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Monetary policy shocks affect interest rates at long horizons (10 years or more). Furthermore, the private sectorâs real GDP forecasts are revised upward in response to a monetary tightening. These facts challenge the prevailing theories in academic and policy circles. In this paper, I propose a micro-founded model to rationalize those facts, based on the signaling channel of monetary policy. I consider a framework where the central bank has private information about future economic conditions. Agents update their beliefs according to Bayes’ theorem. Policy actions play a signaling role, and may therefore rationalize the above empirical findings.

This paper presents a new asset pricing model incorporating fundamental uncertainties by choice of a probability measure. This approach is novel in that we incorporate uncertainties on Brownian motions describing risks into the existing asset pricing model. Particularly, we show extensions of interest rate models to the ones with uncertainties on the Brownian motions, which make the yield curve reflect not only economic factors but also views of the market participants on the Brownian motions. Such yield curve models are especially important in yield curve trading of hedge funds as well as monetary policy making of central banks under low interest rate environments observed after the global financial crisis, in which yield curves are less affected by economic factors since they are controlled by the central banks, but are driven mainly by sentiments of market participants. Firstly, to model aggressive (positive)/conservative (cautious) attitudes towards such fundamental uncertainties, we consider a sup-inf/inf-sup problem on the utility of a representative agent with respect to uncertainties over Brownian motions, i.e. fundamental market risks, by choice of a probability measure. Secondly, we show that the problem is solved via a backward-stochastic differential equations (BSDEs) approach. Then, under a probability measure determined by solving the sup-inf/inf-sup problem, we propose interest rate models with those uncertainties and explicitly obtain their term structures of interest rates. Particularly, we present two approaches to solving the relevant coupled forward-backward stochastic differential equations (FBSDEs) to obtain expressions of the equilibrium interest rate and the term structure of interest rates. In detail, the first approach is by comparison theorems, and the second approach is to predetermine the signs of the volatilities of the BSDE in the coupled system and confirm them by explicitly solving the separated BSDE. Finally, we present concrete examples with numerical experiments.

Monetary policy moves the yield curve. What is the economic interpretation of such moves and what are their macroeconomic consequences? Applying an affine term structure model to high-frequency yield curve movements around FOMC announcements, we shed new light on these questions. Estimation is subject to restrictions addressing estimation bias in previous studies. By imposing additional structure, expectations and term premia are decomposed into three components interpreted as monetary policy action, expected path and its uncertainty. In a local projections model, the shocks identified by the three components provide insights into monetary policy transmission in the context of existing theories.

This paper investigates spillovers between the latent factors of the term structure of the interest rates of the United States and selected South American countries. We show how interest rates of different maturities of these countries interact. The term structure of the interest rate is estimated using the Dynamic Nelson-Siegel model, extracted through the Kalman filter. The main contribution of this article is the use of these spillover indices for stock markets developed by Diebold and Yilmaz (2009, 2012) for the relation between term structures of different countries. The results show a high degree of spillover in the level (22.79%), slope (25.53%), and curvature (15.18%) factors, with high oscillation of transmission intensities over time. There is a substantial increase in the spillover index during financial crises and other high-volatility periods. The results are important because they show the existence and intensity of international monetary spillovers, serving as a framework for the calibration of models used in monetary policy, as they are sensitive to international shocks. Moreover, changes in the interest rate levels may have impact on sectors that are sensitive to long-term rates and global bond markets.

The purpose of this study is to verify the ability of the German treasury yield curve and its components of expectations and term premium to predict recessions in Germany. The German yield curve slope can predict recessions in Germany but with a worse accuracy than the US yield curve predicts recessions in the US. The predictive power of the German yield curve is driven by expectations and reinforced by signals from the term premium component. However, an analysis of the shrunk sub-sample until 1996 shows that the German yield curve’s ability to predict recessions has deteriorated in recent decades. Also, the significance of control variables representing the stance of monetary policy has weakened in recent decades, although they were strong and important predictors of the recession in Germany until 1996. As policy interest rate fell to the effective lower bound, the central bank resorted to other instruments to ease credit conditions, and consequently, policy rate (and its difference from the neutral interest rate) lost some of its meaning as a gauge of monetary accommodation. Orders in manufacturing markedly improve the recession forecast, although they are not early indicators of an impending recession. Although the US yield curve slope may to some extent predict recessions in Germany, its significance completely disappears in the presence of the German yield curve. The predictive power of the US yield curve was greater before 1996 than in the full sample horizon.

