Treasury Rates Research Articles

Gold’s Value as an Investment

Abstract This paper presents an approach for pricing gold from investors’ perspective. The model is based on no-arbitrage principles with minimal structural assumptions. There is no need to specify investor preferences. When fitted to match 10-year real U.S. Treasury rates, the model can replicate the salient fluctuations in the time series of gold prices since 2007. The model is also able to capture key patterns of CME Comex gold futures prices. The model implies that the majority of the value of gold is due to its role as an investment asset. (JEL G12, G13)

Parameter Estimation of Uncertain Differential Equations Driven by Threshold Ornstein–Uhlenbeck Process with Application to U.S. Treasury Rate Analysis

Uncertain differential equations, as an alternative to stochastic differential equations, have proved to be extremely powerful across various fields, especially in finance theory. The issue of parameter estimation for uncertain differential equations is the key step in mathematical modeling and simulation, which is very difficult, especially when the corresponding terms are driven by some complicated uncertain processes. In this paper, we propose the uncertainty counterpart of the threshold Ornstein–Uhlenbeck process in probability, named the uncertain threshold Ornstein–Uhlenbeck process, filling the gaps of the corresponding research in uncertainty theory. We then explore the parameter estimation problem under different scenarios, including cases where certain parameters are known in advance while others remain unknown. Numerical examples are provided to illustrate our method proposed. We also apply the method to study the term structure of the U.S. Treasury rates over a specific period, which can be modeled by the uncertain threshold Ornstein–Uhlenbeck process mentioned in this paper. The paper concludes with brief remarks and possible future directions.

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.

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.

The Complex Connectedness of Global Large-Scale Assets and the Visualization of Their Return Spillover Paths

Due to the increasing connectedness of international financial markets, the measurement of dynamic connectedness among large-scale assets has become a key component of modern financial risk regulation and asset allocation principles. We quantify the dynamic connectedness among large-scale assets and visualize the return spillover paths using cutting-edge complex network spillover measurement theory and physical complex network methods. For the sample period of January 2, 2018 to June 30, 2022, we calculate the daily returns for thirteen representative global large-scale assets. Then, we construct a time-varying parameter-vector autoregressive-stochastic volatility (TVP-VAR-SV) model and measure the time-varying spillover matrix of returns across large-scale assets. From our analyses, the 10-year U.S. treasury rate (shorted for the US10YR) and Brent oil are found to be the core subject matter of global assets. The US10YR has a significant impact on the commodity market. Moreover, there is a significant impact of foreign exchange on other global large-scale assets, and the spillover effects vary from one country to another. On the basis of the empirical findings, this paper proposes recommendations for financial regulators regarding risk monitoring and forward-looking investment recommendations for financial institutions and investors.

Inflation and Monetarism: An Analysis

Forced by the existential crisis permeated by Ukraine War, economists were compelled to change the Treasury rates with fuel crisis aggrandizing its position. It is true that such a change forced by a black swan effect has created ambivalence as a result of which hurried and uncompromising makeover in the economic sector emerged which was relatable in the purchasing power of the people. Antediluvian scan of literature indicates that analysis of a topic of this nature has not been attempted whereas there have been numerous contributions on inflation. Studies carried out on efficacy of altering the Repo rates world all over leads us to the important point that aspirations of neither the industry nor the people are met because of the apocryphal attitude of major players such as administrative machinery and financial policy think-tanks. With inflationary trends the disparity has been widening disproportionately. Attempting to negate the inflationary trend without attempting to rectify the fiscal deficit issue is like a fish out of water having very less chance of surviving. Thus, it was found that such a study would be not only enriching and useful but also interesting giving approbation to inflationary trends which are coming too often in the present world. The findings will give an arcane insight on to the relevance of practicable methods to deal with problem and support further research on the subject. The methodology used has been archetypal descriptive study.

Monetary policy rules and opinionated markets

The Fed and the market often disagree about the path of interest rates. We measure this disagreement using gaps between the Greenbook and the Blue Chip forecasts for future three-month Treasury rates. We decompose this disagreement using the Blinder–Oaxaca decomposition. Our empirical results show that different perceptions about the Taylor rule can contribute substantially to the disagreement.

