fact stable. We consider continuous models, which predict either economic growth or inflation, and binary models, which predict either recessions or inflationary pressure. In each case, we draw on evidence from Germany and the United States. Models that predict real activity are more stable than those that predict inflation, and binary models are more stable than continuous models. The model that predicts recessions is stable over our full sample period in both Germany and the United States.
