Forecasting the Term Structure of South African Government Bond Yields

This article uses the parsimonious dynamic Nelson–Siegel model to fit the yields of South African government bonds. We find that the dynamic Nelson–Siegel model has good fitting abilities for all maturities. We further forecast the term structure by seven different dynamic Nelson–Siegel models with time series models. We find that the DNS–VAR–GARCH model is useful for forecasting the short-term rates, the DNS–VAR best predicts the medium-term rates, and the DNS–RW best predicts the long-term rates. In addition, the dynamic Nelson–Siegel models provide better forecasts of yield data than a random walk model, especially for the 12-month forecasting horizons.     

Do Bonds Span Volatility Risk in the U.S. Treasury Market? A Specification Test for Affine Term Structure Models

We propose using model‐free yield quadratic variation measures computed from intraday data as a tool for specification testing and selection of dynamic term structure models. We find that the yield curve fails to span realized yield volatility in the U.S. Treasury market, as the systematic volatility factors are largely unrelated to the cross‐section of yields. We conclude that a broad class of affine diffusive, quadratic Gaussian, and affine jump‐diffusive models cannot accommodate the observed yield volatility dynamics. Hence, the Treasury market per se is incomplete, as yield volatility risk cannot be hedged solely through Treasury securities.     

Understanding the term structure of credit default swap spreads

The shape of the term structure of credit default swap (CDS) spreads displays large variations over time and across firms. Consistent with the predictions of structural models of credit risk, we find that the slope of CDS spread term structure increases with firm leverage and volatility, but decreases with the level and the slope of the Treasury yield curve. However, these variables together have rather limited explanatory power for CDS slope and there is a significant common component in the regression residuals. In addition, we find that CDS slope predicts future changes in the CDS spreads, even after controlling for the contemporaneous variables that determine changes in the CDS spreads according to the structural models. Our results suggest that while structural models are qualitatively useful for understanding the shape of credit term structure, there are missing factors that importantly affect the term structure of CDS spreads.     

A VARIANCE DECOMPOSITION ANALYSIS OF THE INFORMATION IN THE TERM STRUCTURE

Based on a market efficiency assumption, we use variance decomposition analysis to separate information in the term structure on expected future spot rates from information on time-varying term premia and to examine the market's ability to forecast both future rate changes and excess returns on long versus short securities. We find that fluctuations in the slope of the yield curve are due more to changing term premia than to fluctuations in expected future spot rates and that the market correctly predicts about 40 percent of the month-to-month changes in spot rates, a considerably higher percentage than that found by previous studies.     

The forecasting efficiency of the dynamic Nelson Siegel model on credit default swaps

This paper extends the Diebold–Li dynamic Nelson Siegel model to a new asset class, credit default swaps (CDSs). The similarities between the term structure of CDSs and the term structure of interest rates allow CDS curves to be modelled successfully using a parsimonious three factor model as first proposed by Nelson and Siegel (1987). CDSs and yield curves are modelled using the Diebold and Li (2006) dynamic interpretation of the Nelson Siegel model where the three factors are representative of the level, slope and curvature of the curve. Our results show that the CDS curve fits the data well and allows for the various shapes exhibited by the CDS data including steep, inverted and downward sloping curves. In addition to in sample fit of the modelled curve we explore the out of sample forecasting abilities of the model and using a univariate autoregressive model we forecast 1, 5 and 10 days ahead. Our results show that although the one day ahead forecast under performs the random walk, the 5 and 10 day forecast consistently outperforms the random walk for both yields and CDSs. This study reaffirms the ability of the Diebold–Li (2006) methodology to forecast yields and provides new evidence that this methodology is efficacious when applied to CDS spreads.     

The term structure of inflation expectations

We use information in the term structure of survey-based forecasts of inflation to estimate a factor hidden in the nominal yield curve. We construct a model that accommodates forecasts over multiple horizons from multiple surveys and Treasury real and nominal yields by allowing for differences between risk-neutral, subjective, and objective probability measures. We establish that model-based inflation expectations are driven by inflation, output, and one latent factor. We find that this factor affects inflation expectations at all horizons but has almost no effect on the nominal yields; that is, the latent factor is hidden. We show that this hidden factor is not related to either current and past inflation or the standard set of macro variables studied in the literature. Consistent with the theoretical property of a hidden factor, our model outperforms a standard macro-finance model in its forecasting of inflation and yields.     