Apply big data analytics for forecasting the prices of precious metals futures to construct a hedging strategy for industrial material procurement

AbstractThis study explores the critical factors affecting the prices of precious metals by using Granger causality tests through collecting financial market information and network traffic volume of Google Trend keywords and the traditional financial index. It establishes a forecasting model of precious metal price to assist the procurement of precious metals as raw materials which help to reduce material costs and control risks, thus lifting a company's profits. The results reveal that the prices of precious metals have a causality relation with their own exchange‐traded funds, US 10‐year treasury rate, and the Dow Jones Industrial Average but not Google Trends.

Omega portfolio models with floating return threshold

The Omega ratio is a risk-performance measure that specifies the upside profit and the downside loss by giving a return threshold (τ). Since there is no clear specification for the return threshold, this study advances the Omega models by incorporating various settings of the return threshold that reflect market dynamics: (1) the yields of Treasury securities, and (2) the negative CVaR value (or the gain) generated from equity markets. To ensure the practicality, our models consider the transaction costs and short selling. The empirical results that apply the two asset classes to rebalance portfolios over 17 and 20 years suggest that using the floating τ values, particularly the Treasury rates, realize higher out-of-the-sample performance and less downside loss. Our proposed approach is practical as the benchmark rates in fixed-income markets can be easily observed. The major findings hold for both non-robust and robust portfolio models. The tests of various preferences between seeking profit and controlling loss (δ) confirm robustness of the major conclusion.

Systemic Risk Contributions of Financial Institutions during the Stock Market Crash in China

This paper investigates the systemic risk contributions of each financial institution during the stock market crash in China using systemic risk beta. Based on the FARM-Selection (Factor Adjusted Regularized Model Selection) approach, we calculate the systemic risk beta, implying the importance of each financial institution during the stock market crash. We find that security firms are the main contributors to systemic risk. In addition, some macro variables have a significant influence on systemic risk, including changes in March Treasury rates and the AAA-rated bond and 10-year Treasury credit spreads. This paper provides an important perspective to identify the SIFIs (Systemically Important Financial Institutions) during the stock market crash.

Investment appraisal: Akwaaba university hostel project

Learning outcomes After completing this case, students should be able to: identify and compute relevant cash flows in relation to a real estate project and compute the net present value (NPV). Determine the target return or cost of capital (by looking at historical economic indicators). Design or formulate a sensitivity analysis to determine the drivers of the project value. Evaluate real estate and other investments taking qualitative and quantitative factors into consideration. Demonstrate the computation of a break-even rate to determine the minimum or maximum revenue or cost required for a project to be viable. Case overview/synopsis This case study is about the Golden Beak Securities Pension Fund that wanted to invest in a Hostel Project in one of the universities in Ghana. Most universities in Ghana faced an acute shortage of on-campus accommodation. Also, the Government of Ghana, in 2017, implemented a programme to make Senior High School in Ghana free. This was expected to increase the number of students who will enter the existing universities. The project was therefore seen as strategic, as it would help ease the pressure of on-campus accommodation while providing diversification for the pension fund. As part of the investment committee’s (IC) quest to improve the skill set available to it, especially in relation to real estate investments, Esi Abebrese was appointed as one of the members of the IC of GSB. Her main task was to collect information on key macroeconomic variables, as well as granular information on project costs and revenues and conduct investment appraisal. Esi was scheduled to make a presentation to the IC on the 15th of October 2019 following which the Committee will debate and make a decision. The project had an estimated cost of GH¢52m with a total number of 3,424 student beds and ancillary facilities. Undertaking the project required moving funds from investments in money market securities with one of the banks in Ghana. The investments in the money market securities were currently yielding about 16% a year. The determination of the cost of capital was critical and Esi and Nana eventually settled on a long-term weighted average cost of capital of 14%. This was after considering the trend of inflation, monetary policy rates, treasury rates, stock market returns and a report on returns on commercial real estate properties in Ghana. An exit capitalisation rate of 20% was also estimated for the purposes of determining the value of the property at the end of the investment horizon. Esi also obtained estimates of cost and revenue for the project and proceeded to carry out a feasibility analysis on the project. This consisted of an NPV analysis and sensitivity analysis on various factors to determine the drivers of the project value. The IC had to take several factors (both quantitative and qualitative) into consideration before making a decision. Esi believed that these factors included the diversification of the fund’s assets, the return on investment, potential oversupply of hostel accommodation, the social responsibility of providing student accommodation and the impact of any prolonged shutdown of the university. Complexity academic level Masters/advanced undergraduate. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Subject code CSS 1: Accounting and Finance.