In this paper, we propose a new model to address the problem of negative interest rates that preserves the analytical tractability of the original Cox–Ingersoll–Ross (CIR) model without introducing a shift to the market interest rates, because it is defined as the difference of two independent CIR processes. The strength of our model lies within the fact that it is very simple and can be calibrated to the market zero yield curve using an analytical formula. We run several numerical experiments at two different dates, once with a partially sub-zero interest rate and once with a fully negative interest rate. In both cases, we obtain good results in the sense that the model reproduces the market term structures very well. We then simulate the model using the Euler–Maruyama scheme and examine the mean, variance and distribution of the model. The latter agrees with the skewness and fat tail seen in the original CIR model. In addition, we compare the model’s zero coupon prices with market prices at different future points in time. Finally, we test the market consistency of the model by evaluating swaptions with different tenors and maturities.

We solve a dynamic equilibrium model with generalized disappointment-aversion preferences and continuous state-endowment dynamics. We apply the framework to the term structure of interest rates and show that the model generates an upward-sloping term structure of nominal interest rates and a downward-sloping term structure of real interest rates and that it accounts for the failure of the expectations hypothesis. The key ingredients are preferences with disappointment aversion, preference for early resolution of uncertainty, and an endowment economy with three state variables: time-varying macroeconomic uncertainty, time-varying expected inflation, and inflation uncertainty. This paper was accepted by Karl Diether, finance.

The term structure of interest rates is crucial for the transmission of monetary policy to financial markets and the macroeconomy. Disentangling the impact of monetary policy on the components of interest rates, expected short rates, and term premia is essential to understanding this channel. To accomplish this, we provide a quantitative structural model with endogenous, time-varying term premia that are consistent with empirical findings. News about future policy, in contrast to unexpected policy shocks, has quantitatively significant effects on term premia along the entire term structure. This provides a plausible explanation for partly contradictory estimates in the empirical literature.

This article examines the forecasting performance of continuous-time multi-factor models, in comparison with other parsimonious models, for the term structure of interbank rates in the UK, Europe, and Japan. The article employs two general dynamic frameworks with different factor structures: the generalized Chan-Karolyi-Longstaff-Sanders family of models and the arbitrage-free dynamic Nelson-Siegel family of models. Applying a battery of accuracy measures and a range of formal tests of forecasting superiority, this research provides evidence that extended multi-factor models demonstrate good out-of-sample forecasting performance for the short segment of the yield curve. However, for the euro and in part for the yen, random walk forecasts consistently pass various tests, indicating a higher level of market efficiency compared to the pound sterling interbank market. TOPICS:Factor-based models, currency, developed markets, performance measurement Key Findings ▪ For the term structure of interbank rates in the UK, Europe, and Japan, more complex continuous-time models that include more factors are superior in terms of predictive power to models with less factors or discrete-time models. ▪ Based on a battery of accuracy tests and a range of formal tests, extended multi-factor models demonstrate good out-of-sample forecasting performance for the short segment of the yield curve. ▪ For the euro and in part for the yen, random walk forecasts consistently pass various tests, indicating a higher level of market efficiency compared to the pound sterling interbank market.

This paper estimates the interest rate term structures of Treasury and individual corporate bonds using a robust criterion. The Treasury term structure is estimated with Bayesian regression splines based on nonlinear least absolute deviation. The number and locations of the knots in the regression splines are adaptively chosen using the reversible jump Markov chain Monte Carlo method. Due to the small sample size, the individual corporate term structure is estimated by adding a positive parametric credit spread to the estimated Treasury term structure using a Bayesian approach. We present a case study of U.S. Treasury STRIPS (Separate Trading of Registered Interest and Principal of Securities) and ATT Jarrow, Ruppert, and Yu 2004). Schwartz (1998) uses a robust measure and finds that almost 10% of the US Treasury securities in the Fixed Income database are outliers. This paper considers term structure estimation of Trea- sury and corporate bonds using a robust approach. We extend the normal linear Bayesian regression splines model of Denison, Mallick, and Smith (1998) to the nonlinear term structure model based on the least absolute deviation criterion. The term structure of interest rates of the bonds describes how interest rates evolve over time. The interest rate term structures of both government and cor- porate bonds have important applications in economics and finance, as described in detail in Li and Yu (2005). Obtaining accurate estimates of both Treasury and corporate term structures is essential.

