Equity Market Volatility - Interest Rates - Historical chart and current data through 2025.

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Interest Rates (EMVMACROINTEREST) from Jan 1985 to Oct 2025 about volatility, uncertainty, equity, interest rate, interest, rate, and USA.

The benchmark interest rate in the United Kingdom was last recorded at 4 percent. This dataset provides - United Kingdom Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Month Jan 2022 Feb 2022 Mar 2022 Apr 2022 May 2022 Jun 2022 Jul 2022 Aug 2022 Sep 2022 Oct 2022 Nov 2022 Dec 2022 Jan 2023 Feb 2023 Mar 2023 Apr 2023 May 2023 Jun 2023 Jul 2023 Aug 2023 Sep 2023 Oct 2023 Nov 2023 Dec 2023 Jan 2024 Feb 2024 Mar 2024 Apr 2024 May 2024 Jun 2024 Jul 2024 Aug 2024 Sep 2024 Oct 2024 Nov 2024 Dec 2024 Jan 2025 Feb 2025 Mar 2025 Apr 2025 May 2025 Jun 2025 Jul 2025 Aug 2025 Sep 2025 Oct 2025 Avg Rate 3.20% 3.50% 3.86% 4.31% 4.91% 5.23% 5.46% 5.42% 5.45% 5.99% 6.52% 6.43% 6.25% 6.08% 6.28% 6.30% 6.27% 6.42% 6.57% 6.71% 6.90% 7.08% 7.29% 7.02% 6.58% 6.46% 6.61% 6.67% 6.85% 6.79% 6.70% 6.47% 6.15% 5.98% 6.27% 6.44% 6.53% 6.61% 6.48% 6.40% 6.48% 6.57% 6.53% 6.43% 6.23% 6.14%

United States - Equity Market Volatility Tracker: Macroeconomic News and Outlook: Interest Rates was 5.94573 Index in October of 2025, according to the United States Federal Reserve. Historically, United States - Equity Market Volatility Tracker: Macroeconomic News and Outlook: Interest Rates reached a record high of 23.32740 in October of 1987 and a record low of 1.74079 in May of 2017. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Equity Market Volatility Tracker: Macroeconomic News and Outlook: Interest Rates - last updated from the United States Federal Reserve on November of 2025.

This dataset contains monthly exchange rate data for four major global currencies: United States Dollar (USD), British Pound (GBP), Canadian Dollar (CAD), and Australian Dollar (AUD). The data spans multiple years, starting from January 1999, and is recorded on a monthly basis. It is structured in CSV format with five columns: Date, USD_Price, GBP_Price, CAD_Price, and AUD_Price. The Date column represents the timestamp of the recorded exchange rate in the YYYY-MM-DD format, while the remaining columns represent the exchange rates for their respective currencies.

This dataset can be used for various financial and economic analyses, including identifying long-term trends, studying fluctuations in exchange rates, and understanding periods of stability and volatility. It is particularly useful for researchers and analysts looking to examine historical currency trends and assess the factors influencing foreign exchange markets.

Financial professionals can leverage this data to build predictive models and apply machine learning techniques to estimate future exchange rates. By analyzing past trends, it is possible to gain insights into potential market movements and develop strategies for risk management and investment decision-making.

Economists can use the dataset to examine the impact of global economic events on currency values and study correlations between exchange rates and macroeconomic indicators such as inflation, interest rates, and trade balances. This can provide a deeper understanding of how economic policies and external shocks affect currency markets over time.

Additionally, the dataset is valuable for comparing currency movements with stock markets, commodity prices, and international trade patterns. It enables researchers to analyze how different currencies react to financial crises, policy changes, and economic shifts. By offering a comprehensive view of historical exchange rate fluctuations, this dataset serves as a foundation for financial forecasting, economic research, and market correlation studies.

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Foreign Exchange Market Size 2025-2029

The foreign exchange market size is valued to increase by USD 582 billion, at a CAGR of 10.6% from 2024 to 2029. Growing urbanization and digitalization will drive the foreign exchange market.

