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Author: Sydney C. Ludvigson Publisher: ISBN: Category : Bonds Languages : en Pages : 28
Book Description
This paper uses the factor augmented regression framework to analyze the relation between bond excess returns and the macro economy. Using a panel of 131 monthly macroeconomic time series for the sample 1964:1-2007:12, we estimate 8 static factors by the method of asymptotic principal components. We also use Gibb sampling to estimate dynamic factors from the 131 series reorganized into 8 blocks. Regardless of how the factors are estimated, macroeconomic factors are found to have statistically significant predictive power for excess bond returns. We show how a bias correction to the parameter estimates of factor augmented regressions can be obtained. This bias is numerically trivial in our application. The predictive power of real activity for excess bond returns is robust even after accounting for finite sample inference problems. Forecasts of excess bond returns (or bond risk premia) are countercyclical. This implies that investors are compensated for risks associated with recessions.
Author: Sydney C. Ludvigson Publisher: ISBN: Category : Bonds Languages : en Pages : 28
Book Description
This paper uses the factor augmented regression framework to analyze the relation between bond excess returns and the macro economy. Using a panel of 131 monthly macroeconomic time series for the sample 1964:1-2007:12, we estimate 8 static factors by the method of asymptotic principal components. We also use Gibb sampling to estimate dynamic factors from the 131 series reorganized into 8 blocks. Regardless of how the factors are estimated, macroeconomic factors are found to have statistically significant predictive power for excess bond returns. We show how a bias correction to the parameter estimates of factor augmented regressions can be obtained. This bias is numerically trivial in our application. The predictive power of real activity for excess bond returns is robust even after accounting for finite sample inference problems. Forecasts of excess bond returns (or bond risk premia) are countercyclical. This implies that investors are compensated for risks associated with recessions.
Author: Sydney C. Ludvigson Publisher: ISBN: Category : Bonds Languages : en Pages : 22
Book Description
Empirical evidence suggests that excess bond returns are forecastable by financial indicators such as forward spreads and yield spreads, a violation of the expectations hypothesis based on constant risk premia. But existing evidence does not tie the forecastable variation in excess bond returns to underlying macroeconomic fundamentals, as would be expected if the forecastability were attributable to time variation in risk premia. We use the methodology of dynamic factor analysis for large datasets to investigate possible empirical linkages between forecastable variation in excess bond returns and macroeconomic fundamentals. We find that several common factors estimated from a large dataset on U.S. economic activity have important forecasting power for future excess returns on U.S. government bonds. Following Cochrane and Piazzesi (2005), we also construct single predictor state variables by forming linear combinations of either five or six estimated common factors. The single state variables forecast excess bond returns at maturities from two to five years, and do so virtually as well as an unrestricted regression model that includes each common factor as a separate predictor variable. The linear combinations we form are driven by both "real" and "inflation" macro factors, in addition to financial factors, and contain important information about one year ahead excess bond returns that is not captured by forward spreads, yield spreads, or the principal components of the yield covariance matrix.
Author: Kenneth J. Singleton Publisher: Princeton University Press ISBN: 1400829232 Category : Business & Economics Languages : en Pages : 497
Book Description
Written by one of the leading experts in the field, this book focuses on the interplay between model specification, data collection, and econometric testing of dynamic asset pricing models. The first several chapters provide an in-depth treatment of the econometric methods used in analyzing financial time-series models. The remainder explores the goodness-of-fit of preference-based and no-arbitrage models of equity returns and the term structure of interest rates; equity and fixed-income derivatives prices; and the prices of defaultable securities. Singleton addresses the restrictions on the joint distributions of asset returns and other economic variables implied by dynamic asset pricing models, as well as the interplay between model formulation and the choice of econometric estimation strategy. For each pricing problem, he provides a comprehensive overview of the empirical evidence on goodness-of-fit, with tables and graphs that facilitate critical assessment of the current state of the relevant literatures. As an added feature, Singleton includes throughout the book interesting tidbits of new research. These range from empirical results (not reported elsewhere, or updated from Singleton's previous papers) to new observations about model specification and new econometric methods for testing models. Clear and comprehensive, the book will appeal to researchers at financial institutions as well as advanced students of economics and finance, mathematics, and science.
