Forecasting the US term structure of interest rates using a macroeconomic smooth dynamic factor model

We extend the class of dynamic factor yield curve models in order to include macroeconomic factors. Our work benefits from recent developments in the dynamic factor literature related to the extraction of the common factors from a large panel of macroeconomic series and the estimation of the parameters in the model. We include these factors in a dynamic factor model for the yield curve, in which we model the salient structure of the yield curve by imposing smoothness restrictions on the yield factor loadings via cubic spline functions. We carry out a likelihood-based analysis in which we jointly consider a factor model for the yield curve, a factor model for the macroeconomic series, and their dynamic interactions with the latent dynamic factors. We illustrate the methodology by forecasting the U.S. term structure of interest rates. For this empirical study, we use a monthly time series panel of unsmoothed Fama–Bliss zero yields for treasuries of different maturities between 1970 and 2009, which we combine with a macro panel of 110 series over the same sample period. We show that the relationship between the macroeconomic factors and the yield curve data has an intuitive interpretation, and that there is interdependence between the yield and macroeconomic factors. Finally, we perform an extensive out-of-sample forecasting study. Our main conclusion is that macroeconomic variables can lead to more accurate yield curve forecasts.     

Forecasting economic activity from yield curve factors

This paper provides clear-cut evidence that the slope and curvature factors of the term structure of interest rates (yield curve) contain more information about future changes in economic activity than the term spread itself, often used in the literature as a predictive regressor of economic activity. These two factors reflect different information about future economic activity, which is smoothed out by the term spread. The paper shows that the slope factor has predictive power on future economic activity over longer horizons ahead, and thus may be interpreted as reflecting future business cycle conditions. On the other hand, the curvature factor, which enters the term spread with opposite sign than the slope factor, has predictive power on shorter movements of future economic activity which may be associated with changes in the current stance of monetary policy. These results hold for a number of world developed economies.     

The expected real yield and inflation components of the nominal yield curve

The term structure of real yields and expected inflation are two unobserved components of the nominal yield curve. The primary objectives of this study are to decompose nominal yields into their expected real yield and inflation components and to examine their behaviour using state-space and regime-switching frameworks. The dynamic yield-curve models capture three well-known latent factors – level, slope, and curvature – that accurately aggregate the information for the nominal yields and the expected real and inflation components for all maturities. The nominal yield curve is found to increase slightly with a slope of about 120 basis points, while the real yield curve slopes upward by about 20 basis points, and the expected inflation curve is virtually flat at slightly above 2 per cent. The regime-switching estimations reveal that the nominal yield, real yield and expected inflation curves have shifted down significantly since 1999.     

Yield curve responses to market sentiments and monetary policy

Central banks recently started to target longer term interest rates. The empirical failure of the rational expectations theory of the yield curve, however, limits its applicability to monetary policy analysis. The success of agent-based behavioral asset pricing models and behavioral macroeconomic models in replicating statistical regularities of empirical data series motivates to apply them to yield curve modeling. This paper analyses how the interaction of monetary policy and market sentiments shape the yield curve in a behavioral model with heterogeneous and bounded-rational agents. One result is that the behavioral model replicates empirical facts of term structure data. Moreover, it overcomes one major deficiency of rational expectations models of the yield curve in explaining the empirically observed uncertain responses of longer term yields to changes in the central bank rate. These are explained by the behavioral model’s ability to generate different responses of market sentiments to shocks at different times which lead to a variety of interest rate responses. Further results of this paper can be used as policy advice on how central banks can target the level, slope and curvature of the yield curve by targeting market sentiments about inflation and the business cycle.     

Forecasting the term structure of government bond yields

Despite powerful advances in yield curve modeling in the last 20 years, comparatively little attention has been paid to the key practical problem of forecasting the yield curve. In this paper we do so. We use neither the no-arbitrage approach nor the equilibrium approach. Instead, we use variations on the Nelson–Siegel exponential components framework to model the entire yield curve, period-by-period, as a three-dimensional parameter evolving dynamically. We show that the three time-varying parameters may be interpreted as factors corresponding to level, slope and curvature, and that they may be estimated with high efficiency. We propose and estimate autoregressive models for the factors, and we show that our models are consistent with a variety of stylized facts regarding the yield curve. We use our models to produce term-structure forecasts at both short and long horizons, with encouraging results. In particular, our forecasts appear much more accurate at long horizons than various standard benchmark forecasts.     

COVID-19 related media sentiment and the yield curve of G-7 economies

We explore the connectedness of the components of the sovereign yield curve (slope, level and curvature) across G-7 countries and media sentiment about COVID-19. The recent pandemic is a unique opportunity to identifying the transmitters and receivers of risk. Our results indicate that media sentiment along with the US yield curve components are main transmitter of spillovers, whereas Japan is the leading recipient of spillover. Among the European countries, we notice France as a major transmit, whereas Germany and UK switch role as transmitter and receiver alternatively. The results are important for mapping the interconnectedness between countries. In addition, policy makers can use them when devising disaster plans to prepare for future market crises.     

