10-year Treasury Research Articles

Decoding Market Efficiency: A Multifractal Approach to Financial Market Dynamics

Financial markets were traditionally analyzed using the Efficient Market Hypothesis (EMH), which argued that asset prices fully reflected all available information. This led to the belief that price movements were random, making it impossible to consistently outperform the market. However, emerging research had highlighted that markets could exhibit multifractality, where inefficiencies existed across different time scales, challenging the traditional EMH framework. Our study built on these insights by investigating the market efficiency of several asset classes, including stock market indices, cryptocurrencies (Bitcoin, Ethereum, USDT, and Binance), commodities (gold and crude oil), fixed income (U.S. 10-Year & 30-Year Treasury Bonds), and foreign exchange (EUR/USD & GBP/USD). Utilizing the Multifractal Detrended Fluctuation Analysis (MFDFA) method, we analyzed the multifractal properties and efficiency of each asset class. The results revealed that traditional assets exhibited monofractality, adhering more closely to the EMH. In contrast, cryptocurrencies displayed significant inefficiencies, likely due to their volatility, market structure, and susceptibility to external shocks. The findings provided valuable insights into portfolio diversification and risk management, suggesting that inefficiencies in certain asset classes might present both opportunities and risks for investors.

Price Discovery Function of Government Bond Futures: Evidence from Chinese 10-Year Treasury Note Markets

Utilizing the data of the futures and spot prices of Chinese 10-year Treasury notes from 2015 to 2022, this paper examines the price discovery mechanism on Chinese Treasury bond markets. We find that there is a significant cointegrating relationship between the futures price and the spot price of Chinese 10-year Treasury notes, and that in this long-run equilibrium the futures price has a one-way predicting effect on the spot price. We also find that the proportions of the variations in the spot price and the futures price explained by a shock or impulse in the futures market is higher in a later period than in a comparable earlier period, indicating that the contribution of the futures market to the price discovery has been increasing gradually. These results suggest that the price discovery function of futures markets is significant and has become more important to market efficiency. Furthermore, the disparities of our 10-year-note results from the previous 5-year-note results indicate that the price discovery function varies across underlying assets.

Identifying Interest Rate Transmission Mechanism under a Bayesian Network

This study examines causal relationships among various short- and long-term interest rates in the Korean financial market to identify transmission channels. Monthly time series data from January 2015 to February 2024 were used, covering nine interest rates, including call rates, commercial paper (CP) rates, bank lending rates, and Treasury bond yields of different maturities. The study employs a Bayesian network to identify an acyclic causal structure between interest rates alongside a vector error correction model (VECM) to capture long-term equilibrium relationships and short-term dynamics. The findings reconfirm the traditional call rate transmission mechanism, aligning with conventional monetary policy views, demonstrating the call rate’s significant influence on bank lending rates, which affects corporate bond spreads and CP rates. Additionally, it reveals that 10-year Treasury bond yields form an independent interest rate transmission pathway, a finding not previously identified in the literature. These results underscore the need for coordinated monetary and fiscal policies due to the distinct transmission pathways of Treasury yields for sustainable macroeconomic management and growth.

Dependence of Investing Strategies on Expected Discounted Rate

The Discounted Cash Flow model is a cornerstone in the fields of corporate finance and investment valuation, serving as a crucial decision-making tool for stock valuation. Driven by the motivation of searching for the dependence of different investing strategies on expected discounted rates, the article compares the valuations of two types of hypothetical net free cash flows under the same fluctuation of discounted rates, representing the stable and aggressive growth of companies. Modeling studies are then supported by regressing different representative indexes on the yield-to-maturity of China's 10-year treasury bond. A larger dependence on treasury yield is shown in valuable stocks than in grow-up stocks. The work uses a hypothetical model to explain this phenomenon from the perspective of investors. Valuable stocks investments give greater weight to assessments of the macro environment because of lower sensitivity and predictable patterns, whereas grow-up stocks investors base their careful assessment of the volatile valuation of start-ups less on the standard rate, even though it implies a risk-free rate.

