Graph and download economic data for 20-Year 2-1/2% Treasury Inflation-Indexed Bond, Due 1/15/2029 (DTP20J29) from 2010-01-04 to 2025-07-23 about 20-year, TIPS, bonds, Treasury, interest rate, interest, real, rate, and USA.

The average market yield on the United States Treasury's 10-year bond was **** percent during the second quarter of 2024. This rate was adjusted to reflect a constant maturity and also indexed to inflation, giving an idea of real returns for longer-term investments. The recent expected return was highest at the end of the end of the last quarter of 2024, and lowest in the second half of 2021, when it was negative.

Graph and download economic data for Treasury Long-Term Average (Over 10 Years), Inflation-Indexed (DLTIIT) from 2000-01-03 to 2025-07-21 about TIPS, long-term, Treasury, yield, interest rate, interest, real, rate, and USA.

The United States Treasury's 10-year bond earned an average return of **** percent in 2023. This rate was adjusted to reflect a constant maturity and also indexed to inflation, giving an idea of real returns for longer-term investments. This expected return averaged around *** percent before the financial crisis but was negative in 2011, 2012, 2020, and 2021.

Graph and download economic data for Market Yield on U.S. Treasury Securities at 20-Year Constant Maturity, Quoted on an Investment Basis, Inflation-Indexed (DFII20) from 2004-07-27 to 2025-07-21 about 20-year, TIPS, maturity, securities, Treasury, interest rate, interest, real, rate, and USA.

As of July 18, 2025, the major economy with the highest yield on 10-year government bonds was Turkey, with a yield of ** percent. This is due to the risks investors take when investing in Turkey, notably due to high inflation rates potentially eradicating any profits made when using a foreign currency to investing in securities denominated in Turkish lira. Of the major developed economies, United Kingdom had one the highest yield on 10-year government bonds at this time with **** percent, while Switzerland had the lowest at **** percent. How does inflation influence the yields of government bonds? Inflation reduces purchasing power over time. Due to this, investors seek higher returns to offset the anticipated decrease in purchasing power resulting from rapid price rises. In countries with high inflation, government bond yields often incorporate investor expectations and risk premiums, resulting in comparatively higher rates offered by these bonds. Why are government bond rates significant? Government bond rates are an important indicator of financial markets, serving as a benchmark for borrowing costs, interest rates, and investor sentiment. They affect the cost of government borrowing, influence the price of various financial instruments, and serve as a reflection of expectations regarding inflation and economic growth. For instance, in financial analysis and investing, people often use the 10-year U.S. government bond rates as a proxy for the longer-term risk-free rate.

Graph and download economic data for Market Yield on U.S. Treasury Securities at 5-Year Constant Maturity, Quoted on an Investment Basis, Inflation-Indexed (DFII5) from 2003-01-02 to 2025-07-22 about TIPS, maturity, securities, Treasury, interest rate, interest, real, 5-year, rate, and USA.

Sources:

German Central Bank (ed.), 1975: Deutsches Geld- und Bankwesen in Zahlen 1876 – 1975. (German monetary system and banking system in numbers 1876 – 1975) German Central Bank (ed.), different years: monthly reports of the German Central Bank, statistical part, interest rates German Central Bank (ed.), different years: Supplementary statistical booklets for the monthly reports of the German Central Bank 1959 – 1992, security statistics Reich Statistical Office (ed.), different years: Statistical yearbook of the German empire Statistical Office (ed.), 1985: Geld und Kredit. Index der Aktienkurse (Money and Credit. Index of share prices) – Lange Reihe; Fachserie 9, Reihe 2. Statistical Office (ed.), 1987: Entwicklung der Nahrungsmittelpreise von 1800 – 1880 in Deutschland. (Development of food prices in Germany 1800 – 1880) Statistical Office (ed.), 1987: Entwicklung der Verbraucherpreise (Development of consumer prices) seit 1881 in Deutschland. (Development of consumer prices since 1881 in Germany) Statistical Office (ed.), different years: Fachserie 17, Reihe 7, Preisindex für die Lebenshaltung (price index for costs of living) Donner, 1934: Kursbildung am Aktienmarkt; Grundlagen zur Konjunkturbeobachtung an den Effektenmärkten. (Prices on the stock market; groundwork for observation of economic cycles on the stock market) Homburger, 1905: Die Entwicklung des Zinsfusses in Deutschland von 1870 – 1903. (Development of the interest flow in Germany, 1870 – 1903) Voye, 1902: Über die Höhe der verschiedenen Zinsarten und ihre wechselseitige Abhängigkeit.(On the values of different types of interests and their interdependence).

