Treasury Yield Curve Research Articles

Dollar reserves and U.S. yields: Identifying the price impact of official flows

By exploiting changes in the volatility of U.S. Treasury yields and foreign official (FO) flows into U.S. Treasuries after the 2008 Global Financial Crisis, we identify a FO flow shock via heteroskedasticity in a structural VAR. We estimate that a $100B FO flow shock moves 5 and 10-year U.S. yields by about 100 basis points within a month. An event study of the intraday U.S. Treasury yield curve response to Japan’s FX intervention in September 2022 validates our VAR estimates. Our findings imply that a 1% reduction in the Dollar share of China’s reserves could raise long-term U.S. yields by about 20 basis points.

Reassessing the inversion of the Treasury yield curve as a sign of U.S. recessions: Insights from the housing and credit markets

Is yield curve inversion a reliable recession signal? Prior research on forecasting recessions investigates the role of the slope of the yield curve, housing, banking, and corporate credit spreads in isolation, without fully considering their interconnectedness. Therefore, we conduct a comprehensive investigation into the ability of house prices, residential investment, bank aggregate liquidity creation (LC) and credit (BC), and corporate credit spreads to forecast recessions. For the 1973–2023 sample, after accounting for the slope of the yield curve, our in- and out-of-sample results show that: i) house prices and credit spreads signal recessions—house prices decline, and credit spreads rise ahead of recession quarters; ii) residential investment’s recession forecasting ability is not as robust as house prices; iii) the recession forecasting ability of LC or BC diminishes when we include other indicators. These findings provide important insights into the interconnectedness among economic indicators and their relationship with recessions. Importantly, we demonstrate that the inversion of the yield curve alone is not the surest sign of a recession. For a recession to occur, house prices must decline, and corporate credit spreads must significantly increase.

The shape of the Treasury yield curve and commodity prices

We decompose the U.S. yield curve into three latent factors – the level, slope and curvature – and explore the information content of the yield curve regarding the future evolution in oil, coal, copper, ethanol, gold, heating oil, natural gas, palladium, platinum, silver and zinc prices. Using data from January 1986 to November 2021, we find that the shape of the term structure is very informative about future innovations in these commodities. Results indicate that shocks to the level, which represents long-term expectations about inflation, predict positive (negative) price (volatility) changes. Shocks to the slope and curvature, which are interpreted as part of the business cycle conditions, predict negative price changes. These results are manifested mainly in the period after the financialization era in 2004. Our findings have far-reaching implications for investors, policymakers and firms involved in the mining industry.

Quantile-on-quantile connectedness measures: Evidence from the US treasury yield curve

This study introduces a novel quantile-on-quantile connectedness approach to explore reversely related and directly related quantile spillovers. To illustrate the benefits of the proposed method, we examine the spillovers across the 2-year US Treasury yield (US2Y) and the yield curve spread between the 10-year and 2-year US Treasury yield (US2Y10Y) from 13 July 1998 to 11 July 2023. The empirical results show that the average total connectedness between reversely related quantiles is substantially higher than directly related quantiles. Additionally, the average quantile-based total connectedness is heterogeneous over time and economic events dependent.

Are Shadow Rate Models of the Treasury Yield Curve Structurally Stable?

Are Shadow Rate Models of the Treasury Yield Curve Structurally Stable?

Co-Jumping of Treasury Yield Curve Rates

Abstract We study the role of co-jumps in the interest rate futures markets. To disentangle continuous part of quadratic covariation from co-jumps, we localize the co-jumps precisely through wavelet coefficients and identify statistically significant ones. Using high frequency data about U.S. and European yield curves we quantify the effect of co-jumps on their correlation structure. Empirical findings reveal much stronger co-jumping behavior of the U.S. yield curves in comparison to the European one. Further, we connect co-jumping behavior to the monetary policy announcements, and study effect of 103 FOMC and 119 ECB announcements on the identified co-jumps during the period from January 2007 to December 2017.

The term structure of yield curve and connectedness among ESG investments

We examine the connectedness of different components of the US treasury’s term structure with ESG (Environment, Social, and Governance) leader indices regarding return and volatility. The EMU (Economic and Monetary Union) and the UK lie at the center of the network. The connectedness is time-varying and versatile regarding return- and volatility spillovers and various components of the US treasury yield curve. The long-term rates influence returns, whereas the short- and medium-term are dominant regarding volatility spillovers. Interestingly, both return- and volatility spillovers from the yield curve components change behaviors/directions during the GFC and COVID-19. Importantly, long-term interest rates are significant shock transmitters during times of stress. Our findings have implications for ESG investors, who should keep in mind the shape of the yield curve while making portfolio choices and investment-horizon decisions.