The Impact of the Monetary Policy of the US Federal Reserve on the Ratio of the US Dollar To the Japanese Yen in the Foreign Exchange Market during 2015-2023

The information base of the study is the time series of the daily exchange rate of the currency pair USD/JPY, the US 10-year Treasury bond (TB 10), the Japanese government bond 10-year (JGB 10), the volatility index (VIX) of the Chicago Board Options Exchange (CBOE), which calculated by Bloomberg. Study period: December 1, 2015 (the date the Federal Reserve raised interest rates, which initiated a new regime – began easing its ZIRP zero interest rate policy) to December 29, 2023. The relationship between monetary policy and the currency pair USD/JPY is evaluated using 3 approaches: 1) a rally dynamic system based on the standard theory of nonlinear differential equations aimed at eliminating psychological noise (1/f noise, pink noise, trend noise with spectrum capacity); 2) regression of instrumental variables with heteroskedasticity aimed at managing unobservable sources of variability; 3) an event tracking method to measure anomalous asset price response, analyzing the daily price action of USD/JPY to detect market-related fluctuations in the exchange rate and separate the impact of general market trends from related differences in interest rates. The study found that the US Federal Reserve’s (Fed’s) monetary policy only occasionally affects the USD/JPY exchange rate, and it changes mainly when monetary policy deviates from expected future trajectories. Within the studied period, this happened only 3 times: growth on December 16, 2015; emergency rate reduction due to COVID-19 on March 15, 2020; and the first increase of 75 basis points on June 15, 2022 (the article estimates the impact of each of these episodes on the exchange rate). Statements by the Fed on monetary policy affect the price dynamics of the USD/JPY pair in the short term (since short-term psychological expectations play a decisive role in shaping the dynamics of the currency market), but not often (if the statement on monetary policy is consistent with market forecasts, it is practically will not affect exchange rates). The Fed’s adoption of tools such as “forward guidance” and official statements to inform the future course of monetary policy indirectly affects the price dynamics of USD/JPY. The price action of USD/JPY was driven by the fundamentals of the difference in interest rates between the US and Japanese bonds, which are influenced by the monetary policy of the US Federal Reserve and the Bank of Japan (BoJ). In 2023, it could be hard to attempt to challenge FX intervention except to curb “excess volatility and disorderly movements in exchange rates” like the Minister of Finance’s (MoF) 2022 intervention.

Modeling Historical Volatility of 10-year Chinese Treasury Bond Futures: A Comparative Analysis of MLE and MCMC Approaches

Modeling Historical Volatility of 10-year Chinese Treasury Bond Futures: A Comparative Analysis of MLE and MCMC Approaches

Agricultural commodities market reaction to COVID-19

This study examines the volatility transmission mechanism in agricultural futures returns during crisis periods, with a specific focus on the COVID-19 pandemic. The research employs the Markov Switching model to analyze the market dynamics of key agricultural commodities—cotton, sugar, rice, wheat, and corn. The study period is divided into three phases: Pre-COVID, COVID, and Post-COVID, allowing for a comprehensive analysis of market trends and shifts in response to the pandemic. Key findings reveal significant disruptions and heightened volatility in these markets during the COVID-19 period, with notable deviations from traditional market behaviors. The research reveals how various macroeconomic factors, such as the S&P 500, US 10-year Treasury bills, Brent crude oil prices, and the US Dollar Index, distinctly affect these commodity markets. Crucially, it sheds light on the dual nature of these markets, demonstrating both their resilience and susceptibility to worldwide disruptions, along with their recovery patterns following the pandemic. The policy implications of these findings are crucial for investors and policymakers. The study provides insights into managing and mitigating the impacts of global crises on essential commodities, emphasizing the need for robust strategies to navigate future market uncertainties. Additionally, the research contributes to the broader discourse on the interplay between macroeconomic conditions and agricultural market dynamics, offering valuable perspectives for informed decision-making in these sectors.

