The yield curve, also called the term structure of interest rates, refers to the relationship between the remaining time-to-maturity of debt securities and the yield on those securities. Yield curves have many practical uses, including pricing of various fixed-income securities, and are closely watched by market participants and policymakers alike for potential clues about the markets perception of the path of the policy rate and the macroeconomic outlook. This page provides daily estimated real yield curve parameters, smoothed yields on hypothetical TIPS, and implied inflation compensation, from 1999 to the present. Because this is a staff research product and not an official statistical release, it is subject to delay, revision, or methodological changes without advance notice.

Ten-Year TIPS Yields versus Real Yields is a part of the Inflation Expectations indicator of the Federal Reserve Bank of Cleveland.

The yield on 10 Year TIPS Yield eased to 1.74% on October 10, 2025, marking a 0.05 percentage points decrease from the previous session. Over the past month, the yield has edged up by 0.09 points, according to over-the-counter interbank yield quotes for this government bond maturity. This dataset includes a chart with historical data for the United States 10 Year TIPS Yield.

These rates are commonly referred to as "Real Constant Maturity Treasury" rates, or R-CMTs. Real yields on Treasury Inflation Protected Securities (TIPS) at "constant maturity" are interpolated by the U.S. Treasury from Treasury's daily real yield curve. These real market yields are calculated from composites of secondary market quotations obtained by the Federal Reserve Bank of New York. The real yield values are read from the real yield curve at fixed maturities, currently 5, 7, 10, 20, and 30 years. This method provides a real yield for a 10 year maturity, for example, even if no outstanding security has exactly 10 years remaining to maturity. Dataset updated daily every weekday.

Context

GDPC1 - time series representing real GDP measured quarterly spanning from 1947 to 2018 in billions of dollars, adjusted for inflation and chained to 2012 dollars

DGS2 - time series representing 2Y treasury constant maturity rate measured daily in % spanning from 1976 to 2019

T10Y2Y - time series representing 10Y treasury yields minus 2Y treasury yields measured daily , spanning from 1976 to 2018

USREC - time series represents when the US experienced recession spanning from 1854 to 2018 measured daily. A '1' indicating that the US is in a period of recession and '0' indicating that the US is not in a period of recession

Content

Each CSV file has only two columns, the first column representing the date and the second column representing the value of the time series as indicated above. Missing values are represented by '.'

Acknowledgements

All data was downloaded from the website of the Federal Reserve Bank of St. Louis (https://fred.stlouisfed.org/)

Inspiration

• Recently it was reported that bond yields inverted leading some to fear economic recession in the near future. Is there truth to these fears?

• Is there any relationship between real GDP and bond yields

• Can you use bond yields to predict real GDP?

• Is there any validity to the notion that bond yield inversions are leading indicators of economic recession?

• What other datatypes besides bond yields can we use to improve predictions of real gdp?

The yield on Germany 10Y Bond Yield eased to 2.63% on October 10, 2025, marking a 0.07 percentage points decrease from the previous session. Over the past month, the yield has fallen by 0.02 points, though it remains 0.36 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Germany 10-Year Bond Yield - values, historical data, forecasts and news - updated on October of 2025.

Real-time dataset of 38+ US economic indicators including GDP, inflation, unemployment, Treasury yields, and housing data from Federal Reserve (FRED)

The UK Inflation Sentiment Index tracks how financial media narratives around inflation evolve and how these shifts signal market unease before official data releases or policy moves. By analysing tone, intensity, and coverage volume across thousands of headlines, the dataset provides early insight into credibility risks for the Bank of England and long-dated gilts. Our latest analysis shows UK inflation sentiment turned upwards ahead of the July CPI release, accurately foreshadowing the upside surprise and subsequent surge in 30-year gilt yields above 5.6%. The dataset highlights how inflation sentiment doesn’t just mirror events – it anticipates them, acting as a forward-looking risk signal. For investors, the data offers a direct lens into how market narratives around inflation, policy credibility, and fiscal sustainability influence long-end yields. This makes it a valuable input for portfolio strategy, sovereign risk assessment, and stress testing.

