Graph and download economic data for Nominal Broad U.S. Dollar Index (DTWEXBGS) from 2006-01-02 to 2026-03-20 about trade-weighted, broad, exchange rate, currency, goods, services, rate, indexes, and USA.

The US dollar index of March 4, 2026, was lower than it was in October 2025, although still above the level in June 2025, the lowest value in the current year. This reveals itself in a historical graphic on the past 50 years, measuring the relative strength of the U.S. dollar. This metric is different from other FX graphics that compare the U.S. dollar against other currencies. By March 4, 2025, the DXY index was around 98,97 points. The history of the DXY Index The index shown here – often referred to with the code DXY or USDX -measures the value of the U.S. dollar compared to a basket of six other foreign currencies. This basket includes the euro, the Swiss franc, the Japanese yen, the Canadian dollar, the British pound, and the Swedish króna. The index was created in 1973, after the arrival of the petrodollar and the dissolution of the Bretton Woods Agreement. Today, most of these currencies remain connected to the United States' largest trade partners. The relevance of the DXY Index The index focuses on trade and the strength of the U.S. dollar against specific currencies. It's less about inflation or devaluation, which is measured in alternative metrics like the Big Mac Index. Indeed, as the methodology behind the DXY Index has only been updated once – when the euro arrived in 1999 – some argue this composition is not accurate to the current state of the world. The price development of the U.S. dollar affects many things, including commodity prices in general.

Graph and download economic data for Real Broad Dollar Index (RTWEXBGS) from Jan 2006 to Feb 2026 about trade-weighted, broad, goods, services, real, indexes, and USA.

Graph and download economic data for Trade Weighted U.S. Dollar Index: Major Currencies, Goods (DISCONTINUED) (TWEXM) from 1973-01-03 to 2020-01-01 about major, trade-weighted, exchange rate, currency, goods, rate, indexes, and USA.

Graph and download economic data for Nominal Emerging Market Economies U.S. Dollar Index (DTWEXEMEGS) from 2006-01-02 to 2026-03-13 about trade-weighted, emerging markets, exchange rate, currency, goods, services, rate, indexes, and USA.

When converted to the value of one US dollar in 2020, goods and services that cost one dollar in 1700 would cost just over 63 dollars in 2020, this means that one dollar in 1700 was worth approximately 63 times more than it is today. This data can be used to calculate how much goods and services from the years shown would cost today, by multiplying the price from then by the number shown in the graph. For example, an item that cost 50 dollars in 1970 would theoretically cost 335.5 US dollars in 2020 (50 x 6.71 = 335.5), although it is important to remember that the prices of individual goods and services inflate at different rates than currency, therefore this graph must only be used as a guide.

Graph and download economic data for Real Emerging Market Economies Dollar Index (RTWEXEMEGS) from Jan 2006 to Feb 2026 about trade-weighted, emerging markets, goods, services, real, indexes, and USA.

The trade-weighted index (TWI), also known as the real broad index, measures the strength of the US dollar relative to the currencies of the nation's trading partners. Weightings are determined by the share of trade with each country, with the five largest allocated to the Euro, Canadian dollar, Chinese yuan, Japanese yen and Mexican peso. These five currencies account for over two-thirds of the TWI. The data for this report is price adjusted (i.e. real) and sourced from the Economic Research Division of the Federal Reserve. Figures are based to an index value of 100 at January 2006.

One United States dollar was worth over ********* Indonesian rupiah in September 2025, the highest value in a comparison of over 50 different currencies worldwide. All countries and territories shown here are based on the Big Mac Index - a measurement of how much a single Big Mac is worth across different areas in the world. This exchange rate comparison reveals a strong position of the dollar in Asia and Latin America. Note, though, that several of the top currencies shown here do not rank among the most traded. The quarterly U.S. dollar exchange rate against the ten biggest forex currencies only contains the Korean won and the Japanese yen.