Major Market Trends & Insights

Europe dominated the market and accounted for a 47% growth during the forecast period.
By Type - Reporting dealers segment was valued at USD 278.60 billion in 2023
By Trade Finance Instruments - Currency swaps segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 118.14 billion
Market Future Opportunities: USD 582.00 billion
CAGR from 2024 to 2029 : 10.6%

Market Summary

The market, a dynamic and intricate web of financial transactions, plays a pivotal role in facilitating global trade and economic interactions. Its primary function is to enable the conversion of one currency into another, thereby mitigating the risk of currency fluctuations for businesses and investors. Key drivers of this market include growing urbanization and digitalization, which have expanded trading opportunities to a 24x7 global economy. However, the uncertainty of future exchange rates poses a significant challenge, necessitating effective risk management strategies. The market's evolution reflects the increasing interconnectedness of the global economy. Transactions occur in a decentralized, over-the-counter system, with major trading centers in London, New York, and Tokyo.
Participants include commercial banks, investment banks, hedge funds, and individual investors, all seeking to capitalize on price differences between currencies. Trends shaping the market include the increasing use of automation and artificial intelligence to analyze market data and execute trades. Regulatory changes, such as the introduction of stricter capital requirements, also impact the market's functioning. Looking ahead, the market is expected to remain a vital component of the global financial landscape, with continued growth driven by increased trade and economic interdependence. However, challenges, such as regulatory changes and geopolitical risks, will necessitate adaptability and innovation from market participants.

What will be the Size of the Foreign Exchange Market during the forecast period?

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How is the Foreign Exchange Market Segmented ?

The foreign exchange industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Reporting dealers
  Financial institutions
  Non-financial customers


Trade Finance Instruments

  Currency swaps
  Outright forward and FX swaps
  FX options


Trading Platforms

  Electronic Trading
  Over-the-Counter (OTC)
  Mobile Trading


Geography

  North America

    US
    Canada


  Europe

    Germany
    Switzerland
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The reporting dealers segment is estimated to witness significant growth during the forecast period.

The market, a dynamic and ever-evolving financial landscape, is characterized by constant activity and intricate patterns. Participants engage in various trading strategies, employing advanced tools such as stop-loss and take-profit orders on forex trading platforms. Real-time data feeds and order book dynamics facilitate trade execution speed, while market microstructure and slippage minimization techniques ensure efficient transactions. Currency correlation analysis and transaction cost analysis are integral to informed decision-making, with backtesting methodologies providing valuable insights. Currency forwards contracts, position sizing techniques, and forex derivatives pricing are essential components of risk management systems. Carry trade strategies, hedging strategies, and interest rate parity are popular tactics employed by market participants.

Algorithmic trading strategies, driven by options pricing models and trading algorithms' efficiency, significantly influence price discovery mechanisms. High-frequency trading and volatility modeling contribute to the market's liquidity risk management, while foreign exchange swaps and currency option valuation help manage risk. The market's complexities necessitate sophisticated risk management systems and intricate order routing optimization. Global payments systems facilitate the smooth transfer of funds, and liquidity risk management remains a critical concern for market participants. According to recent studies, The market is estimated to account for approximately USD6 trillion in daily trading volume, und

Between January 2018 and September 2025, Germany's inflation rate experienced significant volatility. Initially fluctuating between 0.3 and 3.1 percent, the rate escalated dramatically, reaching a peak of 10.4 percent in October 2022. By September 2024, the inflation rate had moderated to 1.6 percent. However, inflation began rising again towards the end of 2024, standing at 2.6 percent in December. In the first half of 2025, inflation remained relatively stable, standing at 2.1 percent in May 2025. The European Central Bank (ECB) responded to these inflationary pressures with a series of interest rate adjustments. After maintaining historically low rates, the ECB initiated its first rate hike since March 2016 in July 2022, raising the rate to 0.5 percent. The interest rate continued to increase, stabilizing at 4.5 percent from September 2023 to June 2024. In a notable shift, June 2024 marked the first rate cut during this period. It was followed by a series of rate cuts until the end of the year, with the last cut in 2024 setting the rate at 3.15 percent. Several further cuts were implemented in the first half of 2025, setting the rate at 2.15 percent in June 2025. As of September 2025, the inflation rate is 2.4 percent, with the ECB interest rate at 2.15 percent.