Author: Graham Elliott Publisher: Princeton University Press ISBN: 0691140138 Category : Business & Economics Languages : en Pages : 566
Book Description
A comprehensive and integrated approach to economic forecasting problems Economic forecasting involves choosing simple yet robust models to best approximate highly complex and evolving data-generating processes. This poses unique challenges for researchers in a host of practical forecasting situations, from forecasting budget deficits and assessing financial risk to predicting inflation and stock market returns. Economic Forecasting presents a comprehensive, unified approach to assessing the costs and benefits of different methods currently available to forecasters. This text approaches forecasting problems from the perspective of decision theory and estimation, and demonstrates the profound implications of this approach for how we understand variable selection, estimation, and combination methods for forecasting models, and how we evaluate the resulting forecasts. Both Bayesian and non-Bayesian methods are covered in depth, as are a range of cutting-edge techniques for producing point, interval, and density forecasts. The book features detailed presentations and empirical examples of a range of forecasting methods and shows how to generate forecasts in the presence of large-dimensional sets of predictor variables. The authors pay special attention to how estimation error, model uncertainty, and model instability affect forecasting performance. Presents a comprehensive and integrated approach to assessing the strengths and weaknesses of different forecasting methods Approaches forecasting from a decision theoretic and estimation perspective Covers Bayesian modeling, including methods for generating density forecasts Discusses model selection methods as well as forecast combinations Covers a large range of nonlinear prediction models, including regime switching models, threshold autoregressions, and models with time-varying volatility Features numerous empirical examples Examines the latest advances in forecast evaluation Essential for practitioners and students alike
Author: Harald Tolleshaug Publisher: ISBN: Category : Languages : en Pages : 109
Book Description
Forecasting the expected returns on bonds with increasing certainty is wanted from all rational investors in the fixed income markets. The potential for higher returns increase with the ability to forecast expected returns, through better trading payoffs and improved hedging and risk management. The expectations hypothesis was long prevailing in the academical litterature. It stated that the rational investor was expected to require zero or at least a constant excess return on bonds with long maturity over short maturity. This is equal to no time varying risk premiums. It is however reasonable for the rational investor to have time varying risk preferences based on the economic situation and outlook for the future, as described by Cochrane (1999). Thus, bonds with different maturity may be priced with different risk in an efficient market, and accordingly have time varying risk premiums. The expectations hypothesis has thus been rejected. This has been manifested through the classical studies of Fama and Bliss (1987) as well as Campbell and Shiller (1991). These studies modelled predictions of bond returns on specific maturities, with a R2 up to 18%. In a new and original approach, Cochrane and Piazzesi (2005) models a single-factor that predicts bond returns of any maturity, with a R2 up to 44%, more than doubled from the studies mentioned above. This is done on the same dataset as Fama and Bliss (1987) used and would be a big discovery within the field, if the model can be accepted across time and datasets. I test the model of Cochrane and Piazzesi (2005) based on the framework that these used originally, as well as new tests they have provided as response to critique of the model. So far, no other paper has rejected this model on all these dimensions. I use very well accepted data, and reject the model in every dimension tested. This paper is thus the rejection of the Cochrane and Piazzesi (2005) single-factor bond forecasting model.
Author: Daniel L. Thornton Publisher: ISBN: Category : Languages : en Pages : 36
Book Description
This paper investigates the source of predictability of bond risk premia by means of long-term forward interest rates. We show that the predictive ability of forward rates could be due to the high serial correlation and cross-correlation of bond prices. After a simple reparametrization of models used to predict spot rates or excess returns, we find that forward rates exhibit much less predictive power than previously recorded. Furthermore, our economic value analysis indicates that there are no economic gains to mean-variance investors who use the predictions of these models in a stylized dynamic asset allocation strategy.
Author: Michael D. Bauer Publisher: ISBN: Category : Bonds Languages : en Pages : 61
Book Description
A consensus has recently emerged that variables beyond the level, slope, and curvature of the yield curve can help predict bond returns. This paper shows that the statistical tests underlying this evidence are subject to serious small-sample distortions. We propose more robust tests, including a novel bootstrap procedure specifically designed to test the spanning hypothesis. We revisit the analysis in six published studies and find that the evidence against the spanning hypothesis is much weaker than it originally appeared. Our results pose a serious challenge to the prevailing consensus.
Author: Sydney C. Ludvigson
Author: Sydney C. Ludvigson
Author: Lukas Wäger
Author: Sydney C. Ludvigson
Author: Kenneth J. Singleton
Author: Stefano Nobili
Author: Livingston G. Douglas
Author: Graham Elliott
Author: Harald Tolleshaug
Author: Daniel L. Thornton
Author: Michael D. Bauer