Context

This dataset provides a daily, aligned matrix of macroeconomic and market volatility features designed specifically for Unsupervised Market Regime Detection (e.g., Hidden Markov Models). It spans two decades (2006–2026), capturing major market cycles including the 2008 Financial Crisis, the 2020 COVID-19 crash, and the 2022 rate hike cycle.

Base Features

The dataset contains aligned daily values for the following core indicators:

VIX (CBOE Volatility Index): Represents implied market volatility and fear.

Credit Spread (Credit_Spread): ICE BofA US High Yield Index Option-Adjusted Spread. Measures corporate financial stress.

Yield Curve (Yield_Curve): 10-Year minus 2-Year Treasury Constant Maturity rate. An indicator of economic expectations and recession probability.

US Dollar Index (DXY): Measures the value of the US dollar relative to a basket of foreign currencies.

Chicago Fed National Activity Index (CFNAI): A broad coincident indicator of national economic activity and inflationary pressure.

Initial Jobless Claims (Jobless_Claims): Weekly filings for unemployment insurance, measuring real-time labor market weakness.

SPY Log Returns (SPY_LogRet): Daily logarithmic returns of the S&P 500 ETF (typically used as the predictive target or out-of-sample evaluation metric).

Preprocessing

To make this data directly viable for quantitative modeling and dimensionality reduction (PCA), several transformations have been applied:

Trading Day Alignment: All weekend and market holiday nulls have been strictly dropped. The index represents actual NYSE trading days.

Exponential Decay Fills: Macroeconomic data released periodically (Weekly Jobless Claims, Monthly CFNAI) are not simply forward-filled. An exponential decay function is applied to the discrete releases to mathematically simulate the fading market impact of the news over time.

Rolling Z-Scores (Z_ prefix): Rolling 252-day (1-year) Z-scores applied to the base features to normalize the data and capture relative extremes.

Momentum/Delta (d prefix): Rate-of-change transformations (e.g., 20-day differencing) to capture the trajectory of the indicators.

Data Sourcing & Compliance Note

Please Note : The original project for which this dataset was generated uses ISM manufacturing PMI in place of CFNAI. However in order to comply with data licensing agreements, the ISM PMI has been excluded from this public release and replaced with CFNAI which is free to use

Our dataset consists of monthly data from August 2004 to October 2016 on international copper futures prices (P_CU), global refined copper production (GRCP), global refined copper consumption (GRCC), China’s copper imports (CCI), the percent position held by non-commercial traders (NCPP), federal funds rate (FFR), broad dollar index (BDI), and crude oil prices (COP). The copper futures closing prices of the LME are selected to represent international copper futures prices. Changes in global copper supply and demand are reflected by the global refined copper production and global refined copper consumption selected based on monthly data provided by the International Copper Study Group (ICSG, http://www.icsg.org/). We select China’s copper imports to represent “Chinese factor”, and these data are obtained from the average monthly data of copper ore and concentrate provided by China’s customs authorities. Following Sanders et al. (2004) and Fan and Xu (2011), we use the percent position held by non-commercial traders (NCPP) to measure the financial speculation, which is calculated by (non-commercial long position+ non-commercial short position+2* non-commercial spread position)/ (2* total open interest), the data are sourced from the Commodity Futures Trading Commission (CFTC). We use the federal funds rate as a proxy variable for the interest rate. The change in USD exchange rate is measured by the broad dollar index issued by the Federal Reserve Board, and it measures the change in the exchange rate of USD against a basket of foreign currencies. Regarding the oil price variable, as it is generally considered to be a good proxy for the global oil price market, we use the West Texas Intermediate (WTI) crude oil futures prices for the empirical analysis. The data on international copper futures prices, federal funds rate, broad dollar index and WTI crude oil prices are obtained from the WIND database. To eliminate heteroscedasticity, all the variables except financial speculation and federal funds rate are expressed in natural logarithms.

