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This paper estimates and forecasts yield curve of korea bond market using a three factor term structure model based on the Nelson-Siegel model. The Nelson-Siegel model is in-terpreted as a model of level, slope and curvature and has the flexibility required to match the changing shape of the yield curve. To estimate this model, we use the two-step estima-tion procedure as in Diebold and Li. Estimation results show our model is Quite flexible and gives a very good fit to data.

To see the forecasting ability of our model, we compare the RMSEs (root mean square error) of our model to random walk (RW) model and principal component model for out-of sample period as well as in-sample period. we find that our model has better forecasting performances over principal component model but shows slight edge over RW model especially for long run forecasting period. Considering that it is difficult for any model to show better forecasting ability over the RW model in out-of-sample period, results suggest that our model is useful for practitioners to forecast yields curve dynamics.

The purpose of this paper is to analyze and forecast the Chinese term structure of interest rates using functional principal component analysis (FPCA).

The authors propose an FPCA-K model using FPCA. The forecasting of the yield curve is based on modeling functional principal component (FPC) scores as standard scalar time series models. The authors evaluate the out-of-sample forecast performance using the root mean square and mean absolute errors.

Monthly yield data from January 2002 to December 2016 are used in this paper. The authors find that in the full sample, the first two FPCs account for 98.68 percent of the total variation in the yield curve. The authors then construct an FPCA-K model using the leading principal components. The authors find that the FPCA-K model compares favorably with the functional signal plus noise model, the dynamic Nelson-Siegel models and the random walk model in the out-of-sample forecasting.

The authors propose a functional approach to analyzing and forecasting the yield curve, which effectively utilizes the smoothness assumption and conveniently addresses the missing-data issue.

To the best knowledge, the authors are the first to use FPCA in the modeling and forecasting of yield curves.

This paper estimates a Nelson-Siegel model under the state-space representation in order to circumvent the shortcomings of the conventional Nelson-Siegel model and evaluates the predictive ability of the estimated model. The results indicate that the estimated Nelson-Siegel time-varying three factors have close relations to their counterparts : level, slope and curvature and the inflection of the Korean yield curve is located around the maturity of 55-month. Meanwhile, each factor is found to have unit-root but differenced-factors do not show signs of unit-roots, hence proved I (1) series. In order to assess the efficacy of the estimated model, we compare the yield prediction from our model with several natural competitors : random walk, Fama-Bliss, and Cochrane-Piazzesi. With respect to out-of-sample performance, Fama-Bliss model proves to be the worst in term structure forecasts in Korea. The predictive performance differs between the random walk and the state-space Nelson-Siegel model depending on the forecast horizon lengths. At the shorter horizon, the state-space Nelson-Siegel model outperforms the random walk, but the table is turned in the longer horizon

This paper aims to propose a scenario-based approach for measuring interest rate risks. Many regulatory capital standards in banking and insurance make use of similar approaches. The authors provide a theoretical justification and extensive backtesting of our approach.

The authors theoretically derive a scenario-based value-at-risk for interest rate risks based on a principal component analysis. The authors calibrate their approach based on the Nelson–Siegel model, which is modified to account for lower bounds for interest rates. The authors backtest the model outcomes against historical yield curve changes for a large number of generated asset–liability portfolios. In addition, the authors backtest the scenario-based value-at-risk against the stochastic model.

The backtesting results of the adjusted Nelson–Siegel model (accounting for a lower bound) are similar to those of the traditional Nelson–Siegel model. The suitability of the scenario-based value-at-risk can be substantially improved by allowing for correlation parameters in the aggregation of the scenario outcomes. Implementing those parameters is straightforward with the replacement of Pearson correlations by value-at-risk-implied tail correlations in situations where risk factors are not elliptically distributed.

The paper assumes deterministic cash flow patterns. The authors discuss the applicability of their approach, e.g. for insurance companies.

The authors’ approach can be used to better communicate interest rate risks using scenarios. Discussing risk measurement results with decision makers can help to backtest stochastic-term structure models.

The authors’ adjustment of the Nelson–Siegel model to account for lower bounds makes the model more useful in the current low-yield environment when unjustifiably high negative interest rates need to be avoided. The proposed scenario-based value-at-risk allows for a pragmatic measurement of interest rate risks, which nevertheless closely approximates the value-at-risk according to the stochastic model.

This paper aims to describe a robust empirical approach to generating plausible historically based interest rate shocks, which can be applied to any market environment. These interest rate shocks can be readily linked to movements in other key risk factors, and used to measure market risk on institutions with large fixed-income portfolios.