The main objective in this article is to shed new light on the term structure of subjective time preference rates using a conditional Consumption Capital Asset Pricing Model. Following Samuelson’s (1937)’s suggestion, we analyze the concept of “time consistency”. More precisely, we challenge the relevance of the exponential time discounting function assumption, which leads to a constant subjective time preference rate. First, we develop a parsimonious, consumption-based model of the term structure of interest rates. Second, we test its implications for US monthly data from 1970:4 to 2013:1. We use a bivariate two-factor model of inflation and real consumption (through a VAR-GARCH process) to condition the term premiums of bonds. Our results clearly cast doubt on the assumption of a flat term structure, as implied by the standard exponential discounting function. A decreasing term structure of time preference rates is reported. It is particularly clear for the 1991-2013 period. Our results give strong support for the hyperbolic time discounting function hypothesis and open the way for the hypothesis of time varying time preference rates.

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.

Based on an empirically attractive Gaussian dynamic term structure model and data on the short-term real interest rate and per capita real consumption, this paper examines the information content of the nominal term structure of interest rates with respect to inflation, real interest rates and risk premium effects. The paper finds a number of interesting results, which have monetary policy implications. First, it shows that the suggested model fits well the data and provides estimates of real interest rates and expected inflation rates which are very close to those given by survey data. Second, it provides clear cut evidence that the nominal term structure is mainly determined by movements in real interest rates and, to a lesser extent, by inflation and risk premium effects. These results mean that term spreads between long and short-term nominal rates fail to forecast future changes in inflation rates because are dominated by real term structure effects, which are not orthogonal to inflation rate shocks (changes). Third, to retrieve information from the term spread models of nominal interest rates about future inflation rate changes, the paper suggests a regression model which adjusts the nominal interest rates term spreads for the real term structure effects.

We study the forecasting power of economic uncertainty about government policy for future bond returns. Using the economic policy uncertainty measure (EPU) developed by Baker, Bloom, and Davis (2016), we investigate its relationship to expected bond returns. The impact of the EPU is shown to be large for earlier maturities at shorter investment horizons. Estimating an affine term structure model incorporating the EPU, we show that term premia estimates from this model with this additional pricing factor exhibit higher fluctuations and move closely with the variations in observed yields. The implied term premia show strong countercyclical movements, hence better explaining higher risk compensation under adverse economic conditions as expected by theory.

This study tries to verify the predictive power of the implicit forward rate of the term structure of interest rates in Africa. We used data from Egypt, Ghana, Kenya, Nigeria and the Republic of South Africa. A modified version of the yield term premium and the forward term premium models of Shiller and McCulloch (1990) were used to test the predictive power of the implicit forward rate, rather than the rational expectations hypothesis. We both used FMOLS and DOLS estimators, since they are more consistent than OLS with non-stationary series. The overall results show that the implicit forward rate does not have a significant predictive power in Africa. It therefore appears that operators on African markets should not rely on those predictions.

This paper examines the recession probability in the Eurozone within the next 12 months at the zero lower bound (ZLB) and explores two new perspectives: a revised measure of the traditional term spread and a modification to detect unstable dynamics driven by animal spirits. We find that the yield curve largely lost its forecasting ability at the ZLB. To remove the downward rigidity of short‐term rates, we suggest a modified version of the term spread which uses a “shadow policy rate,” rather than the 3‐month rate, as the front leg of the spread. We further show that a bivariate specification including both the current state of an indicator as well as its lagged deviation from its trend augments the predictive capability for most indicators significantly.

The components of term structure of interest rate are an important element of the asset pricing models. This article studies the connectedness of the component of the sovereign yield curve across e...

This paper provides cross-country analysis of local bond market term premia in emerging countries. In order to investigate the role of domestic and global factors in the determination of compensation demanded by investors for their medium and long term fixed income investments, term premia is computed for emerging countries by using methodology adopted in Adrian et al. (2013) . It is found that changes in market liquidity conditions is important for the variation in term premia. Moreover, movements in domestic and global factors are closely linked to term premia. In this regard, uncertainty related economic surprise indicator and exchange rate related expectations subsume some part of the expected excess returns in both medium and long term. Among other explanatory variables, inflation uncertainty is the only variable found to be insignificant in medium term, albeit it has an explaining power in the long term.

