The Yield Spread and Bond Return Predictability in Expansions and Recessions

2020 ◽  
Author(s):  
Martin M Andreasen ◽  
Tom Engsted ◽  
Stig V Møller ◽  
Magnus Sander
Keyword(s):  
Term Structure ◽  
Yield Curve ◽  
Return Predictability ◽  
Yield Spread ◽  
Market Prices ◽  
Term Structure Model ◽  
Bond Returns ◽  
Bond Return ◽  
Key Driver ◽  
Macro Finance

Abstract This paper uncovers that expected excess bond returns display a positive correlation with the slope of the yield curve (i.e., yield spread) in expansions but a negative correlation in recessions. We use a macro-finance term structure model with different market prices of risk in expansions and recessions to show that a very accommodating monetary policy in recessions is a key driver of this switch in return predictability.

Unspanned Global Macro Risks in Bond Returns

2021 ◽  
Author(s):  
Feng Zhao ◽  
Guofu Zhou ◽  
Xiaoneng Zhu
Keyword(s):  
Term Structure ◽  
Predictive Power ◽  
International Markets ◽  
Bond Markets ◽  
Market Prices ◽  
Macroeconomic Variables ◽  
Bond Returns ◽  
Large Panel ◽  
Term Premia ◽  
Macro Finance

We examine the macro-spanning hypothesis for bond returns in international markets. Based on a large panel of real-time macroeconomic variables that are not subject to revisions, we find that global macro factors have predictive power for bond returns unspanned by yield factors. Furthermore, we estimate macro-finance term structure models with the unspanned global macro factors and find that the global macro factors influence the market prices of level and slope risks and induce comovements in forward term premia in global bond markets. This paper was accepted by David Simchi-Levi, finance.

Understanding the Impact of Monetary Policy in Korea using a Macro-Finance Term Structure Model with

2014 ◽  
Vol 22 (2) ◽  
pp. 161-192
Author(s):  
Woon Wook Jang ◽  
Jaehoon Hahn
Keyword(s):  
Monetary Policy ◽  
Term Structure ◽  
Yield Curve ◽  
Call Rate ◽  
Monetary Policy Shocks ◽  
Macroeconomic Factors ◽  
Term Structure Model ◽  
Term Premium ◽  
Policy Shocks ◽  
Macro Finance

This paper examines the interaction between monetary policy and the macroeconomy using a macro-finance term structure model of Joslin, Priebsch, and Singleton (2012), in which macroeconomic risks are not assumed to be spanned by information about the shape of the yield curve. For model estimation, we apply the Kalman filter to a large number of macroeconomic time series data grouped into output, inflation, and market stress categories and extract three common factors. For the factors determining the shape of the yield curve, we use the call rate, the spread between 10-year government bond yield and the call rate, and a combination of the call rate, 2- and 10-year government bond yields as proxies for the level, slope, and curvature factors. We interpret the call rate as a proxy for both the short rate and the instrument of monetary policy. Empirical results show that the macroeconomic factors have a significant impact on the risk premium associated with monetary policy shocks. Furthermore, we find that monetary policy shocks increase the term premium, which in turn affects the factors determining the yield curve, and such effects on the shape of the yield curve feeds back into the macroeconomic factors. Taken together, empirical findings in this paper can be interpreted as evidence supporting the term premium channel (Ferman, 2011) of monetary policy transmission mechanism.

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 Interest Rates under Falling Stars

2020 ◽  
Vol 110 (5) ◽  
pp. 1316-1354 ◽  
Author(s):  
Michael D. Bauer ◽  
Glenn D. Rudebusch
Keyword(s):  
Interest Rates ◽  
Term Structure ◽  
Yield Curve ◽  
Term Premium ◽  
Long Run ◽  
Trend Inflation ◽  
Bond Returns ◽  
Out Of Sample ◽  
Finance Theory ◽  
Macro Finance

Macro-finance theory implies that trend inflation and the equilibrium real interest rate are fundamental determinants of the yield curve. However, empirical models of the term structure of interest rates generally assume that these fundamentals are constant. We show that accounting for time variation in these underlying long-run trends is crucial for understanding the dynamics of Treasury yields and predicting excess bond returns. We introduce a new arbitrage-free model that captures the key role that long-run trends play in determining interest rates. The model also provides new, more plausible estimates of the term premium and accurate out-of-sample yield forecasts. (JEL E31, E43, E47)

scholarly journals Liquidity-Adjusted Benchmark Yield Curves: A Look at Trading Concentration and Information

2007 ◽  
Vol 10 (04) ◽  
pp. 491-518 ◽  
Author(s):  
William T. Lin ◽  
David S. Sun
Keyword(s):  
Term Structure ◽  
Yield Curve ◽  
Information Discovery ◽  
Affine Term Structure ◽  
Term Structure Model ◽  
Long Run ◽  
Fitting Method ◽  
Bond Issues ◽  
Short Run

Estimation of benchmark yield curve in developing markets is often influenced by liquidity concentration. Based on an affine term structure model, we develop a long run liquidity weighted fitting method to address the trading concentration phenomenon arising from horizon-induced clientele equilibrium as well as information discovery. Specifically, we employ arguments from models of liquidity concentration and benchmark security information. After examining time series behavior of price errors against our fitted model, we find results consistent with both the horizon and information hypotheses. Our evidence indicates that trading liquidity carries information effect in the long run, which cannot be fully captured in the short run. Trading liquidity plays a key role in long run term structure fitting. Markets for liquid benchmark government bond issues collectively form a long term equilibrium. Compared with previous studies, our results provide a robust and realistic characterization of the spot rate term structure and related price forecasting over time, which in turn help portfolio investment of fixed income and long run pricing of financial instruments.

Bond Risk Premiums with Machine Learning

2020 ◽  
Author(s):  
Daniele Bianchi ◽  
Matthias Büchner ◽  
Andrea Tamoni
Keyword(s):  
Machine Learning ◽  
Yield Curve ◽  
Return Predictability ◽  
Time Varying ◽  
Short Term ◽  
Risk Premiums ◽  
Machine Learning Methods ◽  
Yield Information ◽  
Bond Return ◽  
Bond Risk

Abstract We show that machine learning methods, in particular, extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on macroeconomic and yield information translate into economic gains that are larger than those obtained using yields alone. Interestingly, the nature of unspanned factors changes along the yield curve: stock- and labor-market-related variables are more relevant for short-term maturities, whereas output and income variables matter more for longer maturities. Finally, NN forecasts correlate with proxies for time-varying risk aversion and uncertainty, lending support to models featuring both channels.

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