The Effects of the Federal Reserve Chair’s Testimony on Treasury Interest Rates

Communication by the Federal Reserve is important for the conduct of monetary policy. We study how one form of Federal Reserve communication, the congressional testimony by the Chair of the Board of Governors (the Fed Chair), affects interest rates on 2-year and 10-year Treasury Notes. We study three types of Fed Chair testimony: the first day of Monetary Policy Report testimony, the second day of Monetary Policy Report testimony, and testimonies not associated with the Monetary Policy Report but that still relate to monetary policy. We find that the average size of interest rate changes is largest around first-day Monetary Policy Report testimonies and smallest around second-day Monetary Policy Report testimonies. We also document that the sizes of interest rate changes can vary over time and often correspond to the level of the federal funds rate.

Uncertainty about interest rates and crude oil prices

The yield on the 10-year U.S. Treasury Note is among the most cited interest rates by investors, policymakers, and financial institutions. We show that the 10-year Treasury yield’s forward-looking volatility, a VIX-style measure that is a proxy for uncertainty about future interest rates, is a useful state variable capable of predicting the returns and volatility of crude oil prices over the near term. Using monthly data from 2003 to 2020, we document that higher implied volatility in the 10-year U.S. Treasury derivatives market predicts declining oil prices and higher forward-looking volatility in those prices. Our results are robust to different subsamples and various empirical designs.

Recession Prediction Using Multiple Machine Learning Methods and Historical Economic Data

This study explores the application of machine learning methods to enhance economic recession prediction in the UK and USA, considering the limitations of traditional methods. Various models, including Logistic Regression, Linear Discriminant Analysis, K Nearest Neighbors, Decision Tree Classifier, Gaussian Naive Bayes, Support Vector Classifier, Neural Network, RTC, Long Short-Term Memory, Convolutional Neural Network, and XGBoost, were assessed using economic data since 1900. The UK data encompassed GDP, unemployment rate, inflation, FTSE 100 index, yield curve, and debt levels, while the USA utilized the 50-day simple moving average of 10-year treasury rates minus the 50-day simple moving average of 3-month treasury rates. Performance evaluation involved averaged F1, recall, and accuracy over 100 iterations, with confusion matrices illustrating model predictions against actual events. Long Short-Term Memory excelled with recall and F1 values of 0.96 and 0.97, accurately identifying 11 in 12 Positive USA events. K Nearest Neighbours, Decision Tree Classifier, Random Forest Classifier, and XGBoost demonstrated good results, with recall ranging from 0.99 to 0.75, F1 from 1.0 to 0.69, and correctly identifying 2 in 3 Positive events. Conversely, Logistic Regression, Gaussian Naive Bayes, and Neural Network exhibited less reliable predictions. Linear Discriminant Analysis, Support Vector Classifier, and Convolutional Neural Network were completely inadequate. Using recent data, most models predicted the USA avoiding recession in 2023-24, but the probability increased to 0.5 by mid-2023, then decreased. Logistical Regression, Linear Discriminant Analysis, and Long Short-Term Memory initially predicted no recession, but the probability rapidly increased to between 0.83 and 0.97 by April 2024. While recession avoidance is plausible, modelling indicates an escalating risk. The results underscore the utility of machine learning in recession prediction, emphasizing the importance of diverse training datasets. Algorithmic performance varied, with neural network models, particularly Long Short-Term Memory and XGBoost, proving most accurate. Further enhancements in performance necessitate refining training datasets and leveraging advanced models like Long Short-Term Memory.

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.

The 2016 US presidential election, opinion polls and the stock market

Purpose The purpose of this paper is to study the impact of the 2016 presidential election polls on the stock market. Design/methodology/approach The empirical model includes daily stock returns as the dependent variable and past asset prices, 10-year treasury rates, opinion polls and VIX (market uncertainty) as explanatory variables with a one-year lag. The model was estimated using two sets of daily polling data: from July 1, 2015, to November 8, 2016, and from June 1, 2016, to November 8, 2016. Additional descriptive statistics, such as means and standard deviations, were also calculated. Findings The estimated results did not reveal any statistically significant effects of opinion polls in favor of one candidate over another on stock returns. Simple statistical tests, however, show that the market performed better when Trump held a polling advantage over Clinton. Originality/value To the best of the authors’ knowledge, this is the only study that has examined the effects of the 2016 presidential election polls on the US stock market. This study adds value to the understanding of the relationship between election polls and the stock market in the USA.

