Forecasting the Brazilian yield curve using forward-looking variables

Vieira, Fausto José Araújo; Fernandes, Marcelo; Chague, Fernando

doi:10.1016/j.ijforecast.2016.08.001

Cited by 15 publications

(8 citation statements)

References 21 publications

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“…Several hypotheses argue that the information in the yield curve is forward-looking and therefore should have predictive power for real growth [18]. In fact, predicting real economic activity in essence needs the presence of market determined yield curve and its reflection of the expectations about inflation/future movements in short term rates, or to state alternatively, regulation of financial markets should be limited.…”

Section: Theoretical Underpinnings Of the Relationship Between Yield mentioning

confidence: 99%

Wavelet Neural Network Model for Yield Spread Forecasting

Shah

Debnath

2017

Mathematics

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Abstract:In this study, a hybrid method based on coupling discrete wavelet transforms (DWTs) and artificial neural network (ANN) for yield spread forecasting is proposed. The discrete wavelet transform (DWT) using five different wavelet families is applied to decompose the five different yield spreads constructed at shorter end, longer end, and policy relevant area of the yield curve to eliminate noise from them. The wavelet coefficients are then used as inputs into Levenberg-Marquardt (LM) ANN models to forecast the predictive power of each of these spreads for output growth. We find that the yield spreads constructed at the shorter end and policy relevant areas of the yield curve have a better predictive power to forecast the output growth, whereas the yield spreads, which are constructed at the longer end of the yield curve do not seem to have predictive information for output growth. These results provide the robustness to the earlier results.

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Section: Theoretical Underpinnings Of the Relationship Between Yield mentioning

confidence: 99%

Wavelet Neural Network Model for Yield Spread Forecasting

Shah

Debnath

2017

Mathematics

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“…Even though the main focus of the worldwide academy research currently remains centered on US Treasury yield curve, it is worth mentioning some studies that address the interrelationship of economic drivers and the shape of the sovereign debt curves in other geographies (Nyholm, 2015 ; Nymand-Andersen, 2018 ; Barunik & Fiser, 2019 , and Umar et al, 2020 ). Ojo et al, ( 2017 ), study the components of the term structure in Canada, while (Vieira et al, 2017 , and Caldeira et al, 2020 ), are focused on emerging market countries from the bloc known as BRICS (Brazil, Russia, India, China, and South Africa).…”

Section: Introductionmentioning

confidence: 99%

Oil price shocks and the term structure of the US yield curve: a time–frequency analysis of spillovers and risk transmission

et al. 2022

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This paper investigates the influence of oil demand, oil supply, and risk-driven shocks on the yield curve in the US between 1995 and 2020. The US term-structure shape is modeled by three structural factors, the level, slope, and curvature. Their empirical analysis is performed according to the Diebold-Li modified variant of the widely used Nelson-Siegel model. The technique of wavelet analysis allows investigating the interrelation of shocks in oil prices and the US yield curve along time and frequency domains, simultaneously. We report on low, medium, and high coherence zones, relative to the oil price movements and the changes in the three yield-curve factors. The low coherence intervals indicate the potential for the three latent factors to be used for creating diversification strategies capable of hedging adverse dynamics in the oil market, potentially workable through global crises. We document the variability of dynamic patterns observable for the US sovereign yield factors on per-type-of-shock basis, evidencing the potential role of the US sovereign debt investments for designing cross-asset hedge strategies for commodity and fixed-income markets.

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“…However, since Harvey (1988) and Estrella and Hardouvelis (1991), a large amount of empirical literature has documented the leading indicator property of the slope of the yield curve and established it as an important tool for assessing and predicting the economic progress of a country. The shape of the yield curve indicates the view of the market participants regarding economic activity (Sowmya et al 2016;Vieira et al 2017). The term structure relationship can be best seen by examining yields on government bonds because they are similar in terms of having almost zero default risk, tax treatment, and marketability.…”

Section: Introductionmentioning

confidence: 99%

The Relationship between Yield Curve and Economic Activity: An Analysis of G7 Countries

Kumar

Stauvermann

2021

JRFM

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The yield curve is an important tool to assess the economic progress of a country. In this study, we examine the strength of the relationship between term spread and economic activity, and between the components of the yield curve and economic activity in the G7 countries using monthly data on yield rates and seasonally adjusted data on the industrial production index (IPI). After matching the start and end date of the IPI with the yield rates, the data used and respective time period are as follows: Canada: Mar-1994 to Dec-2018, France: Jan-1999 to Dec-2018, Germany: Oct-2005 to Dec-2018, Italy: Jul-2009 to Dec-2018, Japan: Jul-1994 to Jan–2019, the UK: Jan-1994 to Dec-2018, and the US: Feb-1990 to Jan-2019. The results show positive associations between term spread and economic activity for Canada, France, Germany, Japan, the UK, and the US. For Italy, a negative association is noted. All three empirical factors could predict economic activity for France and Germany at the 12-month horizon only. For all other horizons, the factors’ ability to predict economic activity varies. We observe that by including additional macro-finance variables such as the current economic growth rate and the 3-month yield rate to capture the term structure level effects, the relationship between term spread and economic activity becomes stronger. This implies that the usefulness of yield curve and its decomposed components for the purpose of predicting economic activity should be cautiously modelled and employed for policy.

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Forecasting the Brazilian yield curve using forward-looking variables

Cited by 15 publications

References 21 publications

Wavelet Neural Network Model for Yield Spread Forecasting

Wavelet Neural Network Model for Yield Spread Forecasting

Oil price shocks and the term structure of the US yield curve: a time–frequency analysis of spillovers and risk transmission

The Relationship between Yield Curve and Economic Activity: An Analysis of G7 Countries

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