Intermediary balance sheets and the treasury yield curve

We document a regime change in the Treasury market post-Global Financial Crisis (GFC): dealers switched from net short to net long Treasury bonds. We construct “net-long” and “net-short” curves that account for balance sheet and financing costs, and show that actual yields moved from the net short curve pre-GFC to the net long curve post-GFC. Our theory shows the regime shift caused negative swap spreads and co-movement among swap spreads, dealer positions, and covered-interest-parity violations. Furthermore, the effects of various monetary and regulatory policies are regime-dependent. We highlight Treasury supply as a plausible driver of this regime shift.

Emerging Markets Currency Factors and US High-Frequency Macroeconomic Shocks

Relying on the structural vector autoregression developed by Cieslak and Pang, the authors identify four shocks to the US economy based on the US Treasury yield curve and the stock market: two fundamental news shocks (growth and money) and two risk-premium shocks (common and hedging). They find that these shocks explain over 40% of the time-series variation of emerging markets currency (EMFX) returns. Additionally, EMFX returns increase significantly with positive growth shocks and decrease with monetary tightening and risk-premium shocks. The authors show that growth and common shocks are priced in the cross section of EMFX, with a positive and negative risk premiums, respectively. They then build long–short currency portfolios based on several academically researched style factors and test their performance and relative exposure to the macroeconomic shocks affecting the US economy. They find that only carry and macro momentum long–short portfolios generate positive and significant alphas and excess returns over their sample. However, all single-factor portfolios have sizable exposure to the four shocks. The authors show that a simple multifactor approach to investing in EMFXs eliminates the exposure of excess returns to all macroeconomic shocks.

Examining the sources of excess return predictability: Stochastic volatility or market inefficiency?

We use a consumption based asset pricing model to show that the predictability of excess returns on risky assets can arise from only two sources: (1) stochastic volatility of fundamental variables, or (2) departures from rational expectations that give rise to predictable investor forecast errors and market inefficiency. While controlling for stochastic volatility, we find that a variable which measures non-fundamental noise in the Treasury yield curve helps to predict 1-month-ahead excess stock returns, but only during sample periods that include the Great Recession. For these sample periods, higher noise predicts lower excess stock returns, implying that a shortage of arbitrage capital in financial markets allowed excess returns to drop below the levels justified by fundamentals. The statistical significance of the predictor variables that control for stochastic volatility are also typically sensitive to the sample period. Measures of implied and realized stock return variance cease to be signficant when the COVID-influenced data from early 2020 onward is included.

The effect of uncertainty on the sensitivity of the yield curve to monetary policy surprises

I examine how the level of uncertainty affects the sensitivity of the U.S Treasury yield curve to monetary policy surprises. My analysis is based on a nonlinear dynamic Nelson-Siegel model of bond yields with regime shifts that depend on the level of macroeconomic uncertainty. The dynamics of the entire yield curve are captured by three latent factors, level, slope, and curvature, with exogenous monetary policy surprises affecting the yield curve through the factors. I find that uncertainty and the zero lower bound both play important roles in the yield curves reactions to monetary policy surprises. Specifically, during the ZLB period there is a significant increase in the sensitivity of the yield curve when uncertainty is high, with the differences being primarily in terms of the level and slope factors.

Examining the Sources of Excess Return Predictability: Stochastic Volatility or Market Inefficiency?

We use a consumption based asset pricing model to show that the predictability of excess returns on risky assets can arise from only two sources: (1) stochastic volatility of fundamental variables, or (2) departures from rational expectations that give rise to predictable investor forecast errors and market inefficiency. While controlling for stochastic volatility, we find that a variable which measures non-fundamental noise in the Treasury yield curve helps to predict 1-month-ahead excess stock returns, but only during sample periods that include the Great Recession. For these sample periods, higher noise predicts lower excess stock returns, implying that a shortage of arbitrage capital in financial markets caused excess returns to drop below the levels justified by fundamentals. The statistical significance of the predictor variables that control for stochastic volatility are also typically sensitive to the sample period. Measures of implied and realized stock return variance cease to be significant when the COVID-influenced data from early 2020 onward is included.