There is strong evidence in the literature for the hypothesis that interest rates and the market risk premium are not constant during the business cycle. The beta risk of firms in the insurance industry is also time-varying. The major implication of these results is that discount rates for risky cash flows are time varying and must obey a term structure similar to the term structure of interest rates. The purpose of this paper is to estimate discount rates for cash flows with different time horizons for the U.S. insurance industry and for different insurance sectors. We find that the term structure cost of capital takes on different shapes depending on the business cycle. It is therefore meaningful for insurers to evaluate risky projects by selecting a discount rate most appropriate for the nature and the time horizon of each project.

This chapter introduces the role of intertemporal expectations into our analysis. Effects of current and expected inflation on long-term interest rates will be the focus of the first half of this chapter. This will be followed by a discussion of the ultimate macroeconomic meltdown—hyperinflation.

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&ndash, Siegel&ndash, 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.

This paper expands the neural network model to predict exchange rate based on the factors extracted from the Nelson–Siegel model. Based on the theory about exchange rate forecasting, interest could be used to predict the movement of exchange rate. Therefore, this paper analyzes the interest rate term structure factors based on the US and China yield curves data, then uses the Nelson–Siegel model to extract the factors of the interest rate term structure. Finally, the factors of yield curves are used as input data to of the neural network model. And the mean forecasting squared errors, mean absolute errors, mean absolute percentage errors of neural network model, Nelson–Siegel regression model, and ARIMA model are compared. The results show that the neural network model has a superior ability to explain the exchange rate fluctuations of the CNY and USD, and the prediction ability is better than the exchange rate prediction ability of the Nelson–Siegel regression model and ARIMA model.

The authors present a new affine model that can predict future yields and risk premia in the monetary conditions of the past decade more convincingly than current state-of-the-art statistical models. Despite making use of very different sources of information, it produces remarkably similar changes in risk premia as the most popular statistical return-predicting factors. However, it predicts very different—and, they argue, more believable—levels for risk premia and expectations. The model is extremely parsimonious, is financially motivated, fits market yields accurately with very few interpretable parameters, and naturally recovers important qualitative features of the joint ℙ and ℚ dynamics of yields. TOPICS:Analysis of individual factors/risk premia, factor-based models, statistical methods Key Findings • A new affine model of the term structure is shown to give more plausible estimates of risk premia and expectations than the current state-of-the-art yield curve statistical models. • The model uses information from the Fed expectations of the future federal funds rate. • The model is financially justifiable, very parsimonious, and fits observed market yields very well.

Macro-finance theory implies that trend inflation and the equilibrium real interest rate are fundamental determinants of the yield curve. However, empirical models of the term structure of interest rates generally assume that these fundamentals are constant. We show that accounting for time variation in these underlying long-run trends is crucial for understanding the dynamics of Treasury yields and predicting excess bond returns. We introduce a new arbitrage-free model that captures the key role that long-run trends play in determining interest rates. The model also provides new, more plausible estimates of the term premium and accurate out-of-sample yield forecasts. (JEL E31, E43, E47)

We examined the dynamic linkages among money market interest rates in the so-called &ldquo, BRICS&rdquo, countries (Brazil, Russia, India, China, and South Africa) by using weekly data of the overnight, one-, three-, and six- months, as well as of one year, Treasury bills rates covering the period from January 2005 to August 2019. A long-run relationship among interest rates was established by employing the Vector Error Correction modeling (VECM), which revealed the validation of the Expectation Hypothesis Theory (EH) of the term structure of interest rates, taking into account long-run deviations from equilibrium and inherent nonlinearities. We unveiled short-run dynamic adjustments for the term structure of the BRICS, subject to regime switches. We then used Markov Switching Vector Error Correction models (MS-VECM) to forecast them dynamically during an out-of-sample period of May 2016 through August 2019. The MSIH-VECM forecasts were found to be superior to the VECM approaches. The novelty of our paper is mainly due to the exploration of the possibility of parameter instability as a crucial factor, which might explain the rejection of the restricted version of the cointegration space, and on the dynamic out-of-sample forecasts of the term structure over a more recent time span in order to assess further the usefulness of our nonlinear MS-VECM characterization of the term structure, capturing the effects of the global and domestic financial crisis.

