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.     

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.     

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.     

Time Horizons and Smoothing in the Bank of England's Reaction Function: The Contrast Between the Standard GMM and Ex Ante Forecast Approaches*

The monetary policy reaction function of the Bank of England is estimated by the standard GMM approach and the ex ante forecast method developed by Goodhart (2005) , with particular attention to the horizons for inflation and output at which each approach gives the best fit. The horizons for the ex ante approach are much closer to what is implied by the Bank's view of the transmission mechanism, while the GMM approach produces an implausibly slow adjustment of the interest rate, and suffers from a weak instruments problem. These findings suggest a strong preference for the ex ante approach.     

Global yield curve dynamics and interactions: A dynamic Nelson–Siegel approach

The popular Nelson–Siegel [Nelson, C.R., Siegel, A.F., 1987. Parsimonious modeling of yield curves. Journal of Business 60, 473–489] yield curve is routinely fit to cross sections of intra-country bond yields, and Diebold–Li [Diebold, F.X., Li, C., 2006. Forecasting the term structure of government bond yields. Journal of Econometrics 130, 337–364] have recently proposed a dynamized version. In this paper we extend Diebold–Li to a global context, modeling a potentially large set of country yield curves in a framework that allows for both global and country-specific factors. In an empirical analysis of term structures of government bond yields for the Germany, Japan, the UK and the US, we find that global yield factors do indeed exist and are economically important, generally explaining significant fractions of country yield curve dynamics, with interesting differences across countries.     

Testing for Parameter Stability in Dynamic Models across Frequencies*

This paper contributes to the econometric literature on structural breaks by proposing a test for parameter stability in vector autoregressive (VAR) models at a particular frequency ω, where ω ∈ [0, π]. When a dynamic model is affected by a structural break, the new tests allow for detecting which frequencies of the data are responsible for parameter instability. If the model is locally stable at the frequencies of interest, the whole sample size can then be exploited despite the presence of a break. The methodology is applied to analyse the productivity slowdown in the US, and the outcome is that local stability concerns only the higher frequencies of data on consumption, investment and output.     

Testing for short- and long-run causality: A frequency-domain approach

The framework of Geweke (1982. Journal of the American Statistical Association 77, 304–324.) and Hosoya (1991. Probability Theory and Related Fields 88, 429–444.) is adopted to construct a simple test for causality in the frequency domain. This test can also be applied to cointegrated systems. To study the large sample properties of the test, we analyze the power against a sequence of local alternatives. The finite sample properties are investigated by means of Monte Carlo simulations. Our methodology is applied to investigate the predictive content of the yield spread for future output growth. Using quarterly US data we observe reasonable leading indicator properties at frequencies around one year and typical business cycle frequencies.     

A Semiparametric Analysis of the Term Structure of the US Interest Rates*

The short end of the US$ term structure of interest rates is analysed allowing for the possibility of fractional integration and cointegration. This approach permits mean‐reverting dynamics for the data and the existence of a common long run stochastic trend to be maintained simultaneously. We estimate the model for the period 1963–2006 and find it compatible with this structure. The restriction that the data are I(1) and the errors are I(0) is rejected, mainly because the latter still display long memory. This result is consistent with a model of monetary policy in which the Central Bank operates affecting contracts with short term maturity, and the impulses are transmitted to contracts with longer maturities and then to the final goals. However, the transmission of the impulses along the term structure cannot be modelled using the Expectations Hypothesis.     

Scanner data, time aggregation and the construction of price indexes

We examine the impact of time aggregation on price change estimates for 19 supermarket item categories using scanner data. Time aggregation choices lead to a difference in price change estimates for chained indexes which ranged from 0.28% to 29.73% for a superlative index and an incredible 14.88%-46,463.71% for a non-superlative index. Traditional index number theory appears to break down with weekly data, even for superlative indexes. Monthly and (in some cases) quarterly time aggregation were insufficient to eliminate downward drift in superlative indexes. To eliminate drift, a novel adaptation of a multilateral index number method is proposed.     

Anchoring the yield curve using survey expectations

The dynamic behavior of the term structure of interest rates is difficult to replicate with models, and even models with a proven track record of empirical performance have underperformed since the early 2000s. On the other hand, survey expectations can accurately predict yields, but they are typically not available for all maturities and/or forecast horizons. We show how survey expectations can be exploited to improve the accuracy of yield curve forecasts given by a base model. We do so by employing a flexible exponential tilting method that anchors the model forecasts to the survey expectations, and we develop a test to guide the choice of the anchoring points. The method implicitly incorporates into yield curve forecasts any information that survey participants have access to—such as information about the current state of the economy or forward‐looking information contained in monetary policy announcements—without the need to explicitly model it. We document that anchoring delivers large and significant gains in forecast accuracy relative to the class of models that are widely adopted by financial and policy institutions for forecasting the term structure of interest rates.     

