The DXY exchange rate rose to 97.8683 on July 11, 2025, up 0.22% from the previous session. Over the past month, the United States Dollar has weakened 0.05%, and is down by 5.98% over the last 12 months. United States Dollar - values, historical data, forecasts and news - updated on July of 2025.

BMF Forecast: Foreign Exchange Rate: US Dollar: Annual Average data was reported at 1.100 USD/EUR in 2027. This stayed constant from the previous number of 1.100 USD/EUR for 2026. BMF Forecast: Foreign Exchange Rate: US Dollar: Annual Average data is updated yearly, averaging 1.100 USD/EUR from Dec 2016 (Median) to 2027, with 12 observations. The data reached an all-time high of 1.200 USD/EUR in 2022 and a record low of 1.100 USD/EUR in 2027. BMF Forecast: Foreign Exchange Rate: US Dollar: Annual Average data remains active status in CEIC and is reported by Federal Ministry of Finance. The data is categorized under Global Database’s Austria – Table AT.M007: Foreign Exchange Rate: US Dollar: Forecast.

USD index is expected to strengthen in the near term due to persistent safe-haven demand amid global economic uncertainties. The risk associated with this prediction is the potential for a correction if risk appetite improves or the Federal Reserve signals a dovish pivot.

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.

Dollar Index (Live)

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

Russia MED Forecast: Foreign Exchange Rate: Year Average: US Dollar: Baseline Scenario data was reported at 75.156 RUB/USD in 2036. This records an increase from the previous number of 74.739 RUB/USD for 2035. Russia MED Forecast: Foreign Exchange Rate: Year Average: US Dollar: Baseline Scenario data is updated yearly, averaging 69.636 RUB/USD from Dec 2016 (Median) to 2036, with 21 observations. The data reached an all-time high of 75.156 RUB/USD in 2036 and a record low of 58.335 RUB/USD in 2017. Russia MED Forecast: Foreign Exchange Rate: Year Average: US Dollar: Baseline Scenario data remains active status in CEIC and is reported by Ministry of Economic Development of the Russian Federation. The data is categorized under Global Database’s Russian Federation – Table RU.ME002: Foreign Exchange Rate: Year Average: US Dollar: Forecast: Ministry of Economic Development.

The EUR/USD exchange rate fell to 1.1688 on July 11, 2025, down 0.11% from the previous session. Over the past month, the Euro US Dollar Exchange Rate - EUR/USD has strengthened 0.90%, and is up by 7.17% over the last 12 months. Euro US Dollar Exchange Rate - EUR/USD - values, historical data, forecasts and news - updated on July of 2025.

Sweden NIER Forecast: Exchange Rate: US Dollar data was reported at 7.500 SEK/USD in 2028. This records a decrease from the previous number of 7.600 SEK/USD for 2027. Sweden NIER Forecast: Exchange Rate: US Dollar data is updated yearly, averaging 7.400 SEK/USD from Dec 1971 (Median) to 2028, with 58 observations. The data reached an all-time high of 10.300 SEK/USD in 2001 and a record low of 4.200 SEK/USD in 1980. Sweden NIER Forecast: Exchange Rate: US Dollar data remains active status in CEIC and is reported by National Institute of Economic Research. The data is categorized under Global Database’s Sweden – Table SE.M020: Foreign Exchange Rate: Forecast: National Institute of Economic Research.

ABSTRACT Purpose: The objective of this article is to model a minute series of exchange rates for the EUR/USD pair using the singular spectrum analysis (SSA) and ARIMA-GARCH methods and evaluate which one offers better forecasts for a five-minute horizon. Originality/value: Despite being a successful technique in other branches of science, the application of SSA in finance is quite new. Furthermore, exchange rate modeling is a complex problem, comprising statistical concepts and properties. However, despite the complexity, the analysis of this series is extremely important for several agents playing, directly or indirectly, a role in the economy and the financial market. Design/methodology/approach: Time series models were estimated using the ARIMA-GARCH and SSA techniques, taking into account three samples of the ask exchange rate (closing): uptrend, downtrend, and no well-defined trend. Findings: The forecasts carried out by the SSA were the ones closest to the original observations for the three cases. Regarding the quality measurements, SSA obtained the best results for both uptrend and downtrend samples; for the sample with no well-defined trend, the findings indicated that the ARIMA-GARCH technique attained better results. However, it was concluded that the SSA forecasts, regarding exchange rates during the studied period, are more appropriate than the ones obtained by the ARIMA-GARCH model, regardless of the market movement.