Make-wholes in sovereign bonds

Multiple researchers have observed a dramatic shift in US corporate bonds starting roughly in the mid-1990s and extending to the current period: from fixed premium call options to flexible rate call options.1 This flexible rate call provision was known initially as the ‘doomsday call’ and is now referred to as ‘make-whole’ call. It originated in the Canadian corporate bond market in the mid-1980s and migrated to the USA in the mid-1990s.2 Today, it is ubiquitous in the corporate bond market. A fixed premium call typically specified a percentage premium over the principal amount as a function of the remaining time on the bond (eg 110 per cent, if called five years prior to maturity year or 108 per cent if redeemed four years prior and so on).3 A ‘make-whole’ call, instead, measures the amount to be paid as a discounted value of future amounts, with the discount rate being a small spread over the prevailing risk-free rate (eg the discount rate might be the US Treasury rate plus 50 basis points).4

A Study on Estimation and Prediction of Vector Time Series Model Using Financial Big Data (Interest Rates)

Due to the global economic downturn, the Korean economy continues to slump. Hereupon the Bank of Korea implemented a monetary policy of cutting the base rate to actively respond to the economic slowdown and low prices. Economists have been trying to predict and analyze interest rate hikes and cuts. Therefore, in this study, a prediction model was estimated and evaluated using vector autoregressive model with time series data of long- and short-term interest rates. The data used for this purpose were call rate (1 day), loan interest rate, and Treasury rate (3 years) between January 2002 and December 2019, which were extracted monthly from the Bank of Korea database and used as variables, and a vector autoregressive (VAR) model was used as a research model. The stationarity test of variables was confirmed by the ADF-unit root test. Bidirectional linear dependency relationship between variables was confirmed by the Granger causality test. For the model identification, AICC, SBC, and HQC statistics, which were the minimum information criteria, were used. The significance of the parameters was confirmed through t-tests, and the fitness of the estimated prediction model was confirmed by the significance test of the cross-correlation matrix and the multivariate Portmanteau test. As a result of predicting call rate, loan interest rate, and Treasury rate using the prediction model presented in this study, it is predicted that interest rates will continue to drop.

Gold's Value as an Investment

For investors, gold is an asset without a yield that is attractive in times of low and negative real interest rates. Gold also has an embedded put option because investors can sell it to those who value its use as jewelry or as a productive input. This paper presents an approach for pricing gold from investors' perspective based on no-arbitrage principles. There is no need to specify investor preferences and the main input for pricing gold is a term structure model for real interest rates. The model implies that on average more than half of the value of gold is due to its role as an investment asset. When fitted to match 10-year real US Treasury rates the model can replicate the salient fluctuations in the time series of gold prices since 2007.

Predicting Recession Probabilities Using Term Spreads: New Evidence from a Machine Learning Approach

The literature on using yield curves to forecast recessions typically measures the term spread as the difference between the 10-year and the three-month Treasury rates. Furthermore, using the term spread constrains the long- and short-term interest rates to have the same absolute effect on the recession probability. In this study, we adopt a machine learning method to investigate whether the predictive ability of interest rates can be improved. The machine learning algorithm identifies the best maturity pair, separating the effects of interest rates from those of the term spread. Our comprehensive empirical exercise shows that, despite the likelihood gain, the machine learning approach does not significantly improve the predictive accuracy, owing to the estimation error. Our finding supports the conventional use of the 10-year--three-month Treasury yield spread. This is robust to the forecasting horizon, control variable, sample period, and oversampling of the recession observations.

Benchmark interest rates when the government is risky

Since the global financial crisis, interest rate swap rates, which represent future uncollateralized interbank borrowing, have fallen below maturity-matched Treasury rates. This is surprising, because US Treasuries, which are deemed expensive because of superior liquidity and safety, should produce yields that are lower than those of swap rates. We show, by no-arbitrage, that sovereign default risk explains negative swap spreads even without frictions such as balance sheet constraints, convenience yield, and hedging demand. We support this explanation with an equilibrium model that jointly accounts for macroeconomic fundamentals and the term structures of interest and US credit default swap rates.