Predicting interest rates using shrinkage methods,real-time diffusion indexes,and model combinations

In the context of predicting the term structure of interest rates, we explore the marginal predictive content of real-time macroeconomic diffusion indexes extracted from a “data rich” real-time data set, when used in dynamic Nelson–Siegel (NS) models of the variety discussed in Svensson (NBER technical report, 1994; NSS) and Diebold and Li (Journal of Econometrics, 2006, 130, 337–364; DNS). Our diffusion indexes are constructed using principal component analysis with both targeted and untargeted predictors, with targeting done using the lasso and elastic net. Our findings can be summarized as follows. First, the marginal predictive content of real-time diffusion indexes is significant for the preponderance of the individual models that we examine. The exception to this finding is the post “Great Recession” period. Second, forecast combinations that include only yield variables result in our most accurate predictions, for most sample periods and maturities. In this case, diffusion indexes do not have marginal predictive content for yields and do not seem to reflect unspanned risks. This points to the continuing usefulness of DNS and NSS models that are purely yield driven. Finally, we find that the use of fully revised macroeconomic data may have an important confounding effect upon results obtained when forecasting yields, as prior research has indicated that diffusion indexes are often useful for predicting yields when constructed using fully revised data, regardless of whether forecast combination is used, or not. Nevertheless, our findings also underscore the potential importance of using machine learning, data reduction, and shrinkage methods in contexts such as term structure modeling.     

Forecasting macroeconomic variables using collapsed dynamic factor analysis

We explore a new approach to the forecasting of macroeconomic variables based on a dynamic factor state space analysis. Key economic variables are modeled jointly with principal components from a large time series panel of macroeconomic indicators using a multivariate unobserved components time series model. When the key economic variables are observed at a low frequency and the panel of macroeconomic variables is at a high frequency, we can use our approach for both nowcasting and forecasting purposes. Given a dynamic factor model as the data generation process, we provide Monte Carlo evidence of the finite-sample justification of our parsimonious and feasible approach. We also provide empirical evidence for a US macroeconomic dataset. The unbalanced panel contains quarterly and monthly variables. The forecasting accuracy is measured against a set of benchmark models. We conclude that our dynamic factor state space analysis can lead to higher levels of forecasting precision when the panel size and time series dimensions are moderate.     

Assessing international commonality in macroeconomic uncertainty and its effects

This paper uses a large vector autoregression to measure international macroeconomic uncertainty and its effects on major economies. We provide evidence of significant commonality in macroeconomic volatility, with one common factor driving strong comovement across economies and variables. We measure uncertainty and its effects with a large model in which the error volatilities feature a factor structure containing time-varying global components and idiosyncratic components. Global uncertainty contemporaneously affects both the levels and volatilities of the included variables. Our new estimates of international macroeconomic uncertainty indicate that surprise increases in uncertainty reduce output and stock prices, adversely affect labor market conditions, and in some economies lead to an easing of monetary policy.     

Forecasting the yield curve in a data-rich environment: A no-arbitrage factor-augmented VAR approach

This paper suggests a term structure model which parsimoniously exploits a broad macroeconomic information set. The model uses the short rate and the common components of a large number of macroeconomic variables as factors. Precisely, the dynamics of the short rate are modeled with a Factor-Augmented Vector Autoregression and the term structure is derived using parameter restrictions implied by no-arbitrage. The model has economic appeal and provides better out-of-sample yield forecasts at intermediate and long horizons than a number of previously suggested approaches. The forecast improvement is highly significant and particularly pronounced for short and medium-term maturities.     

The small open macroeconomy and the yield curve: A state-space representation

This study extends a state-space representation of the yield curve and the macroeconomy to a small open economy in order to study the dynamic interaction between the yield curves in Canada and the U.S. The framework treats the U.S. term structure of interest rates as being exogenous to both the Canadian yield curve and macroeconomy. The empirical results support very strong links between the yield curves in the two countries, with the U.S. yield curve accounting for as much as 45 per cent of the variation of the movement in the level and about 30 per cent of the movements in the slope and the curvature of the Canadian yield curve. Canadian yield-curve factors are found to account for about 50 per cent of the variation in output and the monetary policy rate, and about 25 per cent of the variation in inflation, much larger than the yield curve effects found for future developments of the macroeconomies of other countries. A relatively strong bilateral relationship is found to exist between the yield curve and the instrument of monetary policy, supporting recent studies that find the dynamic relationship between the yield curve and the macroeconomy is due to the pivotal role that monetary policy plays in the macroeconomy.     

How Stable is the Forecasting Performance of the Yield Curve for Output Growth?*

We provide an extensive evaluation of the predictive performance of the US yield curve for US gross domestic product growth by using new tests for forecast breakdown, in addition to a variety of in‐sample and out‐of‐sample evaluation procedures. Empirical research over the past decades has uncovered a strong predictive relationship between the yield curve and output growth, whose stability has recently been questioned. We document the existence of a forecast breakdown during the Burns–Miller and Volker monetary policy regimes, whereas during the early part of the Greenspan era the yield curve emerged as a more reliable model to predict future economic activity.     