Graph and download economic data for Market Yield on U.S. Treasury Securities at 30-Year Constant Maturity, Quoted on an Investment Basis (DGS30) from 1977-02-15 to 2025-07-22 about 30-year, maturity, Treasury, interest rate, interest, rate, and USA.

Graph and download economic data for 10-Year Real Interest Rate (REAINTRATREARAT10Y) from Jan 1982 to Jul 2025 about 10-year, interest rate, interest, real, rate, and USA.

The yield on 10 Year TIPS Yield eased to 1.94% on July 22, 2025, marking a 0.01 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.06 points and is 0.03 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 the United States 10 Year TIPS Yield.

The yield on Germany 10Y Bond Yield rose to 2.68% on July 18, 2025, marking a 0 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.16 points and is 0.22 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 July of 2025.

Until the 90s information on risk premiums based on empirical studies for the German capital market was only available sporadically and for short time horizons. Therefore a long term comparison of risk and return was not possible. Markus Morawietz investigates profitability and risk of German stock and bond investments since 1870. He takes inflation and tax issues into account. His work contains a comprehensive collection of primary data since 1870 on key figures on a monthly basis which describe the German capital market. The goal of the study is to identify empirical statements on parameters of the German capital market. Therefore the exposition of theoretical economic models is not of primary importance in this study. A special focus is on the potential applicability of existing Germen index numbers as base data on the empirical investigation. The first chapter “methodological bases of performance measurement” concludes with the definition of the term “performance”. The following hypothesis is tested within this study: “There is a risk premium on securities taking inflation and influences of taxes into account.” The test of this hypothesis is run over the longest time period possible. Therefore monthly data on stock and bond investment are subject of the investigation because they are the most actively traded assets. Furthermore a substitute for the risk-free investment was developed in order to determine the risk premium. Before the explicit performance measurement of the different assets takes place, empirical starting points for performance measurement will be defined. These starting points contain a relevant demarcation of the investigation period and a description of the historical events during the investigation periods for all periods. Hereby special consideration is given to the specific problems of long term German value series (interruption trough the First World War with the following Hyperinflation and the Second World War). The analysis of the basics of performance measurement concludes the empirical starting points for performance measurement. The starting points contain the definition of a substitute for the certain segment, the description and preparation of the underlying data material and the calculation method used to determine performance. The third chapter contains a concrete empirical evaluation of the available data. This evaluation is subdivided into two parts: (a) performance measurement with unadjusted original data and (b) performance measurement with adjusted primary data (adjusted for inflation and tax influences). Both parts are structured in the same way. First the performance measurement of the specific asset (stocks, bonds and risk-free instruments) will be undertaken each by itself subdivided by partial periods. Afterwards the results of the performance measurement over the entire investigation period will be analyzed. The collection of derived partial results in the then following chapter shows return risk differences between the different assets. To calculate the net performance the nominal primary data is adjusted by inflation and tax influences. Therefore measured values for the changes in price level and for tax influences will be determined in the beginning of the third chapter. Following the performance measurement will be undertaken with the adjusted primary data. A comparison of the most important results of the different analysis in the last chapter concludes.

Data tables in histat (topic: money and currencies):

A. Discount and Lombard rate A.1 Discount rate: monthly average values, yearly average values (1870-1992) A.2 Lombard rate: monthly average values, yearly average values (1870-1992)

B. Stock price index, dividends and bond market und B.1a Stock price index: monthly average values, yearly average values (1870-1992) B.2 Dividends: monthly average values (1870-1992) B.3 Bond market: monthly average values, yearly average values (1870-1992)

C. Risk free instrument C.1 Private discount rate: monthly average values, yearly average values (1870-1991) C.2 Overnight rate: monthly average values, yearly average values (1924-1992)

D. Inflation rate D.1 Price index for costs of living (base1913/14 = 100), monthly average values, yearly average values (1870-1992) D.2 Inflation rate (base 1913 = 100), M monthly average values, yearly average values (1870-1992)

