Track real-time 1 Year Treasury Rate yields and explore historical trends from year start to today. View interactive yield curve data with YCharts.

Graph and download economic data for Interest Rates, Government Securities, Treasury Bills for Brazil (INTGSTBRM193N) from Jan 1995 to Jun 2025 about Brazil, bills, Treasury, securities, government, interest rate, interest, and rate.

The yield on Malaysia 10Y Bond Yield eased to 3.43% on September 23, 2025, marking a 0.01 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.03 points, though it remains 0.34 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Malaysia 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on September of 2025.

United States US: Government Bond Yield: Long Term data was reported at 2.372 % pa in 2017. This records an increase from the previous number of 1.842 % pa for 2016. United States US: Government Bond Yield: Long Term data is updated yearly, averaging 5.264 % pa from Dec 1953 (Median) to 2017, with 65 observations. The data reached an all-time high of 13.911 % pa in 1981 and a record low of 1.802 % pa in 2012. United States US: Government Bond Yield: Long Term data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s United States – Table US.IMF.IFS: Treasury Bill and Government Securities Rates: Annual.

TT: Treasury Bill Rate: Government Securities data was reported at 1.855 % pa in Sep 2018. This records an increase from the previous number of 1.353 % pa for Jun 2018. TT: Treasury Bill Rate: Government Securities data is updated quarterly, averaging 4.768 % pa from Dec 1964 (Median) to Sep 2018, with 216 observations. The data reached an all-time high of 11.983 % pa in Dec 1998 and a record low of 0.053 % pa in Mar 2014. TT: Treasury Bill Rate: Government Securities data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Trinidad and Tobago – Table TT.IMF.IFS: Treasury Bill and Government Securities Rates: Quarterly.

The yield on France 10Y Bond Yield eased to 3.56% on September 22, 2025, marking a 0 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.05 points and is 0.61 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. France 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on September of 2025.

The yield on India 6 Month Bond Yield eased to 5.56% on September 22, 2025, marking a 0.01 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.01 points and is 1.07 points lower 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 India 6M.

ME: Treasury Bill Rate: Government Securities data was reported at 0.200 % pa in 2017. This records a decrease from the previous number of 2.180 % pa for 2016. ME: Treasury Bill Rate: Government Securities data is updated yearly, averaging 1.420 % pa from Dec 2004 (Median) to 2017, with 14 observations. The data reached an all-time high of 9.000 % pa in 2004 and a record low of 0.200 % pa in 2017. ME: Treasury Bill Rate: Government Securities data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Montenegro – Table ME.IMF.IFS: Treasury Bill and Government Securities Rates: Annual.

Please refer to: https://data.imf.org/en/news/accessing%20international%20financial%20statistics

Lithuania LT: Treasury Bill Rate: Government Securities data was reported at 0.440 % pa in 2013. This records a decrease from the previous number of 2.030 % pa for 2012. Lithuania LT: Treasury Bill Rate: Government Securities data is updated yearly, averaging 6.782 % pa from Dec 1994 (Median) to 2013, with 15 observations. The data reached an all-time high of 29.213 % pa in 1995 and a record low of 0.440 % pa in 2013. Lithuania LT: Treasury Bill Rate: Government Securities data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Lithuania – Table LT.IMF.IFS: Treasury Bill and Government Securities Rates: Annual.

Moldova MD: Government Bond Yield: Long Term data was reported at 7.361 % pa in 2017. This records a decrease from the previous number of 17.467 % pa for 2016. Moldova MD: Government Bond Yield: Long Term data is updated yearly, averaging 14.707 % pa from Dec 1997 (Median) to 2017, with 17 observations. The data reached an all-time high of 29.782 % pa in 1998 and a record low of 6.188 % pa in 2013. Moldova MD: Government Bond Yield: Long Term data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Moldova – Table MD.IMF.IFS: Treasury Bill and Government Securities Rates: Annual.

Denmark DK: Government Bond Yield: Long Term data was reported at 0.515 % pa in 2017. This records an increase from the previous number of 0.321 % pa for 2016. Denmark DK: Government Bond Yield: Long Term data is updated yearly, averaging 6.254 % pa from Dec 1977 (Median) to 2017, with 41 observations. The data reached an all-time high of 20.630 % pa in 1982 and a record low of 0.321 % pa in 2016. Denmark DK: Government Bond Yield: Long Term data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Denmark – Table DK.IMF.IFS: Treasury Bill and Government Securities Rates: Annual.