The Relationship Between Exchange Rates and Interest Rates: Evidence from the U.S. and Europe

We analyze the relationship between exchange rates and interest rates for a sample of exchange rates between the U.S. dollar and the euro, as well as 10-year Treasury rates for both countries. The short-term relationship is modeled using ARMA, GARCH, and TGARCH models, and the study finds that the European 10-year Treasury bond has a significant effect on the exchange rate between the two countries while the U.S. 10-year Treasury bond does not have that effect. The long-term relationship is studied using the ARDL-ECM model, and the results show that between the U.S. 10-year Treasury rate, the European 10-year Treasury rate and the exchange rate during the financial crisis.

Copper-to-gold ratio as a leading indicator for the 10-Year Treasury yield

Preliminary univariate and multivariate regressions, visual inspections, various relative entropy probes, and complementary Pearson correlation tests and Welch’s t-tests all suggest that the copper-to-gold ratio often embeds rather short-termed (up to a few days) yet credible information about the 10-year U.S. Treasury yield. This phenomenon has been more noticeable in recent years and in times where no major economic shocks have developed. False signals in this leading indicator, however, are not uncommon, and they emerge mostly around global macroeconomic tremors. As declared by different market participants, in the absence of macroeconomic turbulences, the copper-to-gold ratio tends to be tightly coordinated (up to a few day lags) with the 10-year U.S. Treasury yield, and as such, it can serve as a momentary leading indicator for the latter, although not always and not with complete precision.

The Impact of U.S. Quantitative Easing (QE) Announcements on Indian Government Bond Yields

This study investigates the impact of U.S. Quantitative Easing (QE) announcements on Indian Treasury yields. Two outstanding channels of spillover effects on bond yields documented in the existing literature are signalingchannel and portfolio balance channel. This study decomposes Indian Treasury yields into yield expectationsand risk premia to measure spillover effects of U.S. QE announcements. The impact on yield expectationmeasures signaling effect while the impact on risk premia measures portfolio balance effect. It is observed that FOMC announcements of Federal Reserve’s Quantitative Easing (QE) policy treated as shocks to Indian government bond yields. To investigate the announcement effects on Indian government Bond yields, event study methodology is used to capture the change in the bond yields, yield expectation and risk premia of Indian bond market around that time especially during the first round of Quantitative Easing (QE1) policy announcement periods in one-day and two-day window period. To support event study results regression analysis method is implemented and found robust evidence supporting larger signaling effect than the portfolio balance effect. At last, this study uses Dynamic Nelson-Siegel (DNS) yield curve model to compute the relationship between the U.S. and Indian Bond market. DNS model involves two-step estimation using VAR regression on Indian government bond yields with U.S. 10-year Treasury yield changes as an exogenous variable. The statistical result of DNS estimation shows that U.S. 10-year Treasury yield change affects the Indian long-term bond yield during the financial crisis period.

The Contagion of International Crises: Implications of Inflation and Investor Sentiment on Stock and Treasury bond Returns

Abstract In recent years, the stock market has faced numerous challenges generated by unexpected events that had a major impact on the global economy. Thus, through this paper, which is based on the analysis of the relationships between inflation, stock market yields and treasury bond yields in the context of international crises, it is intended to illustrate the possible effects of inflation on stock market yields and treasury bonds, as well as to compare the performance of stock market indices and treasury bonds in relation to the corresponding inflation. The main objective of this paper was to capture the complex relationships between inflation, stock market index returns and treasury bond yields over a period of ten years by using a series of advanced statistical techniques. Through the advanced techniques and models implemented, it is proposed to evaluate the correlation and causality between these economic variables, the goal being to determine the degree of influence these correlations have on the investor's sentiment in choosing the investment portfolio. The originality of this paper is reflected by the case study carried out on the daily returns of stock indices from America and Central-Eastern Europe, as well as the variable values of the consumer price index and long-maturity treasury bonds, over a period marked by events with a high impact on the global economy. The results obtained show that in the analyzed time series there is a close connection between the contagion of stock markets, inflation and 10-year treasury bonds, and the strong impact tends to be unidirectional, from the changes recorded in the level of inflation to stock index returns and 10-year treasury bond yields, as a result of the evolution of the geopolitical conflict and of the state of the global and regional economies, but not vice versa.