China’s bonds market has developed rapidly in recent years. A further study of interest rate term structure is essential. Nelson-Siegel model is widely used to fit interest rate term structure around the world. In this essay, we try to find out whether Nelson-Siegel model is efficiency in China, and which model is most efficient among some typical variants of Nelson-Siegel model. After brief theoretical introduction, we conduct empirical analysis, which contains two sections. In the first session, we focus on fitting Chinese interest rate term structure using Nelson-Siegel model, and fitting efficiency turns out to be pretty good. In the second section, we establish a VAR model with macroeconomic variables to predict parameters in Nelson-Siegel model, and use the combination of VAR and NS model to predict interest rate term structure in 2019 and 2020 respectively. Also, in terms of prediction efficiency, VAR (Macro)-NS model performs better than both VAR-NS model without macroeconomic variables and simple NS model.

Linear estimators of the affine term structure model are inconsistent since they cannot reproduce the factors used in estimation. This is a serious handicap empirically, giving a worse fit than the conventional ML estimator that ensures consistency. We show that a simple self-consistent estimator can be constructed using the eigenvalue decomposition of a regression estimator. The remaining parameters of the model follow analytically. Estimates from this model are virtually indistinguishable from that of the ML estimator. We apply the method to estimate various models of U.S. Treasury yields. These exercises greatly extend the range of models that can be estimated.

Agents can learn from financial markets to predict macroeconomic outcomes, and learning dynamics can feed back into both the macroeconomy and financial markets. This paper builds on the adaptive learning (AL) model of [Slobodyan, S. and R. Wouters (2012a) American Economic Journal: Macroeconomics 4, 65–101.] by introducing the term structure of interest rates. This extension enables term structure information to fully characterize agents’ expectations in real time. This feature addresses an imperfect information issue neglected in the related AL literature. The term structure of interest rates results in a strong channel of persistence driven by multi-period forecasting. Including the term structure in the AL model results in a model fit similar to that obtained in the rational expectation (RE) version of the model, but it greatly reduces the importance of other endogenous sources of aggregate persistence such as price and wage stickiness and the elasticity of the cost of adjusting capital. The model estimated also shows that term premium innovations are a major source of persistent fluctuations in nominal variables under AL. This stands in sharp contrast to the lack of transmission of term premium shocks to the macroeconomy under REs.

Purpose This paper aims to illustrate how a Bayesian approach to yield fitting can be implemented in a non-parametric framework with automatic smoothing inferred from the data. It also briefly illustrates the advantages of such an approach using real data. Design/methodology/approach The paper uses an infinite dimensional (functional space) approach to inverse problems. Numerical computations are carried out using a Markov Chain Monte-Carlo algorithm with several tweaks to ensure good performance. The model explicitly uses bid-ask spreads to allow for observation errors and provides automatic smoothing based on them. Findings A non-parametric framework allows to capture complex shapes of zero-coupon yield curves typical for emerging markets. Bayesian approach allows to assess the precision of estimates, which is crucial for some applications. Examples of estimation results are reported for three different bond markets: liquid (German), medium liquidity (Chinese) and illiquid (Russian). Practical implications The result shows that infinite-dimensional Bayesian approach to term structure estimation is feasible. Market practitioners could use this approach to gain more insight into interest rates term structure. For example, they could now be able to complement their non-parametric term structure estimates with Bayesian confidence intervals, which would allow them to assess statistical significance of their results. Originality/value The model does not require parameter tuning during estimation. It has its own parameters, but they are to be selected during model setup.

This paper adopts a novel approach to studying the evolution of interest rate term structure over the U.S. business cycles and to predicting recessions. Applying an effective algorithm, I classify the Treasury yield curve into distinct shapes and find the less frequent shapes intrinsically linked to the recessions in the post-WWII data. In forecasting recessions, the median-short yield spread trumps the long-short spread for horizons up to 17 months ahead and the yield curve shape is nearly impressive as the median-short spread. Overall, the yield curve shape is an informative but more succinct indicator than the spreads in studying the term structure. Key words: Business cycle, recession forecast, U.S. Treasury yield curve, yield spreads.

Este trabajo analiza si existe una relación entre la pendiente de la curva de rendimientos y la actividad económica futura en México para el período 2004-2019. En particular, se evalúa si dicha relación depende de la prima de plazo. Para este propósito, se estima un modelo de umbral en el cual la relación entre la curva de rendimientos y la actividad económica futura, medida ya sea como el crecimiento del producto o como la probabilidad de una contracción, depende de si la prima por plazo se encuentra por encima o por debajo de un umbral estimado. Los resultados principales indican que la pendiente de la curva de rendimientos parece anticipar el comportamiento de la actividad económica solo cuando la prima por plazo se encuentra por encima de un umbral. Los resultados también sugieren que la pendiente de la curva de rendimientos tiene poder predictivo sobre la probabilidad de enfrentar una contracción en el futuro solo cuando la prima por plazo está por encima de un umbral. El valor estimado para dicho umbral depende del horizonte de pronóstico y la medida de la actividad económica.

