Calibrating the Nelson-Siegel-Svensson Model

Gilli, Manfred; Große, Stefan; Schumann, Enrico

doi:10.2139/ssrn.1676747

Cited by 35 publications

(23 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Parameters have been estimated by minimizing the sum of squared errors between market and model prices. We use a gradient descent algorithm with randomly chosen starting values as described in Gilli et al (2010). The optimal parameters are given in Table 5.…”

Section: Parameters Estimationmentioning

confidence: 99%

Kriging of financial term-structures

Cousin

Maatouk

Rullière

2016

European Journal of Operational Research

View full text Add to dashboard Cite

Due to the lack of reliable market information, building financial term-structures may be associated with a significant degree of uncertainty. In this paper, we propose a new term-structure interpolation method that extends classical spline techniques by additionally allowing for quantification of uncertainty. The proposed method is based on a generalization of kriging models with linear equality constraints (market-fit conditions) and shape-preserving conditions such as monotonicity or positivity (no-arbitrage conditions). We define the most likely curve and show how to build confidence bands. The Gaussian process covariance hyper-parameters under the construction constraints are estimated using cross-validation techniques. Based on observed market quotes at different dates, we demonstrate the efficiency of the method by building curves together with confidence intervals for term-structures of OIS discount rates, of zero-coupon swaps rates and of CDS implied default probabilities. We also show how to construct interest-rate surfaces or default probability surfaces by considering time (quotation dates) as an additional dimension. JEL classification C63; E43; G12

show abstract

Section: Parameters Estimationmentioning

confidence: 99%

Kriging of financial term-structures

Cousin

Maatouk

Rullière

2016

European Journal of Operational Research

View full text Add to dashboard Cite

show abstract

“…Source: Author's calculations Considering that is a nonlinear parameter and numerically unstable, almost all the authors fixed the value of , Fabozzi, Martellini & Priaulet (2005) used a value of 3 years, and Diebold & Li (2006) fixed a value of 1.3684 years. Gilli, Große, & Schumann (2010) reported numerical difficulties when calibrating the model, particularly they established two problems. The first one is the optimization issue that results from a not convex problem with multiple local minima.…”

Section: Figure 3: Historical Values Of Defined With a Nonlinear Optimentioning

confidence: 99%

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

Vela¹

2013

View full text Add to dashboard Cite

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.

show abstract

“…6 The discussion on numerical issues related to the estimation and calibration of the DNS model can be found in Gilli et al (2010) 7 Following the literature (e.g. Diebold et al, 2006, Diebold andLi, 2002) we refer to empirical counterparts of the estimated factors.…”

Section: The Nelson-siegel Modelmentioning

confidence: 99%

Measuring the Natural Yield Curve

Brzoza‐Brzezina

Kotłowski

2012

SSRN Journal

View full text Add to dashboard Cite

The views expressed herein are ours and not necessarily those of the National Bank of Poland or the Warsaw School of Economics. This research project was conducted under the NBP Economic Research Committee's open competition for research projects to be carried out by the NBP staff and economists from outside the NBP and was financed by the National Bank of Poland. We would like to thank Ryszard Kokoszczyński, an anonymous referee and the participants of the seminars at the National Bank of Poland and Warsaw School of Economics for useful comments and discussions.

show abstract

Calibrating the Nelson-Siegel-Svensson Model

Cited by 35 publications

References 12 publications

Kriging of financial term-structures

Kriging of financial term-structures

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

Measuring the Natural Yield Curve

Contact Info

Product

Resources

About