Modeling and Forecasting the Yield Curve by an Extended Nelson‐Siegel Class of Models: A Quantile Autoregression Approach

Rezende, Rafael B. De; Ferreira, Mauro Sayar

doi:10.1002/for.1256

Cited by 32 publications

(37 citation statements)

References 53 publications

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“…Later, Bliss [26] introduced the Extended Nelson-Siegel [1] [25] model and the six parameters Rezende [30] model, known as Nelson-Siegel-Svensson model. The reason they chose the Nelson-Siegel family is that these models have substantial flexibility required to match the changing shape of the yield curve, yet they only use a few parameters.…”

Section: Parsimonious Modelsmentioning

confidence: 99%

In Search of the Best Zero Coupon Yield Curve for Nairobi Securities Exchange: Interpolation Methods vs. Parametric Models

Muthoni¹

2015

JMF

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We seek to determine which yield curve construction method produces the best zero coupon yield curve (ZCYC) for Nairobi Securities Exchange (NSE). The ZCYC should be differentiable at all points and at the same time, should produce a continuous and positive forward curve at all knot points. A decreasing discount curve is also expected from the resulting ZCYC, as an indication of monotonicity. For the interpolation method, we will use an improvement of monotone preserving interpolation method on ( ) r t t , while the Nelson and Siegel [1] model is the parametric model of choice. This is because compared to other interpolation methods, the improvement of monotone preserving interpolation method on ( ) r t t produces curves with the desirable trait of differentiability, while the Nelson-Siegel [1] model is shown to produce the best-fit results for Kenyan bond data. We compare the models' performance in terms of accuracy in pricing back the fixed-income securities. For this study, we use bond data from Central Bank of Kenya (CBK). The better of the two methods will be used for the Kenyan securities market and, consequently, the East African Securities markets.

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Section: Parsimonious Modelsmentioning

confidence: 99%

In Search of the Best Zero Coupon Yield Curve for Nairobi Securities Exchange: Interpolation Methods vs. Parametric Models

Muthoni¹

2015

JMF

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“…Yu and Zivot (2011) used the dynamic Nelson-Siegel model to forecast corporate bond yields. Other applications of the dynamic Nelson-Siegel model are Vicente and Tabak (2008), Caldeira et al (2014) and Rezende and Ferreira (2011). Koopman et al (2010) extended the DNS specification to allow (generalized) autoregressive conditional heteroskedasticity ((G)ARCH) volatility and a fourth dynamic factor which allows time dependence to the otherwise fixed parametric factor loading curves.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the US Term Structure of Interest Rates Using Nonparametric Functional Data Analysis

Caldeira

Torrent

2016

Journal of Forecasting

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In this paper we consider a novel procedure to forecasting the US zero coupon bond yields for a continuum of maturities by using the methodology of nonparametric functional data analysis (NP‐FDA). We interpret the US yields as curves since the term structure of interest rates defines a relation between the yield of a bond and its maturity. Within the NP‐FDA approach, each curve is viewed as a functional random variable and the dynamics present in the sample are modeled without imposing any parametric structure. In order to evaluate forecast the performance of the proposed estimator, we consider forecast horizons h = 1,3,6,12… months and the results are compared with widely known benchmark models. Our estimates with NP‐FDA present predictive performance superior to its competitors in many situations considered, especially for short‐term maturities. Copyright © 2016 John Wiley & Sons, Ltd.

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“…Later on, Diebold and Li (2006) determined that the future dynamic of the term structure of interest rates could be described by using a statistical or parametric model that forecasted the factors, which are restructured into the yield curve with the NS model. Subsequent authors, such as De Pooter (2007), Cziráky (2007) or De Rezende & Ferreira (2011), studied the forecasting ability of other functional forms that depart from the NS model, like the Svensson model (1994), Bliss model (1997) or Björk & Christensen (1999).…”

Section: Introductionmentioning

confidence: 99%

“…Almost all the literature that uses the Diebold & Li methodology to forecast the term structure of interest rates focuses exclusively on the yield curve of United States. Few authors, like Bolder (2006) or De Rezende & Ferreira (2011), evaluated the methodology in different markets. Therefore, one of the contributions of this paper is that it tests this methodology in Latin-American yield curves (Colombia, Mexico, Chile and Peru).…”

Section: Introductionmentioning

confidence: 99%

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Forecasting latin-american yield curves: an artificial neural network approach

Vela¹

2013

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This document explores the predictive power of the yield curves in Latin America (Colombia, Mexico, Peru and Chile) taking into account the factors set by the specifications of Nelson & Siegel and Svensson. Several forecasting methodologies are contrasted: an autoregressive model, a vector autoregressive model, artificial neural networks on each individual factor, and artificial neural networks on all factors that explain the yield curve. The out-of-sample performance of the fitting models improves with the neural networks in the one-month-ahead forecast along all studied yield curves. Moreover, the three factor model developed by Nelson & Siegel proves to be the best choice for out-of-sample forecasting. Finally, the success of the cross variable interaction strongly depends on the selected yield curve.

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Modeling and Forecasting the Yield Curve by an Extended Nelson‐Siegel Class of Models: A Quantile Autoregression Approach

Cited by 32 publications

References 53 publications

In Search of the Best Zero Coupon Yield Curve for Nairobi Securities Exchange: Interpolation Methods vs. Parametric Models

In Search of the Best Zero Coupon Yield Curve for Nairobi Securities Exchange: Interpolation Methods vs. Parametric Models

Forecasting the US Term Structure of Interest Rates Using Nonparametric Functional Data Analysis

Forecasting latin-american yield curves: an artificial neural network approach

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