El trabajo proporciona una descripción general de los modelos de estructuras de plazos de las tasas de interés. Se trata de un planteamiento técnico de la teoría del comportamiento libre de arbitraje de tasas de interés de distintos vencimientos. Los modelos de tasa corta están ganando relevancia en la actualidad por su capacidad para describir y explicar la existencia de tasas de interés negativas como se ha observado en Europa y Asia. Las condiciones económicas actuales, en un entorno de incertidumbre generado por una recesión económica global, afectan el comportamiento de las tasas de interés, lo cual invita a realizar una revisión más cuidadosa de los factores que influyen en la dinámica de las mismas. Este artículo tiene como objetivo revisar las tendencias y perspectivas de los modelos de estructuras de plazos y destaca algunas áreas para futuras investigaciones.

The distribution of term premia includes additional information beyond that contained in the traditional three-parameter decomposition of the Treasury yield curve. The author finds that the position and the size of the curvature hump are both important. A yield curve hump positioned at longer maturities appears to be consistent with lengthier time horizons of investors and with more risk-seeking behavior. The author finds that this new parameter Granger drives both future US economic activity and bond returns. TOPICS:Fixed income and structured finance, fixed-income portfolio management, statistical methods

At the zero lower bound, the dynamic Nelson–Siegel (DNS) model and even the Svensson generalization of the model have trouble in fitting the short maturity yields and fail to grasp the characteristics of the Japanese government bonds (JGBs) yield curve. During the zero interest rate policy regime, the short end of the yield curve is flat and yields corresponding to various maturities have asymmetric movements. Therefore, closely related generalized versions of Nelson–Siegel model—with and without no-arbitrage restriction (GAFNS and GDNS)—that have two slopes and curvatures factors are considered and compared empirically in terms of in-sample fit as well as out-of-sample forecasts with the standard Nelson–Siegel model—with and without no-arbitrage restriction (AFNS and DNS). The affine-based models provide a more attractive fit of the yield curve than their counterpart DNS-based models. Both extended models are capable to restrict the estimated rates from becoming negative at the short end of the curve and distill the JGBs term structure of interest rate quite well. The affine-based extended model leads to a better in-sample fit than the simple GDNS model. In terms of out-of-sample accuracy, both non-affine models outperform the affine models at least for 1- and 6-month horizons. The out-of-sample predictability of the GDNS for the 1- and 6-month-ahead forecasts is superior to the GAFNS for all maturities, and for longer horizons, i.e., 12-month-ahead, the former is still compatible to the latter, particularly for short- and medium-term maturities.

This paper contributes to the literature on the relationship between the yield curve, macroeconomic variables, and the unexplored interactions with tae U.S. yield curve movements by focusing on an emerging market: Brazil. We incorporate factors for the U.S. yield curve and domestic macroeconomic variables into the Dynamic Nelson Siegel Model to explore comovements with the Brazilian yield curve. As noted here, foreign macroeconomic factors contain a lot of information about the domestic term structure of yields. The empirical results suggest that both American and macroeconomic components may explain the latent factors of the term structure; in particular, the U.S. factors influence the Brazilian yield curve, since almost half of the variance in the level factor was caused by movements in the U.S. curve. Furthermore, we find evidence that a specification with U.S. yield factors is better for short maturities and long forecasts horizons.

The effect of forward guidance on interest rate expectations in small, open economies is often described as heterogeneous. There are examples when financial markets adjusted term structure to reflect interest rate forecasts provided in the projections published by the central banks. On the other hand, medium-term expectations can persistently deviate from trajectories presented by decision-makers, influenced by foreign monetary policy. Our aim is to find the maximal forecast horizon where the domestic forward guidance of local banks in European economies affects market interest rate expectations strongly as compared to the ECB policy. We analyzed the term structure of interest rates in Sweden, Norway, and the Czech Republic. Central banks in these three economies provide the most mature forward guidance, e.g., regularly publishing interest rate forecasts with detailed discussions. The three-month interbank rate path calculated with the Nelson-Siegel model was contrasted with both the trajectory of policy rates presented in central bank projections and that implied by the three-month EURIBOR. We found that interest rate expectations were more influenced by ECB policy than by domestic assumptions when the forecast horizon exceeds four quarters.