Dataset Description Overview

This dataset contains historical daily exchange rates between the US Dollar (USD) and the Indonesian Rupiah (IDR), sourced from Yahoo Finance. Each row represents one trading day, making it suitable for time-series analysis, forecasting, and financial research. Context

The USD/IDR exchange rate is widely used for:

Monitoring currency risk and hedging USD–IDR exposure
Evaluating Indonesia’s macroeconomic and financial conditions
Backtesting FX trading strategies
Teaching time-series and financial modeling

Because Indonesia is an emerging market, USD/IDR often exhibits notable volatility, driven by global interest rates, commodity prices, and domestic policy changes. Source and Collection

Data provider: Yahoo Finance
Instrument: USD/IDR exchange rate (e.g., ticker USDIDR=X on Yahoo Finance)
Frequency: Daily (one record per trading day)
Fields: Standard Yahoo Finance OHLC data (Open, High, Low, Close, Adjusted Close, Volume)
Collection method: Downloaded programmatically via a Python library that wraps Yahoo Finance data (e.g., yfinance)
Retrieval: Data was fetched in Python and then exported to CSV for this dataset
Coverage period: From the earliest available date on Yahoo Finance for USD/IDR up to the download date (please add the exact start and end dates if you want, e.g., YYYY‑MM‑DD to YYYY‑MM‑DD)

Please check Yahoo Finance’s terms of use before using the dataset in commercial or production settings. Possible Use Cases

Time-series forecasting models (ARIMA, Prophet, LSTM, etc.)
Volatility and risk analysis (e.g., rolling volatility, VaR)
Studying the impact of macroeconomic news or events on IDR
Feature in multi-asset or macroeconomic research datasets
Educational projects in finance, econometrics, and data science

Notes

This dataset contains no personal or sensitive information.
Values are provided as-is from Yahoo Finance; minor discrepancies may exist compared with other FX data vendors.

Between January 2018 and September 2025, the United Kingdom's consumer price inflation rate showed notable volatility. The rate hit its lowest point at *** percent in August 2020 and peaked at *** percent in October 2022. By September 2025, inflation had moderated to *** percent, indicating a gradual increase in inflation rates in the preceding months. The Bank of England's interest rate policy closely tracked these inflationary trends. Rates remained low at -* percent until April 2020, when they were reduced to *** percent in response to economic challenges. A series of rate increases followed, reaching a peak of **** percent from August 2023 to July 2024. The central bank then initiated rate cuts in August and November 2024, lowering the rate to **** percent, signaling a potential shift in monetary policy. In February 2025, the Bank of England implemented another rate cut, setting the bank rate at *** percent, which was further reduced to **** percent in May 2025 and remained at * percent as of September 2025. Global context of inflation and interest rates The UK's experience reflects a broader international trend of rising inflation and subsequent central bank responses. From January 2022 to July 2024, advanced and emerging economies alike increased their policy rates to counter inflationary pressures. However, a shift began in late 2024, with many countries, including the UK, starting to lower rates. This change suggests a potential new phase in the global economic cycle and monetary policy approach. Comparison with other major economies The UK's monetary policy decisions align closely with those of other major economies. The United States, for instance, saw its federal funds rate peak at **** percent in August 2023, mirroring the UK's rate trajectory. Similarly, central bank rates in the EU all increased drastically between 2022 and 2024. These synchronized movements reflect the global nature of inflationary pressures and the coordinated efforts of central banks to maintain economic stability. As with the UK, both the U.S. and EU began considering rate cuts in late 2024, signaling a potential shift in the global economic landscape.

The dataset appears to focus on the following features:

Stock Market Data:

Open, High, Low, Close: Daily price movements of a stock or index. Volume: The number of shares traded. Economic Indicators:

InterestRate: Reflects monetary policy trends, impacting the financial market. ExchangeRate: Represents currency exchange rates, which influence international trade. VIX (Volatility Index): A measure of market sentiment and expected volatility. TEDSpread: The difference between the interest rates on interbank loans and short-term government debt; used to gauge financial risk. EFFR (Effective Federal Funds Rate): Represents the interest rate banks charge each other for overnight loans. Commodity Prices:

Gold: Often considered a safe-haven asset. Oil: Key economic input, reflecting energy market trends. Likely Analysis or Tasks in the Notebook Descriptive Statistics: Summarizing trends in stock prices, trading volumes, and economic indicators.

Visualization: Using line plots, candlestick charts, or correlation heatmaps to explore relationships between variables.