Trade openness,financial openness,and macroeconomic volatility

This paper develops a new model of an open economy for a study of macroeconomic dynamics under different degrees of trade and financial openness. The model is estimated using quarterly data on the Chinese economy over the period 2005Q3-2020Q4 and then applied to analyze how macroeconomic volatility varies with trade and financial openness in several representative settings. We find that the impacts on macroeconomic volatility of trade openness and financial openness depend on the nature of the underlying shocks and a moderate degree of trade openness, together with a high degree of financial openness, yield the optimal welfare results in most cases. These results highlight that the effects of trade and financial openness on macroeconomic volatility differ in various situations and that their interactions can lead to different welfare results.     

A No‐Arbitrage Structural Vector Autoregressive Model of the UK Yield Curve*

This article combines a Structural Vector Autoregression with a no‐arbitrage approach to build a multifactor Affine Term Structure Model (ATSM). The resulting No‐Arbitrage Structural Vector Autoregressive (NASVAR) model implies that expected excess returns are driven by structural macroeconomic shocks. This is in contrast with a standard ATSM, in which agents are concerned with non‐structural risks. As a simple application, we study the effects of supply, demand and monetary policy shocks on the UK yield curve. We show that all structural shocks affect the slope of the yield curve, with demand and supply shocks accounting for a large part of the time variation in bond yields.     

The Role of Financial,Macroeconomic, and Non-financial Information in Bank Loan Default Timing Prediction

We assess the use of bank loan information in predicting the timing to default. We use unique data on defaults in small and medium enterprises maintained by the Central Bank of Portugal which includes financial accounting and macroeconomic indicators, as well as non-financial information. The findings are indicative of the incremental predictive ability of non-financial information over and above macroeconomic and financial accounting information in the baseline, industry, and in- and out-of-sample models. Specifically, total credit secured by firms is, as expected, negatively and significantly related to default. Gross domestic product is negatively and significantly related to default, and benchmark market rate is positively and significantly associated with default. The findings also reveal that firms which are operated by partners, which have stronger financial support from partners, and which possess operational assets exhibit lower hazards of default. The study indicates that non-financial information and macroeconomic indicators assessed alongside financial accounting data can significantly improve the forecasting performance of default models.     

Income risk, macroeconomic and demographic change, and economic inequality in Japan

Using an OLG model with heterogeneous households, we investigate the relationship among income risk, macroeconomic and demographic changes, and economic inequality between 1980 and 2000 in Japan. By decomposing the primary factors in earnings and consumption inequality into macroeconomic variables and the demographic variable, we find that our model replicates the evolution of economic inequality in Japan. By performing counterfactual simulations, we demonstrate that two factors—changes in time-varying macroeconomic factors and the unexpected decline in the total factor productivity growth rate—played important roles in the increase in earnings and consumption inequality in the 1990s.     

Principal component analysis of yield curve movements

An important issue in interest rate modeling is the number and nature of the random factors driving the evolution of the yield curve. This paper uses principal component analysis to examine (1) the inherent dimension of historical yield curve changes indicated by the significance of eigenvalues of the covariance matrix, (2) the practical dimension determined by a variance threshold, (3) the shape of the yield curve change associated with the first principal component, and (4) the persistence of this shape over time. We find that although the first two components explain 93% of the sample variation within a 90% confidence interval, the remaining components make statistically significant contribution to the covariance matrix. Consequently, we can establish a practical limit on the dimension only if we are willing to designate a threshold error variance. Further, our results on the persistence of the shape of the yield curve shift associated with the first component depend upon this threshold. If all components are included, the hypothesis that the shape persists between two sample time periods is rejected. On the other hand, if all but the first six components are eliminated, the hypothesis is not rejected.     

Further evidence on financial information and economic activity forecasts in the United States

Our study provides substantially robust evidence for the predictive power of financial variables in forecasting the business cycle at a further step. We select several interesting and representative financial variables and reveal that they can predict significant information regarding future equity premiums as well as future macroeconomic activity, which are proxied by comprehensive fresh macroeconomic variables. The predictive power remains stable in out-of-sample estimations and can generate profits in an active market-timing trading strategy in excess of the historical mean forecast strategy. Cochrane provides one of the core interpretations for such forecasts in the theoretical asset pricing framework.     

Learning,forecasting and structural breaks

We provide a general methodology for forecasting in the presence of structural breaks induced by unpredictable changes to model parameters. Bayesian methods of learning and model comparison are used to derive a predictive density that takes into account the possibility that a break will occur before the next observation. Estimates for the posterior distribution of the most recent break are generated as a by‐product of our procedure. We discuss the importance of using priors that accurately reflect the econometrician's opinions as to what constitutes a plausible forecast. Several applications to macroeconomic time‐series data demonstrate the usefulness of our procedure. Copyright © 2008 John Wiley & Sons, Ltd.