• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.349-360
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• 2023

Dynamic Nelson-Siegel model is widely used in modeling term structure of interest rates for financial products. In this study, we explain dynamic Nelson-Siegel model from the perspective of the state space model and explore Bayesian approaches that can be applied to that model. By applying SOFR term rate data to the Bayesian dynamic Nelson-Siegel model, we confirm the performance and compare it with other competing models such as Vasicek model, dynamic Nelson-Siegel model based on the frequentist approach, and the two-factor Bayesian dynamic Nelson-Siegel model. We also confirm that the Bayesian dynamic Nelson-Siegel model outperformed its competitors on SOFR term rate data based on RMSE.

• The Korean Journal of Financial Management
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• v.25 no.2
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• pp.1-31
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• 2008

Before the financial crisis in 1997, Korean bond markets had been those of corporate bonds with relatively high market yield. During the period, most of major institutional investors tend to utilize passive strategies such as buying and holding. After the crisis, however, they could not help choosing active bond management strategies because of lowed yield level and intensified competition among the financial institutions. This study is forced on the yield curve, which is the reflection of all information on the bond investment environments. The study also make analysis on the major economic and securities market factors and its structural relationship with the shape of the curve such as level, curvature and slope. For these purposes, an empirical model based on the Nelson-Siegel Model is estimated with the data during $1999{\sim}2006$. Out-of-sample forecasting is also made to test the usefulness of the estimated model. In addition, the dependent variables which are the estimates of level and slope are estimated on the macro variables and securities market variables. VAR and SUR models are used for the estimation. Estimation results show that level and slope of the yield curve are influenced by the target call rate change, exchange rate change rate, inflation rate. These results provide practical implications for the active managements in the overall treasury bond markets.

• Economic Analysis
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• v.26 no.1
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• pp.84-132
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• 2020

A central bank infers market expectations of future yields based on yield curves. The central bank needs to precisely understand the changes in market expectations of future yields in order to have a more effective monetary policy. This need explains why a range of models have attempted to produce yield curves and market expectations that are as accurate as possible. Alongside the development of bond markets, the interconnectedness between them and macroeconomic factors has deepened, and this has rendered understanding of what macroeconomic variables affect yield curves even more important. However, the existence of various theories about determinants of yields inevitably means that previous studies have applied different macroeconomics variables when estimating yield curves. This indicates model uncertainties and naturally poses a question: Which model better estimates yield curves? Put differently, which variables should be applied to better estimate yield curves? This study employs the Dynamic Nelson-Siegel Model and takes the Bayesian approach to variable selection in order to ensure precision in estimating yield curves and market expectations of future yields. Bayesian variable selection may be an effective estimation method because it is expected to alleviate problems arising from a priori selection of the key variables comprising a model, and because it is a comprehensive approach that efficiently reflects model uncertainties in estimations. A comparison of Bayesian variable selection with the models of previous studies finds that the question of which macroeconomic variables are applied to a model has considerable impact on market expectations of future yields. This shows that model uncertainties exert great influence on the resultant estimates, and that it is reasonable to reflect model uncertainties in the estimation. Those implications are underscored by the superior forecasting performance of Bayesian variable selection models over those models used in previous studies. Therefore, the use of a Bayesian variable selection model is advisable in estimating yield curves and market expectations of yield curves with greater exactitude in consideration of the impact of model uncertainties on the estimation.