This work develops and estimates a three-factor term structure model with explicit sentiment factors in a period including the global financial crisis, where market confidence was said to erode considerably. It utilizes a large text data of real time, relatively high-frequency market news and takes account of the difficulties in incorporating market sentiment into the models. To the best of our knowledge, this is the first attempt to use this category of data in term-structure models. Although market sentiment or market confidence is often regarded as an important driver of asset markets, it is not explicitly incorporated in traditional empirical factor models for daily yield curve data because they are unobservable. To overcome this problem, we use a text mining approach to generate observable variables which are driven by otherwise unobservable sentiment factors. Then, applying the Monte Carlo filter as a filtering method in a state space Bayesian filtering approach, we estimate the dynamic stochastic structure of these latent factors from observable variables driven by these latent variables. As a result, the three-factor model with text mining is able to distinguish (1) a spread-steepening factor which is driven by pessimists’ view and explaining the spreads related to ultra-long term yields from (2) a spread-flattening factor which is driven by optimists’ view and influencing the long and medium term spreads. Also, the three-factor model with text mining has better fitting to the observed yields than the model without text mining. Moreover, we collect market participants’ views about specific spreads in the term structure and find that the movement of the identified sentiment factors are consistent with the market participants’ views, and thus market sentiment.

Evidence in favour of the ability of the term spread to forecast economic growth of the South African economy is non-existent. This could be due to the term spread aggregating information contained in the expected spread and the term premium. To decompose the term spread into its subcomponents, we develop an estimable small open economy new Keynesian dynamic stochastic general equilibrium (SOENKDSGE) model of the inflation targeting South African economy. The SOENKDSGE model is estimated with Bayesian methods over the quarterly period of 2000:01–2014:04. We then use a linear predictive regression framework to analyse the out-of-sample forecasting ability of the aggregate term spread, as well as the expected spread and term premium. Our forecasting results fail to detect forecasting gains from the aggregate term spread and also the term premium, but the expected spread is found to contain important information in forecasting output growth over short- to medium-run horizons, over the period of 2004:01–2014:04, using an in-sample period of 2000:01–2003:04. The results therefore highlight the importance of the forward-looking component of the term spread—the expected spread—in forecasting the output growth of South Africa.

Campbell and Shiller (1991) find the presence of term structure anomaly, in which the slope of the term structure predicts inconsistently to the change in yield of longer term bonds over the life of shorter term bonds during 1952–1987. Focusing on the post Campbell and Shiller period, our findings suggest that the anomaly is not only attributed to term premia but also relates to expectation errors. We found that macroeconomic surprises and irrationality from investors' behaviour are important determinants of time‐varying expectation errors. These factors are capable of explaining the rejection of the expectation hypothesis and the U.S. term structure anomaly in long‐term securities.

It is well documented in the literature that individual saving decisions vary with the life cycle and at the macroeconomic level, a changing demographic age structure affects aggregated savings, which then drives a slow movement of interest rates. In this paper, we propose a semiparametric affine arbitrage-free yield curve model with a low-frequency trend structure driven by the entire age distribution through a life cycle impact function. The unified framework not only fully explores the demographic age structure to robustly explain yield trend, but also utilizes efficiently the interest rate term structure to infer the S-shaped age impact function of the whole life cycle. We estimate the model with quarterly U.S. data from 1950s to present. The results show clearly that the model fits U.S. Treasury yields remarkably well in sample and outperforms popular alternative models out of sample. After removing the demography-driven trend especially pertaining to the baby boomer’s life cycle, the remaining term structure component is stationary with counter-cyclical risk premia.

In this paper we analyse the predictive content of the introduction of macroeconomic variables in term structure dynamic models. We tested the dynamic models using data from the public debt, inflation rate and annual variation of the industrial production index for four European countries: Portugal, Spain, the United Kingdom and Germany. Results obtained for the period from January 1990 to December 2012 indicate that considering macroeconomic factors makes a positive contribution to the improvement of forecasts for different countries and maturities. However, the paper presents evidence of time-varying forecast accuracy, not only across yield maturities and forecast horizons, but also over data subperiods.