Drivers of Mortgage Spreads across the Business and Housing Cycle

This article provides a review of the underlying drivers of mortgage spreads at various points of the housing and economic cycle, with an in-depth look at the spreads between mortgage-backed securities (MBS) and Treasury rates and between mortgage and MBS rates. The authors provide evidence of the role of prepayment and credit-related risks, as well as lender capacity issues and large-scale asset purchases by the Federal Reserve in explaining these spreads. They also investigate recently observed differences in rates across purchase and refis as additional evidence of the role of capacity constraints in the persistent widened spreads during the ongoing COVID-19 outbreak. TOPICS:MBS and residential mortgage loans, real estate, legal/regulatory/public policy Key Findings • Mortgage spreads, especially the primary–secondary spread (mortgage rate versus current coupon yield on MBS), remain historically wide following the COVID outbreak in the US. • Increases in liquidity in the mortgage-backed securities (MBS) market coinciding with the Federal Reserve’s increased MBS purchases have historically been correlated with declines in the secondary spread (current coupon yield on MBS versus Treasuries), consistent with the post-COVID surge in Fed MBS purchases and subsequent decline in the secondary spread. • Increases in the primary–secondary spread have historically been correlated with increased capacity constraints faced by lenders, for instance, measured by increases in production per mortgage-related employee. The persistent widened primary–secondary spread in the aftermath of COVID-19 is consistent with this historical pattern.

Smart Derivatives Contracting: Automating Interest Rate Swaps in the Over-the-Counter (OTC) Market with the DAML

The over-the-counter (OTC) market sees the majority of trading volume in the finance industry, yet it remains vastly unregulated. Inherent to its nature are issues with transparency, efficiency and security, which have been barely addressed by legislators post the 2008 crisis. We propose that the utilization of Smart Contracts in the OTC market and creation of “smart derivatives”, solves the majority of issues that are associated with the market. While other researchers have debated on this matter and proposed the use of Smart Contracts in OTC markets, there are no papers offering an example of practical implementation. We selected interest rate swaps (IRS) as the test-case derivative for implementation in the form of a smart contract. Having unpacked the mathematical and theoretical framework behind structuring and pricing IRS, we produced a Vasicek Simulated Yield Curve from the Ten Year Constant Maturity Treasury Rates and found that the curve was inverted, which, combined with a characteristic LIBOR Simulated Yield Curve pointed to an increasingly negative swap spread for longer maturities that was demonstrated with a plotted Swap Spread. The parameters of the model were derived from live market data through Maximum Likelihood Estimation with Monte Carlo Simulation of Euler Scheme discretized Vasicek instantaneous rates. We concluded that short term interest rate swap contracts were preferable and implemented a one-year Interest Rate Swap by instantiating the DAML Ledger Sandbox. The implementation offers the likely innovation of merging a Swap Execution Facility and a Swap Data Repository into one platform, ensuring end-to-end automation, as well as providing the benefits of visibility, while preserving anonymity, improving efficiency and security in the market.

Financial Crises and the Transmission of Monetary Policy to Consumer Credit Markets

How does the health of creditors affect the pass-through of monetary policy to households? In a financial crisis, asset losses among creditors can either dampen or amplify the effects of monetary policy on lending, depending on how these losses and policies interact with financial frictions. Frictions such as leverage constraints my hinder creditor responses, however easing may instead alleviate frictions that would otherwise constrain lending. Using data on the universe of US credit unions, I document that asset losses increase the sensitivity of consumer credit to monetary policy. Identification exploits plausibly exogenous variation in asset losses and high frequency identification of monetary policy shocks. I find that a one standard deviation asset loss increases the response of credit union lending to a 10 basis point fall in the two-year Treasury rate from a 0.86 to 1.15 percentage point increase. The estimates imply that constraints on monetary policy become more costly in a financial crisis characterized by creditor asset losses and that an additional benefit of monetary easing is that it weakens the causal, contractionary effect of asset losses.