Intermediary Balance Sheets and the Treasury Yield Curve

Intermediary Balance Sheets and the Treasury Yield Curve

Intermediary Balance Sheets and the Treasury Yield Curve

Intermediary Balance Sheets and the Treasury Yield Curve

Intermediary Balance Sheets and the Treasury Yield Curve

Intermediary Balance Sheets and the Treasury Yield Curve

Shape Evolution of the Interest Rate Term Structure

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.

The GDP, the US treasury yield and the federal funds rate: who follows whom, when and why?

PurposeThis study aims to address the fundamental question on how the major players in the economy dynamically interact with each other: among the central bank, the investors in the bond market and the firms and consumers that contribute to the economic growth, who gets information from whom, when and why?Design/methodology/approachTo answer “who follows whom,” the authors apply a novel technique to examine the lead–lag relations between three time series, the federal funds rate, the treasury yield curve and the gross domestic product (GDP). To investigate “when and why,” the authors combine the lead–lag relations with principal component analysis to cluster economic states that are similar with respect to the eight macroeconomic variables.FindingsThe authors show that during the period 1977–2019, the bond market potentially obtained information from the federal funds rate (61% of the time) and less often (34% of time) from the changes in the GDP. Meanwhile, the funds rate decision by the Federal Reserve seems to lead the economic growth about 63% of the time. The analysis also suggests that the bond market obtained information directly from GDP when unemployment and inflation was high. In addition, the authors find that the federal funds rate was leading the GDP when the GDP deviated from the target value, consistent with the Federal Reserve’s policy of boosting and damping the economy when the GDP growth is low or high, respectively.Originality/valueThis study provides insights into the fundamental questions that have important implications for empirical work on the monetary policy, financial stability and economic activities.

Emerging Markets Currency Factors and U.S. High Frequency Economic Shocks

Relying on the structural vector autoregression developed by Cieslak and Pang (2021), we identify four shocks to the U.S. economy based on the U.S. Treasury yield curve and the stock market: two fundamental news shocks (growth and monetary policy) and two risk-premium shocks (common and hedging). We find that these shocks explain over 40% of the time-series variation of emerging markets currency (EMFX) returns. Additionally, EMFX returns increase significantly with positive U.S. growth shocks and decrease with monetary tightening and risk-premium shocks. We then build long-short currency portfolios based on several academically researched style factors and test their relative exposure to U.S. macroeconomic shocks. We find that only Carry and Macro Momentum long-short portfolios generate positive and significant alphas and excess returns over our sample. However, all single factor portfolios have sizable exposure to U.S. high-frequency shocks. We show that a simple multifactor approach to investing in EMFXs eliminates the exposure of excess returns to U.S. macroeconomic shocks.

The Effect of Uncertainty on the Sensitivity of the Yield Curve to Monetary Policy Surprises

I examine how the level of uncertainty affects the sensitivity of the U.S Treasury yield curve to monetary policy surprises. My analysis is based on a nonlinear dynamic Nelson-Siegel model of bond yields with regime shifts that depend on the level of macroeconomic uncertainty. The dynamics of the entire yield curve are captured by three latent factors, level, slope, and curvature, with exogenous monetary policy surprises affecting the yield curve through the factors. I find that uncertainty and the zero lower bound both play important roles in the yield curve’s reactions to monetary policy surprises. Specifically, during the ZLB period there is a significant increase in the sensitivity of the yield curve when uncertainty is high, with the differences being substantial in terms of the level and curvature factors.

The Yield Curve as a Leading Indicator: Accuracy and Timing of a Parsimonious Forecasting Model

Previous studies have shown that the treasury yield curve, T, forecasts upcoming recessions when it obtains a negative value. In this paper, we try to improve the yield curve model while keeping its parsimony. First, we show that adding the federal funds rate, FF, to the model, GDP = f(T, FF), gives seven months vs. five months warning time, and it gives a higher prediction skill for the recessions in the out-of-sample test set. Second, we find that including the quadratic term of the yield curve and the federal funds rate improves the prediction of the 1990 recession, but not the other recessions in the period 1977 to 2019. Third, the T caused a pronounced false peak in GDP for the test set. Restricting the learning set to periods where T and FF were leading the GDP in the learning set did not improve the forecast. In general, recessions are predicted better than the general movement in the economy. A “horse race” between GDP = f(T, FF) and the Michigan consumer sentiment index suggests that the first beats the latter by being a leading index for the observed GDP for more months (50% vs. 6%) during the first test year.