scholarly journals Recovering Yield Curves from Dynamic Term Structure Models with Time-Varying Factors

Stats ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 284-329
Author(s):  
Hiroyuki Kawakatsu
Keyword(s):  
Term Structure ◽  
Linear Interpolation ◽  
Time Varying ◽  
Structure Model ◽  
Yield Data ◽  
Cross Sectional ◽  
Term Structure Model ◽  
Term Structure Models ◽  
Out Of Sample ◽  
Dynamic Version

A dynamic version of the Nelson-Siegel-Svensson term structure model with time-varying factors is considered for predicting out-of-sample maturity yields. Simple linear interpolation cannot be applied to recover yields at the very short- and long- end of the term structure where data are often missing. This motivates the use of dynamic parametric term structure models that exploit both time series and cross-sectional variation in yield data to predict missing data at the extreme ends of the term structure. Although the dynamic Nelson–Siegel–Svensson model is weakly identified when the two decay factors become close to each other, their predictions may be more accurate than those from more restricted models depending on data and maturity.

Term spread regressions of the rational expectations hypothesis of the term structure allowing for risk premium effects

2015 ◽  
Vol 19 (1) ◽  
Author(s):  
Efthymios Argyropoulos ◽  
Elias Tzavalis
Keyword(s):  
Interest Rates ◽  
Term Structure ◽  
Rational Expectations ◽  
Time Varying ◽  
Structure Model ◽  
Expectations Hypothesis ◽  
Term Structure Model ◽  
Term Spread ◽  
Term Premium

AbstractThis paper suggests a new empirical methodology of testing the predictions of the term spread between long and short-term interest rates about future changes of the former allowing for term premium effects, according to the rational expectations hypothesis of the term structure. To capture the effects of a time-varying term premium on the term spread, the paper relies on an empirically attractive affine Gaussian dynamic term structure model which assumes that the term structure of interest rates is spanned by three unobserved state variables. To retrieve accurate values of these variables from interest rates series, the paper suggests a new method which can overcome the effects of measurement (or pricing) errors inherent in these series on the estimates of the model. This method is assessed by a Monte Carlo study. Ignoring these errors will lead to biased estimates of term structure models. The empirical results of the paper provide support for the suggested term structure model. They show that this model can efficiently capture the time-varying term premium effects embodied in long-term interest rates, which can explain the failures of term spread to forecast future changes in long-term rates.

Estimating and Forecasting the Term Structure of Korea Markets Using the Nelson-Siegel Model

2004 ◽  
Vol 12 (2) ◽  
pp. 101-126
Author(s):  
Joon Haeng Lee
Keyword(s):  
Term Structure ◽  
Yield Curve ◽  
Principal Component ◽  
Component Model ◽  
Structure Model ◽  
Sample Period ◽  
Term Structure Model ◽  
Long Run ◽  
Out Of Sample ◽  
Principal Component Model

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.

A Note on the Relation Between Principal Components and Dynamic Factors in Affine Term Structure Models

2005 ◽  
Vol 25 (1) ◽  
pp. 89 ◽  
Author(s):  
Caio Ibsen Rodrigues de Almeida
Keyword(s):  
Term Structure ◽  
Principal Components ◽  
Principal Component ◽  
Estimation Procedure ◽  
Structure Model ◽  
Affine Term Structure ◽  
Term Structure Model ◽  
Affine Term Structure Models ◽  
Term Structure Models ◽  
Dynamic Factors

In econometric applications of the term structure, affine models are among the most used ones. Nevertheless, even presenting a closed form characteristic function, its estimation procedure still presents many points to be understood and difficulties to be removed. In this note, we address one of these points. Suppose we estimate an affine dynamic term structure model, and also apply principal component analysis to the interest rate database available. A very plausible question would inquire about the relation (if any) between the principal components obtained assuming no dynamic restrictions, and the dynamic factors estimated using the proposed term structure model. We answer this question when estimating a standard affine model using zero coupon data. We show that each principal component can be approximated by a linear transformation of the dynamic factors. Although simple, this is an important step to the understanding of the mechanics of dynamic affine term structure models. A numerical example using U.S. zero data illustrates the result

A FILTERING APPROACH TO PRICING IN MULTIFACTOR TERM STRUCTURE MODELS

2001 ◽  
Vol 04 (02) ◽  
pp. 303-320 ◽  
Author(s):  
ANDREA GOMBANI ◽  
WOLFGANG J. RUNGGALDIER
Keyword(s):  
Term Structure ◽  
Structure Model ◽  
Latent Factors ◽  
Term Structure Model ◽  
The Real ◽  
Stochastic Filtering ◽  
Term Structure Models ◽  
Basic Model ◽  
Real Market ◽  
Filtering Approach

We present an approach for the pricing of illiquid bonds (and bond derivatives) in an arbitrage-free way and which is consistent with the observed prices of liquid bonds. The basic model is a multifactor term structure model with abstract latent factors. The approach is based on stochastic filtering techniques, leading to a continuous update of the distribution of the latent factors on the basis of the information coming from the observations. This allows our model to continuously "track" the real market.

scholarly journals A Segmented and Observable Yield Curve for Colombia

2021 ◽  
Vol 10 (2) ◽  
pp. 179-200
Author(s):  
Carlos Castro-Iragorri ◽  
Juan Felipe Peña ◽  
Cristhian Rodríguez
Keyword(s):  
Term Structure ◽  
Yield Curve ◽  
Structure Model ◽  
Short Term ◽  
Bond Prices ◽  
Term Structure Model ◽  
Model Based ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Three Factor

Abstract Following (Almeida, Ardison, Kubudi, Simonsen, & Vicente, 2018) we implement a segmented three factor Nelson-Siegel model for the yield curve using daily observable bond prices and short term interbank rates for Colombia. The flexible estimation for each segment (short, medium, and long) provides an improvement over the classical Nelson-Siegel approach in particular in terms of in-sample and out-of-sample forecasting performance. A segmented term structure model based on observable bond prices provides a tool closer to the needs of practitioners in terms of reproducing the market quotes and allowing for independent local shocks in the different segments of the curve.

scholarly journals Zero-Coupon Yields and the Cross-Section of Bond Prices

2021 ◽  
Author(s):  
N Aaron Pancost
Keyword(s):  
Term Structure ◽  
Yield Curve ◽  
Structure Model ◽  
Bond Prices ◽  
Cross Sectional ◽  
Term Structure Model ◽  
No Arbitrage ◽  
Affine Model ◽  
The Cross ◽  
Coupon Bond

Abstract I estimate a dynamic term structure model on an unbalanced panel of Treasury coupon bonds, without relying on an interpolated zero-coupon yield curve. A linearity-generating model, which separates the parameters that govern the cross-sectional and time-series moments of the model, takes about 8 min to estimate on a sample of over 1 million bond prices. The traditional exponential affine model takes about 2 hr, because of a convexity term in coupon-bond prices that cannot be concentrated out of the cross-sectional likelihood. I quantify the on-the-run premium and a “notes versus bonds” premium from 1990 to 2017 in a single, easy-to-estimate no-arbitrage model.

Sign in / Sign up

Export Citation Format

Share Document