Predictive Modeling: Potential machine learning models to forecast stock prices or analyze the relationship between indicators and market performance.

Economic Insights: Investigating how factors like interest rates, exchange rates, and commodity prices impact the Nasdaq index.

Extensive and dependable pricing information spanning the entire range of financial markets. Encompassing worldwide coverage from stock exchanges, trading platforms, indicative contributed prices, assessed valuations, expert third-party sources, and our enhanced data offerings. User-friendly request-response, bulk access, and tailored desktop interfaces to meet nearly any organizational or application data need. Worldwide, real-time, delayed streaming, intraday updates, and meticulously curated end-of-day pricing information.

TraditionData’s Caps & Floors service offers extensive datasets of volatility surfaces for multiple currencies, vital for interest rate risk management.

• Provides real-time pricing across various strikes and tenors, as well as historical data.
• Helps professionals make informed trading decisions and manage interest rate risk effectively.
• Covers 14 currencies and includes volatility surfaces managed directly by Tradition’s brokerage desks.

For a detailed look at this service, visit Caps & Floors Data.

TraditionData’s Interest Rate Swaps service offers comprehensive coverage across 33 currencies, focusing on portfolio interest rate risk management and yield enhancement. This service includes:

• Real-time, intraday, and end-of-day pricing for interest rate markets.
• Datasets sourced directly from Tradition’s brokerage desks.
• Flexible data content and delivery method.
• Beneficial for managing interest rate risk, enhancing yields, improving diversification, hedging interest rate exposure, and creating synthetic positions.

For further details, you can visit TraditionData Interest Rate Swaps.

Interest Rate Futures Options Market Outlook

According to our latest research, the global interest rate futures options market size reached USD 2.84 billion in 2024, reflecting robust activity across institutional and retail investors. The market is projected to grow at a CAGR of 7.1% from 2025 to 2033, reaching a forecasted value of USD 5.27 billion by 2033. This growth is primarily driven by increased demand for risk management instruments amid volatile macroeconomic conditions and evolving regulatory frameworks that emphasize transparency and liquidity in derivatives trading.

The growth trajectory of the interest rate futures options market is underpinned by several key factors. First, the ongoing volatility in global interest rates—driven by central bank policy shifts, inflationary pressures, and macroeconomic uncertainties—has heightened the need for efficient hedging instruments. Institutional investors, banks, and corporates are increasingly turning to interest rate futures options to manage exposure to rate fluctuations, optimize portfolio strategies, and enhance yield. The proliferation of sophisticated trading technologies and analytics platforms has further democratized access to these instruments, enabling a wider range of market participants to engage in hedging, speculation, and arbitrage activities with greater precision and efficiency.

Another pivotal driver is the regulatory environment, which continues to evolve in response to lessons learned from past financial crises. Regulatory bodies across North America, Europe, and Asia Pacific have implemented stringent reporting, margining, and clearing requirements for derivatives trading, fostering greater market stability and transparency. These regulations have incentivized market participants to migrate from over-the-counter (OTC) to exchange-traded platforms, where interest rate futures options offer standardized contracts, robust risk management, and enhanced liquidity. The shift towards electronic trading and centralized clearing has also reduced counterparty risk, making these instruments more attractive to a broader audience, including smaller institutions and corporates.

Technological innovation remains a cornerstone of market expansion. The integration of artificial intelligence, machine learning, and big data analytics into trading platforms has revolutionized the way participants analyze market trends, identify arbitrage opportunities, and execute complex strategies in real time. Automated trading systems and algorithmic execution have not only improved efficiency but also reduced transaction costs and slippage. These advancements are particularly significant for high-frequency trading firms and institutional investors, who rely on speed and accuracy to capitalize on fleeting market opportunities. As a result, the interest rate futures options market is witnessing increased participation from non-traditional players, further deepening liquidity and enhancing price discovery.

Interest Rate Caps and Floors have become increasingly relevant in the current financial landscape as they offer a mechanism to limit exposure to interest rate fluctuations. These instruments are particularly useful for borrowers and lenders who wish to manage the risk associated with variable interest rates. By setting a cap, borrowers can ensure that their interest payments do not exceed a certain level, providing a safeguard against rising rates. Conversely, floors can protect lenders by guaranteeing a minimum interest rate, ensuring a steady income stream. The integration of these tools into financial strategies highlights the growing sophistication of risk management practices in the derivatives market.