Using yield curve factorization, we parameterize a time series of historical yield curves and measure interest rate shocks as the historical change in each of the model’s factors. We then demonstrate how to add these parameterized shocks to any market environment, while retaining positive rates and plausible credit spreads. Given a set of shocked interest rate curves, joint risk factor movements are calculated based upon historical, reduced form dependencies.

Our approach is based upon yield curve parameterization and requires a parsimonious yet flexible factorization model. In the process of selecting a model, we evaluate three variants of the Nelson–Siegel approach to yield curve approximation and find that, in the current low interest rate environment, a 5-factor parameterization developed by Björk and Christensen (1999) is best suited for accurately translating historical interest rate movements into plausible, current period shocks.

An accurate measure of market risk can help to inform institutions about the amount of capital needed to withstand a series of adverse market events. A plausible set of shocks is required to ensure market value, and cash flow projections are indicative of meaningful market sensitivities.

Using a discrete-time version of the arbitrage-free Nelson–Siegel (AFNS) term structure model, the authors examine how yield curves in the US and China react to exchange rate policy shocks as China introduces gradual reforms to make its exchange rate regime more flexible. The paper aims to discuss this issue.

The authors characterize the specification of the discrete-time AFNS model, prove the uniqueness of the solution for model identification, perform specification analysis on its canonical form and detail the MCMC estimation method with a fast and reliable prior extraction step.

Model decomposition reveals that in the US yield responses, changes in risk premia for medium- to long-term yields dominate changes in yield expectation for short- to medium-term yields, indicating that the portfolio rebalancing effect due to varying risk perception is stronger than the signaling effect due to policy rate expectation.

The results are helpful in diagnosing market sentiment and exchange rate risk pricing as China further internationalizes its currency.

The methodology can be easily extended to study yield curve responses to other scenarios of policy shocks or regime changes.

Refers to previous research on the yields of default‐free securities and uses the Nelson‐Siegel model for estimating yield curve as a basis for developing a model which decomposes the risk premium into long‐term risk, two factors influencing the rate of decay (curvature) and a feed back factor. Applies this to 1984‐1993 data for treasury bills to test for predictive validity and shows that the feedback factor (prediction error of the most recent period) improves this by around 10 per cent. Goes on to apply a multivariate exponential GARCH process to the components to produce a prediction model for the term structure of interest rates. Promises further research to refine this estimation and compare it with the expectations hypothesis as a basis for strategy.

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.

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.

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.

The yield curves of Australia and the USA reliably reflect investor’s expectations about prospective monetary policies in each economy.

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.

The purpose of the paper is to investigate the global and regional influences on the domestic term structure of nine Asian economies.

The dynamic Nelson Siegel model was used to extract the latent factors of a country’s yield curve movements in a state-space framework using the Kalman filter. The global and regional factors of the yield curve were extracted using the dynamic factor model. Further, the Bayesian inference of Gibbs sampling approach was used to identify the influence of global and regional factors on the domestic yield curve.

The results suggest that financial integration does not reduce the control of monetary authorities on the front end of the yield curve, and long-term interest rate is the potential transmission channel through which the contagion of the financial crisis spreads.

The results of this study would help the monetary authorities to understand the efficacy of the monetary policy transmission mechanism. It also offers the global investors diversification opportunities for investing in the Asian bond markets.

It is one of the earliest attempts to capture the global and regional yield curve movements and their impact on the emerging Asian economies yield curve. It contributes to literature by identifying the linkages in the long-term factor that is the potential channel through which crisis spreads.

Solvency-II is the current regulatory framework of insurance companies in the European Union. Under this standard, European Insurance and Occupational Pension Authority (EIOPA), as a regulatory board, has established that the Smith–Wilson (SW) model can be used as the model to estimate interest rate curve. This paper aims to analyze whether this model adjusts to the market curve better than Nelson–Siegel (NS) and whether the values set for the parameters are adequate.

This empirical study analyzes whether the SW interest rate curve shows lower root mean squared errors than the NS curve for a sample of daily prices of Spanish Government bonds between 2014 and 2019.

The results indicate that NS adjusts the market data better, the parameters recommended by the EIOPA correspond to the maximum values observed in the sample period and the current recommended curve for insurance companies underestimates company operations.

This paper verifies that the criterion of the last liquid point does not allow for selecting an optimal sample to adjust the curve and criteria based on prices without arbitrage opportunities are more appropriate.