At **** U.S. dollars, Switzerland has the most expensive Big Macs in the world, according to the January 2025 Big Mac index. Concurrently, the cost of a Big Mac was **** dollars in the U.S., and **** U.S. dollars in the Euro area. What is the Big Mac index? The Big Mac index, published by The Economist, is a novel way of measuring whether the market exchange rates for different countries’ currencies are overvalued or undervalued. It does this by measuring each currency against a common standard – the Big Mac hamburger sold by McDonald’s restaurants all over the world. Twice a year the Economist converts the average national price of a Big Mac into U.S. dollars using the exchange rate at that point in time. As a Big Mac is a completely standardized product across the world, the argument goes that it should have the same relative cost in every country. Differences in the cost of a Big Mac expressed as U.S. dollars therefore reflect differences in the purchasing power of each currency. Is the Big Mac index a good measure of purchasing power parity? Purchasing power parity (PPP) is the idea that items should cost the same in different countries, based on the exchange rate at that time. This relationship does not hold in practice. Factors like tax rates, wage regulations, whether components need to be imported, and the level of market competition all contribute to price variations between countries. The Big Mac index does measure this basic point – that one U.S. dollar can buy more in some countries than others. There are more accurate ways to measure differences in PPP though, which convert a larger range of products into their dollar price. Adjusting for PPP can have a massive effect on how we understand a country’s economy. The country with the largest GDP adjusted for PPP is China, but when looking at the unadjusted GDP of different countries, the U.S. has the largest economy.

The USD/CAD exchange rate rose to 1.3893 on March 27, 2026, up 0.24% from the previous session. Over the past month, the Canadian Dollar has weakened 1.58%, but it's up by 2.90% over the last 12 months. Canadian Dollar - values, historical data, forecasts and news - updated on March of 2026.

Overview

This dataset explores the relationship between global oil markets and geopolitical events from 2010 to 2026. It combines daily oil price data with macroeconomic indicators, a geopolitical risk index, and a curated timeline of major global events that have historically influenced energy markets.

The dataset includes Brent and WTI crude oil prices, along with key financial indicators such as the U.S. Dollar Index (DXY) and the VIX volatility index. It also integrates the Geopolitical Risk (GPR) Index, which measures global geopolitical tensions based on news coverage.

In addition to raw financial data, the dataset includes engineered features such as lagged prices, rolling volatility measures, and price spreads between Brent and WTI. A curated list of major geopolitical events—including wars, sanctions, supply disruptions, and OPEC policy changes—is also included, allowing researchers to analyze how geopolitical shocks affect oil markets.

This dataset is designed to support time series analysis, forecasting, and event-driven financial research.

Possible Use Cases

• Time series forecasting of oil prices
• Event study analysis around geopolitical shocks
• Analyzing the relationship between geopolitical risk and energy markets
• Volatility modeling in commodity markets
• Machine learning models for commodity price prediction
• Studying the impact of sanctions, wars, and supply disruptions on oil prices

Dataset Contents

The dataset includes the following components:

Market Data

• Brent crude oil price
• WTI crude oil price

Macroeconomic Indicators

• U.S. Dollar Index (DXY)
• VIX volatility index

Geopolitical Indicators

• Geopolitical Risk Index (GPR)

Engineered Features

• Daily returns
• Lagged prices
• Rolling volatility measures
• Brent–WTI spread

Event Annotations

• Event flag (indicating major geopolitical events)
• Event type
• Event description
• Event severity score

Files Included

• oil_geopolitics_dataset_2010_2026.csv – Final merged dataset with engineered features and event annotations
• geopolitical_events_timeline.csv – Timeline of major geopolitical events affecting global oil markets

Data Sources

• Yahoo Finance – Historical oil prices and macroeconomic indicators
• Geopolitical Risk Index (Caldara & Iacoviello)
• Publicly available records of major geopolitical events and energy market disruptions

View monthly updates and historical trends for US Consumer Price Index: Purchasing Power Of the Consumer Dollar. from United States. Source: Bureau of Lab…

MX Peso Index (PXY) México — Daily Sample with 30‑Day Delay

This dataset provides a daily 12‑month sample of the MX Peso Index (PXY), an index developed by Arandkeito measure the relative strength of the Mexican Peso (MXN) against a fixed basket of major international currencies.