The fed and the term structure: Addressing simultaneity within a structural VAR model

This paper applies a new identification approach to estimate the contemporaneous relation between the term structure and monetary policy within a VAR framework. To achieve identification, we combine high-frequency Treasury futures and fed funds futures data with the VAR methodology. Results indicate that policy actions have a slope effect in the yield curve. We also find that the Fed responds to Treasury yields and that this response is stronger for the short and intermediate rates and less aggressive for long-yields. All estimated parameters are significant and robust to various model specifications.     

Can credit spreads help predict a yield curve?

In this paper we investigate whether information in credit spreads helps improve the forecasts of government bond yields. To do this, we propose and estimate a joint dynamic Nelson–Siegel (DNS) model of the U.S. Treasury yield curve and the credit spread curve. The model accounts for the possibility of regime changes in yield curve dynamics and incorporates a zero lower bound constraint on yields. We show that our joint model produces more accurate out-of-sample density forecasts of bond yields than does the yield-only DNS model. In addition, we demonstrate that incorporating regime changes and a zero lower bound constraint is essential for forecast improvements.     

Modelling the term structure of interest rates: An efficient nonparametric approach

We propose a new approach for estimating the coefficients of the term structure equation by means of the volatility of the interest rates and the slope of the yield curve. One advantage of this approach consists in the fact that the drift and the market price of risk are jointly estimated and need not be individually specified. We then generate trajectories in a test problem to investigate the finite properties of this approach. Our simulation results show that this new approach outperforms the classic nonparametric models in the literature. Finally, an application to USA Treasury Bill data is also illustrated.     

公开市场操作与利率期限结构行为——基于混频数据信息的研究视角

在混频数据信息环境中,精准识别公开市场操作(央行政策利率)和国债收益率曲线(基准利率体系)之间的关联机制至关重要,其影响了货币政策期限结构传导的有效性。本文在混频Nelson-Siegel(N-S)利率期限结构模型框架下,引入央行政策利率,揭示公开市场操作与利率期限结构(水平、斜率、曲度)因子之间的作用机制。实证结果表明:混频数据信息条件下,引入的公开市场操作信息显著改进国债收益率曲线的拟合效果;斜率因子冲击对公开市场操作具有显著的正向影响,而利率期限结构因子对政策调控的反应不敏感。进一步研究表明,2015年以来,公开市场操作对斜率因子的影响逐渐扩大,政策利率向国债收益率曲线的传导效率得到显著提高,我国现代货币政策框架日益健全。     

MODELING THE TERM STRUCTURE FROM THE ON‐THE‐RUN TREASURY YIELD CURVE

We propose a new model to estimate the term structure of interest rates using observed on‐the‐run Treasury yields. The new model is an improvement over models that require a priori knowledge of the shape of the yield curve to estimate the term structure. The general form of the model is an exponential function that depends on the estimation of four parameters fit by nonlinear least squares and has straightforward interpretations. In comparing the proposed model with current yield‐curve‐smoothing models, we find that, for the data used, the proposed model does best overall in terms of pricing accuracy both in sample and out of sample. JEL classification: E43, G12     

Term Structure Dynamics in Theory and Reality

This article is a critical survey of models designed for pricingfixed-income securities and their associated term structuresof market yields. Our primary focus is on the interplay betweenthe theoretical specification of dynamic term structure modelsand their empirical fit to historical changes in the shapesof yield curves. We begin by overviewing the dynamic term structuremodels that have been fit to treasury or swap yield curves andin which the risk factors follow diffusions, jump-diffusion,or have "switching regimes." Then the goodness-of-fit of thesemodels is assessed relative to their abilities to (i) matchlinear projections of changes in yields onto the slope of theyield curve; (ii) match the persistence of conditional volatilities,and the shapes of term structures of unconditional volatilities,of yields; and (iii) to reliably price caps, swaptions, andother fixed-income derivatives. For the case of defaultablesecurities we explore the relative fits to historical yieldspreads.     

Pricing the term structure with linear regressions

We show how to price the time series and cross section of the term structure of interest rates using a three-step linear regression approach. Our method allows computationally fast estimation of term structure models with a large number of pricing factors. We present specification tests favoring a model using five principal components of yields as factors. We demonstrate that this model outperforms the Cochrane and Piazzesi (2008) four-factor specification in out-of-sample exercises but generates similar in-sample term premium dynamics. Our regression approach can also incorporate unspanned factors and allows estimation of term structure models without observing a zero-coupon yield curve.     