The USD/TWD exchange rate rose to 29.2420 on July 11, 2025, up 0.00% from the previous session. Over the past month, the Taiwanese Dollar has strengthened 1.08%, and is up by 10.12% over the last 12 months. Taiwanese Dollar - values, historical data, forecasts and news - updated on July of 2025.

Between 2025 and 2028, the U.S. dollar to Polish zloty exchange rate in Poland will maintain a stable value of ****** zloty for one U.S. dollar.

Luxembourg STATEC Forecast: Foreign Exchange Rate: US Dollar data was reported at 1.180 USD/EUR in 2018. This records an increase from the previous number of 1.130 USD/EUR for 2017. Luxembourg STATEC Forecast: Foreign Exchange Rate: US Dollar data is updated yearly, averaging 1.155 USD/EUR from Dec 2017 (Median) to 2018, with 2 observations. The data reached an all-time high of 1.180 USD/EUR in 2018 and a record low of 1.130 USD/EUR in 2017. Luxembourg STATEC Forecast: Foreign Exchange Rate: US Dollar data remains active status in CEIC and is reported by The Portal of Statistics of Luxembourg. The data is categorized under Global Database’s Luxembourg – Table LU.M009: Foreign Exchange Rate: Forecast: The Portal of Statistics of Luxembourg.

The official currency of Puerto Rico is the US Dollar. This dataset displays a chart with historical values for the US Dollar Index. United States Dollar - values, historical data, forecasts and news - updated on July of 2025.

The USD/CAD exchange rate rose to 1.3686 on July 11, 2025, up 0.22% from the previous session. Over the past month, the Canadian Dollar has weakened 0.61%, and is down by 0.38% over the last 12 months. Canadian Dollar - values, historical data, forecasts and news - updated on July of 2025.

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.

US Dollar Index: Bullish Break or Bearish Trap?

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

The euro-to-dollar exchange rate fluctuated significantly in 2022, reaching its lowest recorded value since 2008 during that time. Figures were different later in the year, however, with a rate of 1.17 USD recorded at the end of June 27, 2025. The average (standardized) measure is based on the calculation of many observations throughout the period in question. It is therefore different from an annual measure at a point, which reflects concrete values as of end of the year. Establishment The euro, which was established in 1992, introduced in non-physical form in 1999 and finally rolled out in 2002, is used by 19 of the 27 member states of the European Union. This group of 19 countries is otherwise known as the eurozone or euro area. By 2018, the total value of euro currency in circulation was almost 1.2 trillion euros, or over 3.4 thousand euros per capita. Euro to USD Between 2001 and 2008, the average annual exchange rate of the euro to the U.S. dollar noted a steep increase. In 2008, the euro to U.S. dollar annual average exchange rate was equal to 1.47, which meant that one euro could buy 1.47 U.S. dollars. By 2019, this value had decreased overall, to a value of 1.12 which meant that one euro could buy 1.12 U.S. dollars. Similar dynamics in the euro to U.S. dollar exchange rate were also reflected in the monthly exchange rate recently.

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.

Dollar's Outlook: Experts Predict a Volatile Ride for U.S. Dollar Index.

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

The NZD/USD exchange rate fell to 0.6004 on July 11, 2025, down 0.61% from the previous session. Over the past month, the New Zealand Dollar has weakened 1.00%, and is down by 1.86% over the last 12 months. New Zealand Dollar - values, historical data, forecasts and news - updated on July of 2025.

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.

Dollars and Sense: The Correlation Between US Total Reserves and the Dollar Index

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

BOF Forecast: Foreign Exchange Rate: US Dollar data was reported at 1.180 EUR/USD in 2020. This stayed constant from the previous number of 1.180 EUR/USD for 2019. BOF Forecast: Foreign Exchange Rate: US Dollar data is updated yearly, averaging 1.180 EUR/USD from Dec 2017 (Median) to 2020, with 4 observations. The data reached an all-time high of 1.200 EUR/USD in 2018 and a record low of 1.130 EUR/USD in 2017. BOF Forecast: Foreign Exchange Rate: US Dollar data remains active status in CEIC and is reported by Bank of France. The data is categorized under Global Database’s France – Table FR.M013: Foreign Exchange Rate: US Dollar: Forecast: Bank of France.

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.

Dollar's Fate Uncertain as Analysts Weigh Future of U.S. Dollar index.

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