Für den deutschen Kapitalmarkt lagen bis in die 90er Jahre Informationen über Risikoprämien, die sich auf empirische Untersuchungen stützen konnten, nur vereinzelt und lediglich für die jüngere Zeit vor, so dass ein langfristiger Rendite- und Risikovergleich nicht möglich war. Markus Morawietz untersucht die Rentabilität und das Risiko deutscher Aktien- und Rentenanlagen seit dem Jahr 1870. Dabei werden auch die Geldentwertung und die steuerlichen Einflüsse berücksichtigt. Die Arbeit enthält eine umfassende Sammlung von Primärdaten seit dem Jahr 1870, die auf Monatsbasis die Kennzahlen liefert, die den deutschen Geld- und Kapitalmarkt beschreiben. Ziel der vorliegenden Arbeit sind empirische Aussagen über Parameter des deutschen Kapitalmarktes als solche zu gewinnen. Die Darstellung wirtschaftswissenschaftlicher Modelle hat daher in der Arbeit untergeordneten Charakter. Besonderer Wert wird auf die eventuelle Verwendbarkeit bestehender deutscher Indexzahlen als Basisdaten für die empirische Untersuchung gelegt. Das erste Kapitel „Methodische Grundlagen der Performancemessung“ wird mit einer Definition des Begriffs „Performance“ abgeschlossen. Innerhalb der Arbeit wird folgende Hypothese getestet: „Es existiert eine Risikoprämie bei Wertpapieren unter Berücksichtigung von Geldentwertung und steuerlichen Einflüssen in Deutschland“. Der Test dieser Hypothese wird über einen längstmöglichen Zeitraum für den deutschen Kapitalmarkt durchgeführt. Daher werden die monatlichen Renditen der beiden meistgehandelten Vermögensgüter auf dem Kapitalmarkt, die Aktien- und die Rentenanlage, zum Gegenstand der Untersuchung gemacht. Darüber hinaus wird für die Ermittlung der Risikoprämie ein Substitut für die risikolose bzw. risikofreie Anlage entwickelt. Bevor auf die Performancemessung der jeweiligen Anlageform explizit eingegangen wird, werden die empirischen Ausgangspunkte der Performancemessung gelegt. Diese beinhalten eine für alle Anlageformen gleichermaßen relevante Abgrenzung des Untersuchungszeitraumes und die Darstellung der geschichtlichen Ereignisse innerhalb des Beobachtungszeitraumes. Dabei werden die speziellen Probleme langfristiger deutscher Wertreihen (Unterbrechung durch den ersten Weltkrieg mit anschließender Hyperinflation und durch den zweiten Weltkrieg) besonders berücksichtigt. Die Analyse der Grundlagen der Performancemessung - sowohl für den Aktien- und Rentenmarkt als auch für die risikolose Anlage - die für die jeweiligen Anlageformen obligat sind, schließen die Ausführungen der empirischen Ausgangspunkte für die Performancemessung ab. Die Ausgangspunkte umfassen jeweils die Festlegung eines Substituts im Sinne des Indexkonzeptes für das jeweilige Segment, die Darstellung und die Aufbereitung des zugrunde liegenden Datenmaterials sowie die jeweilige Berechnungsmethode zur Ermittlung der Performance. Im dritten Kapitel erfolgt dann die konkrete empirische Auswertung der verfügbaren Daten. Diese Auswertung ist in zwei Teile gegliedert: (a) Performancemessung mit unbereinigten Ausgangsdaten und (b) Performancemessung mit den um die maßgeblichen Einflüsse (Geldentwertung, und steuerliche Einflüsse) bereinigten Primärdaten. Beide Teilbereiche sind gleich aufgebaut. So wird zunächst in einem Kapitel die Performancemessung der untersuchten Anlageformen (Anlage in Aktien, Renten und risikofreien Papieren) einzeln, d.h. jede für sich und in Teilperioden, durchgeführt. Im Anschluss daran werden die Ergebnisse der Performancemessung über den gesamten Beobachtungszeitraum analysiert. Erst die anschließende Zusammenstellung der gewonnenen Teilergebnisse in einem anschließenden Kapitel zeigt Rendite- und Risikodifferenzen zwischen den untersuchten Anlageformen auf. Zur Ermittlung der Netto – Performance werden die nominalen Primärdaten um die Geldentwertung und um die steuerlichen Einflüsse bereinigt. Daher werden in dem dritten Kapitel zunächst die Maßzahlen zur Darstellung der Veränderungen des Preisniveaus und des Ertragsteuereinflusses für die untersuchten Anlageformen festgelegt. Nach dieser Festlegung und der anschließenden Bereinigung um diese Einflüsse wird die Performancemessung mit den bereinigten Primärdaten durchgeführt. Eine Gegenüberstellung der wichtigsten Analyseergebnisse in dem letzten Kapitel schließt die Untersuchung ab. Datentabellen in HISTAT (Thema: Geld und Währung): A. Diskont- und LombardsatzA.1 Diskontsatz: Monatsdurchschnittswerte, Jahresdurchschnitte (1870-1992)A.2 Lombardsatz: Monatsdurchschnittswerte, Jahresdurchschnitte (1870-1992) B. Aktienkursindex und Dividenden, RentenmarktB.1a Aktienkursindex: Monatsdurchschnittswerte, Jahresdurchschnitte (1870-1992)B.2 Dividenden: Monatdurchschnittswerte (1870-1992)B.3 Rentenmarkt: Monatsdurchschnittswerte, Jahresdurchschnitte (1870-1992) C. Risikolose AnlageC.1 Privatdiskontsatz: Monatsdurchschnittswerte, Jahresdurchschnitte (1870-1991)C.2 Tagesgeldsatz: Monatsdurchschnittswerte, Jahresdurchschnitte (1924-1992) D. InflationsrateD.1 Preisindex für die Lebenshaltung (Basis 1913/14 = 100), Monatsdurchschnittswerte, Jahresdurchschnitte (1870-1992)D.2 Inflationsrate (Basis 1913 = 100), Monatsdurchschnittswerte, Jahresdurchschnitte (1870-1992) Until the 90s information on risk premiums based on empirical studies for the German capital market was only available sporadically and for short time horizons. Therefore a long term comparison of risk and return was not possible. Markus Morawietz investigates profitability and risk of German stock and bond investments since 1870. He takes inflation and tax issues into account. His work contains a comprehensive collection of primary data since 1870 on key figures on a monthly basis which describe the German capital market. The goal of the study is to identify empirical statements on parameters of the German capital market. Therefore the exposition of theoretical economic models is not of primary importance in this study. A special focus is on the potential applicability of existing Germen index numbers as base data on the empirical investigation. The first chapter “methodological bases of performance measurement” concludes with the definition of the term “performance”. The following hypothesis is tested within this study: “There is a risk premium on securities taking inflation and influences of taxes into account.” The test of this hypothesis is run over the longest time period possible. Therefore monthly data on stock and bond investment are subject of the investigation because they are the most actively traded assets. Furthermore a substitute for the risk-free investment was developed in order to determine the risk premium. Before the explicit performance measurement of the different assets takes place, empirical starting points for performance measurement will be defined. These starting points contain a relevant demarcation of the investigation period and a description of the historical events during the investigation periods for all periods. Hereby special consideration is given to the specific problems of long term German value series (interruption trough the First World War with the following Hyperinflation and the Second World War). The analysis of the basics of performance measurement concludes the empirical starting points for performance measurement. The starting points contain the definition of a substitute for the certain segment, the description and preparation of the underlying data material and the calculation method used to determine performance.The third chapter contains a concrete empirical evaluation of the available data. This evaluation is subdivided into two parts: (a) performance measurement with unadjusted original data and (b) performance measurement with adjusted primary data (adjusted for inflation and tax influences). Both parts are structured in the same way. First the performance measurement of the specific asset (stocks, bonds and risk-free instruments) will be undertaken each by itself subdivided by partial periods. Afterwards the results of the performance measurement over the entire investigation period will be analyzed. The collection of derived partial results in the then following chapter shows return risk differences between the different assets. To calculate the net performance the nominal primary data is adjusted by inflation and tax influences. Therefore measured values for the changes in price level and for tax influences will be determined in the beginning of the third chapter. Following the performance measurement will be undertaken with the adjusted primary data. A comparison of the most important results of the different analysis in the last chapter concludes.

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.

What is the difference between refund and adjustment?

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

Graph and download economic data for Interest Rates: Long-Term Government Bond Yields: 10-Year: Main (Including Benchmark) for Germany (IRLTLT01DEM156N) from May 1956 to Jun 2025 about long-term, Germany, 10-year, bonds, yield, government, interest rate, interest, and rate.

Graph and download economic data for Moody's Seasoned Baa Corporate Bond Yield (BAA) from Jan 1919 to Jun 2025 about Baa, bonds, yield, corporate, interest rate, interest, rate, and USA.

Graph and download economic data for Interest Rates: Long-Term Government Bond Yields: 10-Year: Main (Including Benchmark) for United Kingdom (IRLTLT01GBM156N) from Jan 1960 to Jun 2025 about long-term, 10-year, United Kingdom, bonds, yield, government, interest rate, interest, and rate.