Treasury Yields Research Articles

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.

Evaluating the Effectiveness of Quantitative Easing Measures of the Federal Reserve and the European Central Bank

This scientific paper examined the relation between conventional and unconventional monetary policy, with an anticipation to provide a comprehensive assessment of how they behave with the goal of mitigating financial distress, at the state level, influencing global economy. The sampling frame involved five variables related to the US Federal Reserve and five variables related to the European Central Bank, observed during the period 2015-2020 (data for January, April, July and October of each year, regarding every research variable were collected). These variables included: Euro Area Inflation Rate, ECB Bonds Yields, Euro Area Broad Money Supply (M2), Euro Area Unemployment Rate, Debt to GDP; and US Inflation Rate, US Treasury Yields, US Broad Money Supply (M2), US Unemployment Rate, Debt to GDP. Accordingly, two adequate research models were created. Research methodology focused on examining the accuracy of the hypotheses using SmartPLS 3 as a tool for conducting mediation analysis. Research implications suggest that Quantitative Easing caused a significant increase in the Federal Reserve’s and European Central Bank’s balance sheet, especially during the global financial crisis (2007-2008) and during the post-crisis, recovery period. In terms of policy recommendations, monetary authorities need to have policy sets ready in place, in order to know how to behave during and post an economic crisis. This scientific paper will serve as an accurate source of information to future researchers in the field of conventional and unconventional monetary measures, because the work is well systematically organized, clear for interpretation and provides an extensive insight into the Fed’s and ECB’s transmission mechanisms of monetary policy.

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.

Forecasting Startup Return using Artificial Intelligence Methods and Econometric Models and Portfolio Optimization Using VaR and C-VaR

In this paper, we have tried to study the main role of startups in economy, their characteristics, main goals and etc. The main goal of article is prediction of startup's return using artificial intelligence methods such as genetic algorithm (GA) and artificial neural network (ANN). Some global indices such as S&P500, DJAI, and economic indicators such as 10 years Treasury yield, Wilshire 5000 Total Market Full Cap Index along with some other special indicators in startups like team, idea, timing and etc. are used as input variables. GA is used as feature selection and finding the most important variables. ANN is used as an optimization model and prediction of startup's returns. We used econometric models such as regression analysis. We have estimated Value at risk (VaR) and Conditional Value at risk (C-VAR) for considered portfolios including three startups (public company) such as Dropbox, Inc. (DBX), Scout24 SE (G24.DE) and TIE.AS and optimal portfolio formation. The results show that AI based methods are more powerful in prediction of startup's return. On the other hand, VaR and C-VaR models are very beneficial approach in minimizing risk and maximizing return.

Mutual Fund Liquidity Transformation and Reverse Flight to Liquidity

Abstract We identify fixed-income mutual funds as an important contributor to the unusually high selling pressure in liquid asset markets during the COVID-19 crisis. We show that mutual funds experienced pronounced investor outflows amplified by their liquidity transformation. In meeting redemptions, funds followed a pecking order by first selling their liquid assets, including Treasuries and high-quality corporate bonds, which generated the most concentrated selling pressure in these markets. Overall, the estimated price impact of mutual funds was sizable at a third of the increase in Treasury yields and a quarter of the increase in corporate bond yields during the COVID-19 crisis.

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.

Commercial Bank and Currency Bureau Exchange Rate Sell-Buy Spreads in a Northern Mexico Metropolitan Economy

ABSTRACT Commercial banks and currency kiosk bureaus provide exchange services throughout Ciudad Juarez. This study employs newspaper data to examine how the bank and kiosk sell-buy spreads are affected by border crossings, the bilateral peso per dollar exchange rate, and 90-day Mexico – U.S. treasury yield differentials. Sample data are from January 2009 through June 2016. Parameter estimation is carried out using a GLS ARMAX procedure. Empirical results indicate that bank spreads increase as the peso weakens. Exchange bureau spreads decrease as cross-border traffic flows increase. Currency kiosk spreads tend to widen as 90-day yield gaps become larger.