The methodology mirrors the US Dollar Index (DXY), replacing USD with MXN as the base currency, which allows direct comparison between both indices.

The sample is published with a 30‑day intentional delay and is intended for exploratory analysis, modeling, and academic research. It does not expose the full historical series.

Methodology

The PXY replicates the DXY basket and weights:

Geometric weighted formula:

PXY = 0.6287 × EUR^−0.576 × JPY^−0.136 × GBP^−0.119 × CAD^−0.091 × SEK^−0.042 × CHF^−0.036

Base period: March 1, 1973 = 100.00

Pre-euro period (1973–1999) uses the original ten-currency composition including Deutsche Mark, French Franc, Italian Lira, Dutch Guilder, and Belgian Franc with their respective original DXY weights

Dataset Contents

Columns included:

• date — ISO 8601 date
• pxy — Daily index value
• change — Daily change (pxy_t − pxy_t‑1) / pxy_t‑1

Coverage: - Last 12 months
- 30‑day publication delay

This dataset does not update automatically.

Interpretation

• PXY rising: MXN appreciation relative to the basket
• PXY falling: MXN depreciation relative to the basket

Used alongside the DXY, the PXY allows analysts to determine whether movements in USD/MXN reflect dollar strength, peso weakness, or both — a distinction that no existing benchmark currently provides.

Use Cases

• FX quantitative modeling
• Macroeconomic analysis
• Academic research
• Trend visualization
• Comparisons with DXY and other currency indices

Limitations

• Basket and weights mirror the DXY as published since 1973 and have not been updated, consistent with the DXY's own methodology.
• The index measures currency strength against major financial currencies, not trade partners.

Full Historical Data & Official API

For:

• Full historical series (1973–present)
• Real‑time daily updates
• Additional endpoints
• Programmatic access

Use the official API on RapidAPI:

👉 (Ir a API)

📌 License

This dataset is released under the CC BY‑NC 4.0 (Attribution–NonCommercial) license.

Rights Holder

Arandkei

Required Citation

MX Peso Index (PXY) — Arandkei / RapidAPI

Commercial use or redistribution of this dataset, in whole or in part, requires prior written permission from Arandkei.

This report analyses New Zealand's trade-weighted index (TWI), which measures the value of the New Zealand dollar against the currencies of 17 of New Zealand's major trading partners. The group of currencies is weighted based on the level of trade with each country. The weights are calculated annually and typically take effect in December. The five most heavily weighted currencies in the index for 2026 are the Chinese renminbi (21.5%), Australian dollar (17.8%), US dollar (16.2%), the Euro (9.2%) and the South Korean won (4.8%). The data for this report is sourced from the Reserve Bank of New Zealand (Te Putea Matua) and is presented as an average index over each financial year.

CRB Index rose to 457.52 Index Points on March 26, 2026, up 1.55% from the previous day. Over the past month, CRB Index's price has risen 16.06%, and is up 22.67% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. CRB Commodity Index - values, historical data, forecasts and news - updated on March of 2026.

The value of refinance applications for mortgages in the United States soared during the COVID-19 pandemic, followed by a drop in 2021. In the week ending September 13, 2024, Fannie Mae's dollar value Refinance Application-Level Index (RALI) amounted to ***** index points, down from ***** index points when the market peaked in March 2020. The index measures the development of the value of mortgage refinance applications, with the first week of 2004 chosen as a baseline year. An index value of *** suggests an increase in the value of refinance applications of ** percent since the baseline period.

The Canadian effective exchange rate index (CERI) measures the value of the Canadian dollar against a weighted basket of major trading partner currencies, using 1999 as the base year (index value of 100). This metric captures the overall strength or weakness of the Canadian dollar in international markets by weighting currencies according to Canada's bilateral trade flows, providing a more comprehensive measure of currency competitiveness than bilateral exchange rates alone. The index is compiled by the Bank of Canada using daily exchange rates and trade weights that reflect Canada's actual trading relationships.