How does bubble risk propagate among financial assets? A perspective from the BSADF-vine copula model

In this paper, the BSADF method is used to construct the bubble net value sequence of 11 mainstream financial assets, and the vine copula model is used to analyze the characteristics of the bubble-related structure. First, we use the BSADF method to measure the bubbles of various financial assets. The results show that since the outbreak of COVID-19, most types of assets have experienced a sharp bubble rise and their longest bubble period as central banks have implemented quantitative easing policies. Furthermore, this article reveals the path characteristics of bubble risk transmission in different assets using the vine copula model. The R vine copula is found to be best for analyzing the tail dependence structure. The S&P 500 and the international gold price occupy the hub position in the bubble dependence structure of various financial assets. The Shanghai Composite Index and the US Dollar Index can act as relays and accelerators in the transmission of bubble risk across the financial system. Finally, we explore the tail dependence structure between financial assets. The results indicate that gold can be a good hedge against the bubble risk of the Hang Seng Index and WTI crude oil futures. The COVID-19 pandemic and accommodative monetary policies by governments can create conditions for bubble risk to spread between assets such as Bitcoin and China’s 10-year Treasury bonds and between the US Dollar Index and the S&P 500.

Portfolio Analysis and Investment Value Based on US Stock Market

An investment portfolio can contain a variety of assets, including cash, stocks, bonds, financial derivatives, precious metals, and cash. You shouldn't invest all of your money in the same thing, similar to the adage "don't put all your eggs in one basket." Put your eggs in a variety of baskets so that even if one breaks, they will remain safe. In this study, five equities—Eli Lilly, Amazon, FedEx, J.P. Morgan Chase Bank, and Pfizer—are constructed using the Markowitz Mean-Variance Model and Effective Frontier. The portfolio is built using data from these five stocks during the previous three years. Given that stocks can be shorted, the portfolio has a Sharpe Ratio of 9.07%, a yield of 2.99%, and a ceiling of negative 1 for each stock. The risk of this best answer, which was determined by solving combinations, is 9.63%. The usual 10-year Treasury bond's average return is 2.12%, which is less than the ideal yield.

The effects of Trump’s trade war on U.S. financial markets

We study the impact of President Trump’s trade war on U.S. financial markets by integrating trade war–related news into a single “trade war shock,” based on a structural vector autoregressive model identified via event-day heteroscedasticity. We find that the identified trade war shock not only significantly affects financial variables such as stock prices and Treasury yields but also explains at least 30% of their variation on impact. The main channels through which the trade war shock affects the S&P 500 index and 10-year Treasury yields are the growth news channel, through which the trade war shock changes investors’ growth expectations, and the risk premium channel, through which the trade war shock generates shifts in risk appetite or economic uncertainty. Additional analyses that employ stock prices in the S&P 500 index to explore the role of firms’ global value chain exposure during the trade war reveal that firms with high output exposure to China’s market suffer significantly more from the trade war, while input exposure does not cause a persistent significant difference.

Li-Cycle expects federal loan

The battery recycling start-up Li-Cycle has received a $375 million conditional loan commitment from the US Department of Energy in support of its planned processing facility in upstate New York. Li-Cycle already accepts lithium-ion batteries for recycling at four sites and uses a process called submerged shredding to reduce the batteries to “black mass,” an intermediate containing the valuable metals. The processing facility, in Rochester, will extract lithium carbonate, nickel sulfate, and cobalt sulfate from black mass at a capacity equivalent to 90,000 metric tons of used battery material. The DOE loan comes from Inflation Reduction Act funds and will carry the same relatively low interest rate as 10-year Treasury notes.