Interest Rate Skewness and Biased Beliefs

Conditional yield skewness is an important summary statistic of the state of the economy. It exhibits pronounced variation over the business cycle and with the stance of monetary policy, and a tight relationship with the slope of the yield curve. Most importantly, variation in yield skewness has substantial forecasting power for future bond excess returns, high-frequency interest rate changes around FOMC announcements, and consensus survey forecast errors for the ten-year Treasury yield. The COVID pandemic did not disrupt these relations: historically high skewness correctly anticipated the run-up in long-term Treasury yields starting in late 2020. The connection between skewness, survey forecast errors, excess returns, and departures of yields from normality is consistent with a theoretical framework where one of the agents has biased beliefs.

The Price of Uncertainty in the Term Structure of Equity and Treasury Yields

The Price of Uncertainty in the Term Structure of Equity and Treasury Yields

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

Further Evidence on Greenspan’s Conundrum

During his February 2005 congressional testimony, Alan Greenspan identified what he termed a conundrum. Despite the fact that the Federal Open Market Committee (FOMC) had increased the federal funds rate 150 basis points since June 2004, the 10-year Treasury yield remained essentially unchanged.

Intermediary Balance Sheets and the Treasury Yield Curve

Intermediary Balance Sheets and the Treasury Yield Curve

Copper-to-Gold Ratio as a Leading Indicator for the 10-Year Treasury Yield

Copper-to-Gold Ratio as a Leading Indicator for the 10-Year Treasury Yield

Why Do the Mid-Term Maturity Treasury Yields Get More Attention?

Why Do the Mid-Term Maturity Treasury Yields Get More Attention?

Demand Shocks for Public Debt in the Eurozone

AbstractIn this paper we use intraday government bond futures price changes around German and Italian Treasury auctions to identify unexpected shifts in the demand for public debt. Estimates show that positive demand shocks lead to large negative movements in Treasury yields. Evidence shows significant spillover effects into Treasury bond, equity, and corporate bond markets of other eurozone countries. We find interesting differences in the effects of demand shocks between the two countries, consistent with the “safe‐haven” status of German bonds versus the “high‐debt” status of Italian Treasuries. Results suggest that these effects are stronger during periods of high financial stress.

Dimension Reduction via Penalized GLMs for Non-Gaussian Response: Application to Stock Market Volatility

We fit U.S. stock market volatilities on macroeconomic and financial market indicators and some industry level financial ratios. Stock market volatility is non-Gaussian distributed. It can be approximated by an inverse Gaussian (IG) distribution or it can be transformed by Box–Cox transformation to a Gaussian distribution. Hence, we used a Box–Cox transformed Gaussian LASSO model and an IG GLM LASSO model as dimension reduction techniques and we attempted to identify some common indicators to help us forecast stock market volatility. Via simulation, we validated the use of four models, i.e., a univariate Box–Cox transformation Gaussian LASSO model, a three-phase iterative grid search Box–Cox transformation Gaussian LASSO model, and both canonical link and optimal link IG GLM LASSO models. The latter two models assume an approximately IG distributed response. Using these four models in an empirical study, we identified three macroeconomic indicators that could help us forecast stock market volatility. These are the credit spread between the U.S. Aaa corporate bond yield and the 10-year treasury yield, the total outstanding non-revolving consumer credit, and the total outstanding non-financial corporate bonds.

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.

Monetary policy shocks and Bitcoin prices

The paper analyzes the impact of changes in U.S. monetary policy on Bitcoin prices. We find that a hypothetical unanticipated monetary tightening, which increases 1 bp on two-year Treasury yield, is associated with about a 0.25\% decrease in Bitcoin price on the Federal Open Market Committee(FOMC) meeting day. The accumulated effect is much stronger in the next few days after FOMC meeting. Quantile regression indicates a greater impact of monetary policy surprises on Bitcoin during the market boom.