What's Unique About the Federal Funds Rate? Evidence from a Spectral Perspective*

This paper compares the behaviour of the effective federal funds rate to 10 US interest rates with maturities ranging from overnight to 10 years. Using spectral estimation methods, we identified idiosyncratic shocks to the funds rate and provided evidence on their impact on other rates at various frequencies. Our results suggest that, while all of the interest rates examined have common shocks at low frequencies, the federal funds rate contains some unique information at high frequency, although this information appears to be relevant only at the short end of the term structure. In turn, these results are open to various alternative interpretations.     

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.     

No-arbitrage macroeconomic determinants of the yield curve

No-arbitrage macro-finance models use variance decompositions to gauge the extent of association between the macro variables and yields. We show that results generated by this approach are sensitive to the order of variables in the recursive identification scheme. In a four-factor model, one may obtain 18 different sets of answers out of 24 possible. We propose an alternative measure that is based on levels of macro variables as opposed to shocks. We account for the correlation between the macro and latent factors via projection of the latter onto the former. As a result, the association between macro variables and yields can be computed uniquely via an R²$R^2$. Macro variables explain 80% of the variation in the short rate and 50% of the slope, and 54% to 68% of the term premia.     

Policy change and learning in the RBC model

What is the impact of surprise and anticipated policy changes when agents form expectations using adaptive learning rather than rational expectations? We examine this issue using the standard stochastic real business cycle model with lump-sum taxes. Agents combine knowledge about future policy with econometric forecasts of future wages and interest rates. Dynamics under learning can have large impact effects and a gradual hump-shaped response, and tend to be prominently characterized by oscillations not present under rational expectations. These fluctuations reflect periods of excessive optimism or pessimism, followed by subsequent corrections.     

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.     

How better monetary statistics could have signaled the financial crisis

This paper explores the disconnect of Federal Reserve data from index number theory. A consequence could have been the decreased-systemic-risk misperceptions that contributed to excess risk-taking prior to the housing bust. We find that most recessions in the past 50 years were preceded by more contractionary monetary policy than indicated by simple-sum monetary data. Divisia monetary aggregate growth rates were generally lower than simple-sum aggregate growth rates in the period preceding the Great Moderation, and higher since the mid 1980s. Monetary policy was more contractionary than likely intended before the 2001 recession and more expansionary than likely intended during the subsequent recovery.     

Negative interest rate policy and the yield curve

We evaluate the implications of the ECB's negative interest rate policy (NIRP) on the yield curve. To capture various shapes of the short end of the yield curve induced by the NIRP, we introduce two policy indicators, which summarize the immediate and longer horizon future monetary policy stances. We find that the four NIRP events lowered the short-term interest rate by the same amount. The impact is dampened at longer maturities for the first two event dates, due to lack of forward guidance. By contrast, for the last two dates, forward guidance drives the largest effects in two years.     

A Rational Expectations Consistent Measure of Risk: Using Financial Market Data from a Middle Income Context*

Although economic theory assumes that risk is of central importance in financial decision making, it is difficult to measure the uncertainty faced by investors. Commonly used empirical proxies for risk (such as the moving standard deviation of the returns on an asset) are not firmly grounded in economic theory. Risk measures have been developed by other studies, but these are often based on subjective weights attaching to a range of objective component indicators, are difficult to replicate and are not strictly consistent with underlying theory. The contribution of this article is to develop a methodology to construct rational expectations consistent empirical risk measures. It has the advantages of being explicitly consistent with economic theory and easily replicable. We illustrate this methodology by specific application to the South African context. The time‐varying risk measure developed in this article is consistent with a rational expectations application of the expectations hypothesis. The constructed measure is a broad one (it includes political risk and peso problems for instance) and reflects investors’ perceptions of systematic risk.     

State‐Dependent Threshold Smooth Transition Autoregressive Models*

In this article, we consider extensions of smooth transition autoregressive (STAR) models to situations where the threshold is a function of variables that affect the separation of regimes of the time series under consideration. Our specification is motivated by the observation that unusually high/low values for an economic variable may sometimes be best thought of in relative terms. State‐dependent contemporaneous‐threshold STAR and logistic STAR models are introduced and discussed. These models are also used to investigate the dynamics of US short‐term interest rates, where the threshold is allowed to be a function of past output growth and inflation.     

Eliminating chain drift in price indexes based on scanner data

The use of scanner data in the CPI makes it possible to compile superlative price indexes at detailed aggregation levels since prices and quantities are available. A potential drawback is the high attrition rate of items. The usual solution to handle this problem, high-frequency chaining, can create drift in the index series due to price and quantity bouncing arising from sales. Ivancic, Diewert and Fox (2009) have recently proposed an approach that provides drift free, superlative-type indexes through adapting multilateral index number theory. In this paper we apply their proposal to seven product groups and find promising results. We compare the results with those obtained by using the Dutch method to deal with supermarket scanner data.