Is Less More? Alternative Yield Curve Measures and Their Time-Varying Predictive Content for Real Activity

2007 ◽  
Author(s):  
Qingwei Wang ◽  
Andreas Schrimpf
Keyword(s):  
Yield Curve ◽  
Time Varying ◽  
Real Activity ◽  
Predictive Content

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.

scholarly journals Hedging Long-Term Liabilities*

2020 ◽  
Author(s):  
Rogier Quaedvlieg ◽  
Peter Schotman
Keyword(s):  
Yield Curve ◽  
Mean Squared Error ◽  
Factor Model ◽  
Interest Rate Risk ◽  
Time Varying ◽  
Hedging Strategy ◽  
Factor Loadings ◽  
Life Insurers ◽  
Five Factors

Abstract Pension funds and life insurers face interest rate risk arising from the duration mismatch of their assets and liabilities. With the aim of hedging long-term liabilities, we estimate variations of a Nelson–Siegel model using swap returns with maturities up to 50 years. We consider versions with three and five factors, as well as constant and time-varying factor loadings. We find that we need either five factors or time-varying factor loadings in the three-factor model to accommodate the long end of the yield curve. The resulting factor hedge portfolios perform poorly due to strong multicollinearity of the factor loadings in the long end, and are easily beaten by a robust, near Mean-Squared-Error- optimal, hedging strategy that concentrates its weight on the longest available liquid bond.

The Predictive Content of the Yield Curve for In‡flation

2013 ◽  
Author(s):  
Hans Dewachter ◽  
Leonardo Iania ◽  
Marco Lyrio
Keyword(s):  
Yield Curve ◽  
Predictive Content

The Yield Curve and Real Activity

1993 ◽  
Vol 93 (19) ◽  
pp. 1 ◽  
Author(s):  
Zuliu Hu ◽  
Keyword(s):  
Yield Curve ◽  
Real Activity

Measuring Uncertainty

2015 ◽  
Vol 105 (3) ◽  
pp. 1177-1216 ◽  
Author(s):  
Kyle Jurado ◽  
Sydney C. Ludvigson ◽  
Serena Ng
Keyword(s):  
Business Cycles ◽  
Time Varying ◽  
Real Activity ◽  
Uncertainty Shocks ◽  
Macroeconomic Uncertainty

This paper exploits a data rich environment to provide direct econometric estimates of time-varying macroeconomic uncertainty. Our estimates display significant independent variations from popular uncertainty proxies, suggesting that much of the variation in the proxies is not driven by uncertainty. Quantitatively important uncertainty episodes appear far more infrequently than indicated by popular uncertainty proxies, but when they do occur, they are larger, more persistent, and are more correlated with real activity. Our estimates provide a benchmark to evaluate theories for which uncertainty shocks play a role in business cycles. (JEL C53, D81, E32, G12, G35, L25)

scholarly journals Estimating the Level of the Brazilian Yield Curve Using the Time-Varying Coefficient Model GAS (2,2) with Gamma Distribution

2019 ◽  
Vol 11 (9) ◽  
pp. 1
Author(s):  
Daiane Rodrigues dos Santos ◽  
Tiago Costa Ribeiro ◽  
Marco Aurélio Sanfins
Keyword(s):  
Time Series ◽  
Gamma Distribution ◽  
Yield Curve ◽  
Developing Economies ◽  
Evaluation Criteria ◽  
Score Function ◽  
Predictive Distribution ◽  
Time Varying ◽  
Varying Coefficient ◽  
Time Series Modelling

The level of the yield curve is strongly associated with a very important macroeconomic variable for developing economies: the inflation. Therefore, it becomes relevant for economic studies the development of a time series model that can accurately predict this variable. This article proposes the estimation and prediction of the yield curve level using the GAS (Generalized Autoregressive Score) class of time-varying coefficient models. The formulation of these models facilitates a general framework for time series modelling presenting a series of advantages, including the possibility of specifying any conditional distribution deemed appropriate for the yield curve level. In addition, the complete structure of the predictive distribution is transported to the mechanism that updates the time-varying parameters, via score function. When analyzing the evaluation criteria, the measures of adherence, and both Wilcoxon and Diebold & Mariano tests, it was verified that the adjustment of the GAS model (2,2) with gamma distribution to the series containing the Brazilian Yield Curve level of January 2006 and February 2017 presented a satisfactory result.

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