Indonesia Government Securities: Benchmark Securities: FR0087: Yield data was reported at 6.350 % in 30 Dec 2021. This records an increase from the previous number of 6.340 % for 29 Dec 2021. Indonesia Government Securities: Benchmark Securities: FR0087: Yield data is updated daily, averaging 6.330 % from Jan 2021 (Median) to 30 Dec 2021, with 247 observations. The data reached an all-time high of 6.820 % in 19 Mar 2021 and a record low of 5.870 % in 04 Jan 2021. Indonesia Government Securities: Benchmark Securities: FR0087: Yield data remains active status in CEIC and is reported by Directorate General of Budget Financing and Risk Management. The data is categorized under Indonesia Premium Database’s Financial Market – Table ID.ZB005: Ministry of Finance: Government Securities: Benchmark Securities: Price and Yield.

Sri Lanka LK: Treasury Bill Rate: Government Securities data was reported at 10.111 % pa in 2017. This records an increase from the previous number of 9.998 % pa for 2016. Sri Lanka LK: Treasury Bill Rate: Government Securities data is updated yearly, averaging 10.111 % pa from Dec 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 18.914 % pa in 2008 and a record low of 6.598 % pa in 2014. Sri Lanka LK: Treasury Bill Rate: Government Securities data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Sri Lanka – Table LK.IMF.IFS: Treasury Bill and Government Securities Rates: Annual.

The Emerging Markets Bond Index (EMBI), commonly known as "riesgo país" in Spanish speaking countries, is a weighted financial benchmark that measures the interest rates paid each day by a selected portfolio of government bonds from emerging countries. It is measured in base points, which reflect the difference between the return rates paid by emerging countries' government bonds and those offered by U.S. Treasury bills. This difference is defined as "spread". Which Latin American country has the highest risk bonds? As of September 19, 2024, Venezuela was the Latin American country with the greatest financial risk and highest expected returns of government bonds, with an EMBI spread of around 254 percent. This means that the annual interest rates paid by Venezuela's sovereign debt titles were estimated to be exponentially higher than those offered by the U.S. Treasury. On the other hand, Brazil's EMBI reached 207 index points at the end of August 2023. In 2023, Venezuela also had the highest average EMBI in Latin America, exceeding 40,000 base points. The impact of COVID-19 on emerging market bonds The economic crisis spawned by the coronavirus pandemic heavily affected the financial market's estimated risks of emerging governmental bonds. For instance, as of June 30, 2020, Argentina's EMBI spread had increased more than four percentage points in comparison to January 30, 2020. All the Latin American economies measured saw a significant increase of the EMBI spread in the first half of the year.

Indonesia Government Securities: Benchmark Securities: FR0087: Price data was reported at 101.080 IDR in 30 Dec 2021. This records a decrease from the previous number of 101.120 IDR for 29 Dec 2021. Indonesia Government Securities: Benchmark Securities: FR0087: Price data is updated daily, averaging 101.220 IDR from Jan 2021 (Median) to 30 Dec 2021, with 247 observations. The data reached an all-time high of 104.760 IDR in 04 Jan 2021 and a record low of 97.780 IDR in 19 Mar 2021. Indonesia Government Securities: Benchmark Securities: FR0087: Price data remains active status in CEIC and is reported by Directorate General of Budget Financing and Risk Management. The data is categorized under Indonesia Premium Database’s Financial Market – Table ID.ZB005: Ministry of Finance: Government Securities: Benchmark Securities: Price and Yield.

NE: Government Bond Yield: Long Term data was reported at 6.000 % pa in Sep 2015. This stayed constant from the previous number of 6.000 % pa for Jun 2015. NE: Government Bond Yield: Long Term data is updated quarterly, averaging 6.000 % pa from Jun 2014 (Median) to Sep 2015, with 5 observations. The data reached an all-time high of 6.250 % pa in Sep 2014 and a record low of 6.000 % pa in Sep 2015. NE: Government Bond Yield: Long Term data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Niger – Table NE.IMF.IFS: Treasury Bill and Government Securities Rates: Quarterly.

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.

BAC^S Bank of America Corporation Depositary shares each representing 1/1000th interest in a share of 4.750% Non-Cumulative Preferred Stock Series SS

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 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.

American Financial Group Inc.'s 4.500% Debt: A Solid Investment for the Long Haul? (AFGE)

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 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.

F5 (FFIV) Network Security: A Bright Future?

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