There are mixed results on whether macro risks are spanned by the yield curve. This paper reviews the major arguments and takes a global perspective to obtain comprehensive evidence. We study a large cross-section of 22 countries, including both developed and emerging markets. Our regression evidence confirms that macro information provides explanatory power for bond excess returns on top of yield factors. This finding is particularly strong in emerging markets. However, from a mechanical perspective, discriminating between spanned and unspanned models when considering in-sample fit and term premium predictions makes no difference.

I analyze time series momentum along the Treasury term structure. Past bond returns predict future returns because of autocorrelation in both bond carry and yield changes. Yield curve momentum can largely be captured using a single bond return or yield change factor. Because yield changes are partly induced by changes in the federal funds rate, yield curve momentum is related to FOMC post announcement drift. The momentum factor is unspanned by the information in the term structure today and is hence inconsistent with standard term structure, macrofinance and behavioural models, including models designed to explain momentum. I propose a potential resolution.

This paper proposes a new approach to estimate an expected market equity yield curve by aggregating equity yields of individual firms over the market. The firms' equity yields are estimated using the implied cost of capital approach. During the period from 1990 to 2019, the U.S. equity yield curve was, on average, upward-sloping. This is a result of the aggregation effect: companies with high equity yields were expected to grow at higher rates than companies with low equity yields. Thus, high-yield companies dominate the market when increasing the investment time horizon. Together with the usual concave shape of the term structure of interest rates, this generates an upward-sloping market equity yield curve.

Understanding the movements of the yields on key government securities and the risks associated with the downside of the distribution of the yield curve are crucial to understanding the movements of the markets influenced by short-term rates. Moreover, understanding how macrofinancial factors and domestic financial conditions can be linked to these yields is a crucial linkage which can deepen understanding on the transmissions and spillovers from the financial markets toward key economic variables. This study seeks to propose and implement a framework that quantifies the risks associated with the downside of yields and estimate a yield curve with these tail risks considered. Moreover, the framework can be used as a scenario analysis tool on seeing changes in the yield curve given changes in macrofinancial conditions in the economy.

The literature on using yield curves to forecast recessions typically measures the term spread as the difference between the 10-year and the three-month Treasury rates. Furthermore, using the term spread constrains the long- and short-term interest rates to have the same absolute effect on the recession probability. In this study, we adopt a machine learning method to investigate whether the predictive ability of interest rates can be improved. The machine learning algorithm identifies the best maturity pair, separating the effects of interest rates from those of the term spread. Our comprehensive empirical exercise shows that, despite the likelihood gain, the machine learning approach does not significantly improve the predictive accuracy, owing to the estimation error. Our finding supports the conventional use of the 10-year--three-month Treasury yield spread. This is robust to the forecasting horizon, control variable, sample period, and oversampling of the recession observations.

This chapter deals with the dynamic relationship between term and credit spreads within a framework that incorporates regime shifts. Through the estimation of an asymmetric Markov-Switching Vector Equilibrium Correction Model (MS-VECM), comprising the short- and the long-term risk-free rate and the Moody’s Baa-rated bond rate, we arrive at three basic results. First, the short-run relationship between credit and term spreads is regime dependent, being negative and significant during low volatility periods only and in the presence of an inverted yield curve. Second, through an impulse response analysis, it is established that the response of credit spreads to a given change in the term spread depends on the rate, short- or long term, that has caused this change. Third, a probit analysis reveals that the credit spread is a useful predictor of future economic recessions, both at a six-month and a one-year ahead horizon, and that this result is valid even in the presence of the term spread variable.

A novel decomposition highlights the scope for information to in uence the term structure of interest rates. Based on the law of total covariance, we show that real term premia in macroeconomic models contain a component that depends on covariances of realised stochastic discount factors and a component that depends on covariances of expectations of those stochastic discount factors. The impact of different informational assumptions can then be identified by looking at their effect on the second, expectational, component. If agents have full information about technology in a simple macro-finance model then the conditional covariance of expectations is low, which contributes to the real term premia implied by the model being at least an order of magnitude too small, a result that is unchanged if some components of technology are unobservable or observed with noise. To generate realistic term premia, we draw on the beauty contest literature by differentiating between private and public information and introducing the possibility of strategic complementarities in the formation of expectations. A quantitative version of the model is found to explain a significant proportion of observed term premia when estimated using data on expectations of productivity growth from the Survey of Professional Forecasters.