An Unobserved Components Model of the Yield Curve

We develop an unobserved component model in which the short‐term interest rate is composed of a stochastic trend and a stationary cycle. Using the Nelson–Siegel model of the yield curve as inspiration, we estimate an extremely parsimonious state‐space model of interest rates across time and maturity. The time‐series process suggests a specific functional form for the yield curve. We use the Kalman filter to estimate the time‐series process jointly with observed yield curves, greatly improving empirical identification. Our stochastic process generates a three‐factor model for the term structure. At the estimated parameters, trend and slope factors matter while the third factor is empirically unimportant. Our baseline model fits the yield curve well. Model generated estimates of uncertainty are positively correlated with estimated term premia. An extension of the model with regime switching identifies a high‐variance regime and a low‐variance regime, where the high‐variance regime occurs rarely after the mid‐1980s. The term premium is higher, and more so for yields of short maturities, in the high‐variance regime than in the low‐variance regime. The estimation results support our model as a simple and yet reliable framework for modeling the term structure.     

A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables

We describe the joint dynamics of bond yields and macroeconomic variables in a Vector Autoregression, where identifying restrictions are based on the absence of arbitrage. Using a term structure model with inflation and economic growth factors, together with latent variables, we investigate how macro variables affect bond prices and the dynamics of the yield curve. We find that the forecasting performance of a VAR improves when no-arbitrage restrictions are imposed and that models with macro factors forecast better than models with only unobservable factors. Variance decompositions show that macro factors explain up to 85% of the variation in bond yields. Macro factors primarily explain movements at the short end and middle of the yield curve while unobservable factors still account for most of the movement at the long end of the yield curve.     

Analyzing interest rate risk: Stochastic volatility in the term structure of government bond yields

We propose a Nelson–Siegel type interest rate term structure model where the underlying yield factors follow autoregressive processes with stochastic volatility. The factor volatilities parsimoniously capture risk inherent to the term structure and are associated with the time-varying uncertainty of the yield curve’s level, slope and curvature. Estimating the model based on US government bond yields applying Markov chain Monte Carlo techniques we find that the factor volatilities follow highly persistent processes. We show that yield factors and factor volatilities are closely related to macroeconomic state variables as well as the conditional variances thereof.     

The great moderation of the term structure of UK interest rates

The conduct of monetary policy, the term structure of interest rates and the structure of the economy in the UK have changed over the post-WWII period. We model the interaction between the macroeconomy and financial markets using a time-varying VAR augmented with the factors from the yield curve. There is evidence of a great moderation in the dynamics of the yield curve, with the factors being persistent and volatile before the introduction of inflation targeting in 1992 but becoming stable afterwards. The introduction of time-variation in the Factor Augmented VAR improves the fit of the model and results in expectation hypothesis consistent yields that are close to actual yields, even at long maturities. Monetary policy shocks had a significant impact on the volatility of inflation, output and the policy rate over the pre-inflation targeting era, but their contribution has been negligible under the current regime. Shocks to the level of the yield curve accounted for a large fraction of inflation variability only before 1992.     

人口学特征与利率期限结构:老年社会平缓的收益率曲线

本文将刻画人口结构的生命周期模型引入消费-资本资产定价模型,考察人口结构对利率期限结构的影响。模型表明,人口结构及其家庭生命周期特征不仅决定利率水平,而且将通过人对债券期限的不同偏好,影响利率期限结构。少年人口占比对利率期限结构的影响为正,中年和老年人口占比的影响为负。相比少年人口,中老年人口更偏好长期债券,使长期收益率下降,期限结构的斜率更为平缓。基于全球数据的经验研究验证了这一结论。少年人口占比增加期限利差,中老年人口占比则起反向作用。因此,在年长的经济体中,期限利差更小,呈现更平缓的收益率曲线特征。在更换人口结构变量、期限利差变量、估计方法、赋权样本和处理遗漏变量后,结果表现稳健。本文从人口学视角拓宽了利率期限结构的决定因素,揭示了老年经济体可能面临一个平缓的收益率曲线,而这说明老龄化还可能通过抑制短期投机和促进长期投资来提高长期经济发展质量。     

Risk Premiums in Dynamic Term Structure Models with Unspanned Macro Risks

This paper quantifies how variation in economic activity and inflation in the United States influences the market prices of level, slope, and curvature risks in Treasury markets. We develop a novel arbitrage‐free dynamic term structure model in which bond investment decisions are influenced by output and inflation risks that are unspanned by (imperfectly correlated with) information about the shape of the yield curve. Our model reveals that, between 1985 and 2007, these risks accounted for a large portion of the variation in forward terms premiums, and there was pronounced cyclical variation in the market prices of level and slope risks.