scholarly journals Bond Risk Premia and Gaussian Term Structure Models

2018 ◽  
Vol 64 (3) ◽  
pp. 1413-1439 ◽  
Author(s):  
Bruno Feunou ◽  
Jean-Sébastien Fontaine
Keyword(s):  
Term Structure ◽  
Risk Premia ◽  
Term Structure Models ◽  
Bond Risk Premia ◽  
Bond Risk

scholarly journals Bond Risk Premia and Restrictions on Risk Prices

2018 ◽  
Vol 11 (4) ◽  
pp. 60 ◽  
Author(s):  
Constantino Hevia ◽  
Martin Sola
Keyword(s):  
Term Structure ◽  
Risk Premium ◽  
Risk Premia ◽  
Risk Premiums ◽  
Affine Term Structure ◽  
Affine Term Structure Models ◽  
Term Structure Models ◽  
Bond Risk Premia ◽  
Measures Of Risk ◽  
Bond Risk

Researchers who estimate affine term structure models often impose overidentifying restrictions (restrictions on parameters beyond those necessary for identification) for a variety of reasons. While some of those restrictions seem to have minor effects on the extracted factors and some measures of risk premia, such as the forward risk premium, they may have a large impact on other measures of risk premia that is often ignored. In this paper, we analyze how apparently innocuous overidentifying restrictions imposed on affine term structure models can lead to large differences in several measures of risk premiums.

scholarly journals Bond risk premia in consumption‐based models

10.3982/qe887 ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 1461-1484 ◽  
Author(s):  
Drew D. Creal ◽  
Jing Cynthia Wu
Keyword(s):  
Term Structure ◽  
Time Preference ◽  
Risk Premia ◽  
Recursive Preferences ◽  
Affine Term Structure ◽  
Affine Term Structure Models ◽  
Bond Risk Premia ◽  
Bond Risk ◽  
Term Premia ◽  
Conditional Means

Gaussian affine term structure models attribute time‐varying bond risk premia to changing risk prices driven by the conditional means of the risk factors, while structural models with recursive preferences credit it to stochastic volatility. We reconcile these competing channels by introducing a novel form of stochastic rate of time preference into an otherwise standard model with recursive preferences. Our model is affine and has analytical bond prices making it empirically tractable. We use particle Markov chain Monte Carlo to estimate the model, and find that time variation in bond term premia is predominantly driven by the risk price channel.

Forecasting Bond Risk Premia with Unspanned Macroeconomic Information

2019 ◽  
Vol 09 (01) ◽  
pp. 1940001
Author(s):  
Rui Liu
Keyword(s):  
Term Structure ◽  
Yield Curve ◽  
Predictive Accuracy ◽  
Risk Premia ◽  
Term Structure Model ◽  
Bond Risk Premia ◽  
Macroeconomic Information ◽  
Combining Forecasts ◽  
Bond Risk ◽  
Macro Finance

I provide evidence on the existence of unspanned macro risk. I investigate the usefulness of unspanned macro information for forecasting bond risk premia in a macro-finance term structure model from the perspective of a bond investor. I account for model uncertainty by combining forecasts with and without unspanned output and inflation risks optimally from the forecaster’s objective. Incorporating macro information generates significant gains in forecasting bond risk premia relative to yield curve information at long forecast horizons, especially when allowing for time-varying combination weight. These gains in predictive accuracy significantly improve investor utility.

scholarly journals Bond risk premia and the return forecasting factor

2019 ◽  
Vol 24 (1) ◽  
Author(s):  
Agustin Gutierrez ◽  
Constantino Hevia ◽  
Martin Sola
Keyword(s):  
Term Structure ◽  
Yield Curve ◽  
Risk Premia ◽  
Term Structure Model ◽  
Forward Rates ◽  
Bond Risk Premia ◽  
Bond Returns ◽  
Main Driver ◽  
Bond Risk ◽  
Better Than

Abstract The return forecasting factor is a linear combination of forward rates that seems to predict 1-year excess bond returns of bond of all maturities better than traditional measures obtained from the yield curve. If this single factor actually captures all the relevant fluctuations in bond risk premia, then it should also summarize all the economically relevant variations in excess returns considering different holding periods. We find that it does not. We conclude that including the return forecasting factor as the main driver of risk premia in a term structure model, as has been suggested, is not supported by the data.

scholarly journals Expected Business Conditions and Bond Risk Premia

2017 ◽  
Vol 52 (4) ◽  
pp. 1667-1703 ◽  
Author(s):  
Jonas N. Eriksen
Keyword(s):  
Term Structure ◽  
Predictive Accuracy ◽  
Risk Premia ◽  
Forecast Performance ◽  
Survey Of Professional Forecasters ◽  
Bond Risk Premia ◽  
Predictive Regressions ◽  
Out Of Sample ◽  
Business Conditions ◽  
Bond Risk

In this article, I study the predictability of bond risk premia by means of expectations to future business conditions using survey forecasts from the Survey of Professional Forecasters. I show that expected business conditions consistently affect excess bond returns and that the inclusion of expected business conditions in standard predictive regressions improve forecast performance relative to models using information derived from the current term structure or macroeconomic variables. The results are confirmed in a real-time out-of-sample exercise, where the predictive accuracy of the models is evaluated both statistically and from the perspective of a mean-variance investor that trades in the bond market.

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