Nelson–Siegel, Affine and Quadratic Yield Curve Specifications: Which One is Better at Forecasting?

Nyholm, Ken; Vidova-Koleva, Rositsa

doi:10.1002/for.1239

Cited by 14 publications

(3 citation statements)

References 42 publications

(68 reference statements)

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“…However, there are improvements in the NS‐based models as the forecast horizon lengthens, especially by the NLS method using the monthly data. Our findings are in contrast to some studies (such as Diebold & Li, 2006, and Nyholm & Vidova‐Koleva, 2012) that the NS‐based models outperform the RW when liquidity trap is not present. However, our results are largely consistent with respect to Steeley (2014), who reports that simple competitor models (in particular, the RW model) seem to provide better out‐of‐sample forecasts in the presence of liquidity trap.…”

Section: Out‐of‐sample Forecastscontrasting

confidence: 99%

Forecasting term structure of the Japanese bond yields in the presence of a liquidity trap

Tsui

Zhang

et al. 2023

Journal of Forecasting

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The Nelson–Siegel (NS) model is widely used in practice to fit the term structure of interest rates largely due to its high efficacy in the in‐sample fit and out‐of‐sample forecasting of bond yields. In this paper, we compare forecasting performances of estimated yields from the Nelson–Siegel‐based models and some simpler time series models, using the daily, weekly, and monthly data during a prolong period of liquidity trap in Japan. We find that the out‐of‐sample expanding window forecasts by NS‐based models in general perform less satisfactory than the competitor models. However, the NS‐based models can be useful in forecasting yields over longer horizons and can work well with GARCH‐type structures in modeling the conditional volatility.

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Section: Out‐of‐sample Forecastscontrasting

confidence: 99%

Forecasting term structure of the Japanese bond yields in the presence of a liquidity trap

Tsui

Zhang

et al. 2023

Journal of Forecasting

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show abstract

“…Furthermore, the practical relevance of the Nelson-Siegel framework is witnessed by the fact that some of the main financial institutions as the International Monetary Fund (Gasha et al [54]), the European Central Bank and the Federal Reserve (Moench [55], Bolotnyy [56]) rely on it for their studies and activities. Nyholm and Vidova-Koleva [57] confirm that the best out-of-sample performance is generated by three-factor affine models and the dynamic Nelson-Siegel model variants, while quadratic models provide the best in-sample fit.…”

Section: Background On Estimating and Forecasting Models Of Interest mentioning

confidence: 85%

Forecasting Interest Rates Using Geostatistical Techniques

Arbia

Marcantonio

2015

Econometrics

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Geostatistical spatial models are widely used in many applied fields to forecast data observed on continuous three-dimensional surfaces. We propose to extend their use to finance and, in particular, to forecasting yield curves. We present the results of an empirical application where we apply the proposed method to forecast Euro Zero Rates (2003–2014) using the Ordinary Kriging method based on the anisotropic variogram. Furthermore, a comparison with other recent methods for forecasting yield curves is proposed. The results show that the model is characterized by good levels of predictions’ accuracy and it is competitive with the other forecasting models considered

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“…relative factors based on principal components, the random walk or AR (1) model of exchange rates) [11]. Such an extension would be along the lines of recent studies, notably Nyholm and Vidova–Koleva (2012) and Steeley (2014), which evaluate the out-of-sample forecast performance of contending models of the yield curve. .…”

Section: Discussionmentioning

confidence: 99%

The relative term structure and the Australian-US exchange rate

Bui

Fisher

2016

SEF

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Purpose The purpose of this paper is to investigate whether the factors that summarise the information in the yield curves of Australia and the USA can predict changes in the Australian–USA exchange rate (i.e. the AUD/USD rate) and Australian dollar excess returns. Design/methodology/approach The paper extracts the three Nelson–Siegel factors (level, slope and curvature) from the relative yield curve of Australia with the USA to predict changes in the bilateral exchange rate and excess returns on the Australian dollar. The full sample regressions allow for a shift in the coefficient on the relative curvature factor which can account for the impact of the Fed’s changed monetary policy to one of quantitative easing. Findings The paper finds that the relative curvature factor strongly predicts changes in the AUD/USD exchange rate and Australian dollar excess returns out to 12 months ahead in the sample that precedes the Fed’s policy of quantitative easing. The relative curvature factor retains its predictive power in the full sample regressions but anticipates smaller exchange rate changes and excess currency returns in in-sample predictions made from August 2007. Practical implications The yield curves of Australia and the USA reliably reflect investor’s expectations about prospective monetary policies in each economy. Originality/value The paper investigates the predictive content of the relative Nelson–Siegel factors for changes in the AUD/USD exchange rate and for Australian dollar excess returns over various forecast horizons for a period that covers the Fed’s policy of quantitative easing.

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Nelson–Siegel, Affine and Quadratic Yield Curve Specifications: Which One is Better at Forecasting?

Cited by 14 publications

References 42 publications

Forecasting term structure of the Japanese bond yields in the presence of a liquidity trap

Forecasting term structure of the Japanese bond yields in the presence of a liquidity trap

Forecasting Interest Rates Using Geostatistical Techniques

The relative term structure and the Australian-US exchange rate

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