Replication code and data for: A Portfolio-Balance Model of Inflation and Yield Curve Determination. Instructions reside in the readme.txt file

This paper models the dynamics of Chinese yuan–denominated long-term interest rate swap yields. It shows that the short-term interest rate exerts a decisive influence on the long-term swap yield after controlling for various macrofinancial variables, such as core inflation, the growth of industrial production, the percent change in the equity price index, and the percentage change in the Chinese yuan exchange rate. The autoregressive distributed lag approach is applied to model the dynamics of the long-term swap yield. The findings reinforce and extend John Maynard Keynes’s conjecture that in advanced countries, as well as emerging market economies such as China, the central bank’s actions have a decisive role in setting the long-term interest rate on government bonds and over-the-counter financial instruments, such as swaps.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

S&P 500: A Bull or a Bear?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This dataset contains monthly and quarterly time-series data from 2012 to 2024 for Indonesian sovereign credit risk (∆CDS), global volatility (VIX), international equity proxy (MSCI World Index), Indonesia Stock Exchange Composite Index (IHSG), exchange rate (USD/IDR), and inflation. The dataset supports the empirical analysis in the article titled “The Interaction Between Sovereign Risk, Global Volatility, and Domestic Stock Returns: An Indonesian Case Study.

This work was conducted by the Diverse Rotations Improve Valuable Ecosystem Services (DRIVES) project, based in the USDA-ARS Sustainable Agricultural Systems Lab in Beltsville, MD. The DRIVES team compiled a database of 20-plus long-term cropping systems experiments in North America in order to conduct cross-site research. This repository contains all scripts from our first research paper from the DRIVES database: "Rotational complexity increases cropping system output under poorer growing conditions," published in One Earth (in press). This analysis uses crop yield and experimental design data from the DRIVES database and public data sources for crop prices and inflation. This repository includes limited datasets derived from public sources or lacking connection to site IDs. We do not have permission to share the full primary dataset, but can provide data upon request with permission from site contacts.The scripts show all data setup, analysis, and visualization steps used to investigate how crop rotation diversity (defined by rotation length and the number of species) impacts productivity of whole rotations and component crops under varying growing conditions. We used Bayesian multilevel modeling fit to data from 20 long-term cropping systems datasets in North America (434 site-years, 36,000 observations). Rotation- and crop-level productivity were quantified as dollar output, using price coefficients derived from National Agriculture Statistics Service (NASS) price data (included in repository). Growing condtions were quantified using an Environmental Index calculated from site-year average output. Bayesian multilevel models were implemented using the 'brms' R package, which is a wrapper for Stan. Descriptions of all files are included in README.pdf.

Record-Setting Auction Data provides record highs and lows from U.S. Treasury auctions. This includes lowest and highest rates/yields, highest offering amount, and highest bid-to-cover ratios as well as the dates for these record-setting auctions. The data also indicates the security type and term. Security types include Treasury Bills, Treasury Notes, Treasury Nonds, Cash Management Bills (CMBs), Floating Rate Notes (FRNs), and Treasury Inflation-Protected Securities (TIPS). Security terms range from a few days for CMBs to 30-year securities. The U.S. Treasury uses an auction process to sell these marketable securities and determine their rate or yield. Marketable securities can be bought, sold, or transferred after they are originally issued.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

The yield on 30 Year TIPS Yield rose to 2.47% on September 30, 2025, marking a 0.01 percentage points increase from the previous session. Over the past month, the yield has fallen by 0.18 points, though it remains 0.57 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. This dataset includes a chart with historical data for the United States 30 Year TIPS Yield.

The yield on Japan 10Y Bond Yield held steady at 1.70% on October 10, 2025. Over the past month, the yield has edged up by 0.12 points and is 0.74 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Japan 10 Year Government Bond Yield - values, historical data, forecasts and news - updated on October of 2025.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains the annually updated global multi-asset market portfolio of Doeswijk, Lam, and Swinkels (2014). The latest update contains data until 31 December 2023.The market portfolio contains important information for purposes of strategic asset allocation. One could consider it a natural benchmark for investors. The authors composed the invested global multi-asset market portfolio for 1990–2012 by estimating the market capitalization for equities, private equity, real estate, high-yield bonds, emerging-market debt, investment-grade credits, government bonds, and inflation-linked bonds. They also used an expanded period (1959–2012) for the main asset categories: equities, real estate, nongovernment bonds, and government bonds.

The yield on China 10Y Bond Yield eased to 1.85% on October 10, 2025, marking a 0.08 percentage points decrease from the previous session. Over the past month, the yield has edged up by 0.04 points, though it remains 0.28 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. China 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on October of 2025.