From a regional perspective, North America continues to dominate the global interest rate futures options market, accounting for the largest share in 2024. The regionÂ’s mature financial infrastructure, deep capital markets, and presence of major exchanges such as CME Group and ICE have cemented its leadership position. Europe follows closely, supported by active government bond markets and a strong institutional investor base. Meanwhile, Asia Pacific is emerging as a high-growth region, driven by financial market liberalization, rising foreign investment, and the adoption of advanced trading technologies. Latin America and the Middle East & Africa are

This paper investigates the extent to which the high macroeconomic volatility experienced in the classical Gold Standard era of US history can be attributed to the monetary policy regime per se as distinct from other shocks. For this purpose, we estimate a small dynamic stochastic general equilibrium model for the classical Gold Standard era. We use this model to conduct a counterfactual experiment to assess whether a monetary policy conducted on the basis of a Taylor rule characterizing the Great Moderation data would have led to different outcomes for macroeconomic volatility and welfare in the Gold Standard era. The counterfactual Taylor rule significantly reduces inflation volatility, but at the cost of higher real-money and interest-rate volatility. Output volatility is very similar. The end result is no welfare improvement.

Summary: Quarterly time series (starting in 1959Q4) of estimates of macroeconomic stars and output gap. These estimates of stars and other model objects were developed using a semi-structural model to jointly estimate “stars” — long-run levels of output (its growth rate), the unemployment rate, the real interest rate, productivity growth, price inflation, and wage inflation. It features links between survey expectations and stars, time-variation in macroeconomic relationships, and stochastic volatility. Survey data help discipline stars’ estimates and have been crucial in estimating a high-dimensional model since the pandemic. The model has desirable real-time properties, competitive forecasting performance, and superior fit to the data compared to variants without the empirical features mentioned above. The paper that developed the model is available from the Working Paper Series of the Federal Reserve Bank of Cleveland - A Unified Framework to Estimate Macroeconomic Stars. For the historical real-time archives: https://github.com/zamansaeed/macrostars/Citation:To learn more about the data and the model, see:Zaman, Saeed. 2024. "A Unified Framework to Estimate Macroeconomic Stars." Working Paper No. 21-23R2. Federal Reserve Bank of Cleveland. https://doi.org/10.26509/frbc-wp-202123r2.JEL CodesC5, E4, E31, E24, O4File Description:Each vintage includes the posterior mean, 68% and 90% Credible Intervals for:U-star: long-run level of unemployment rateR-star: long-run real rate of interestPi-star: long-run level of price inflationP-star: long-run level of productivity growthW-star: long-run level of nominal wage inflationG-star: growth rate of potential outputOutput Gap: cyclical assessment of the US economy Persistence in price inflation (gap)Persistence in nominal wage inflation (gap)Slope of the price Phillips CurveSlope of the wage Phillips CurveShort-run passthrough from prices to wagesWedge: between W-star and (P-star + Pi-star)D: the catch all component in R-star equationStochastic volatility price inflation gapStochastic volatility nominal wage inflation gapStochastic volatility labor productivity gapStochastic volatility interest rate gapStochastic volatility output gapStochastic volatility UR gapDisclaimer:These data are updated by the authors and are not an official product of the Federal Reserve Bank of Cleveland.Latest Estimates of Stars (and the output gap):-- based on US data through 2025Q2In bold is the (posterior) Mean estimate and in parentheses 68% coverage Interval:U-star (long-run level of unemployment rate): 4.4% (4.0% to 4.8%)R-star (long-run real rate of interest): 1.5% (0.8% to 2.2%)Pi-star (long-run level of price inflation): 2.2% (1.7% to 2.6%)P-star (long-run level of productivity growth): 1.7% (1.1% to 2.2%)W-star (long-run level of nominal wage inflation): 3.6% (3.2% to 4.0%)G-star (growth rate of potential output): 2.7% (2.5% to 3.0%)Output Gap (cyclical assessment of the US economy): +0.3% (-0.6% to +1.2%)

Index Time Series for BNY Mellon ETF Trust - BNY Mellon Ultra Short Income ETF. The frequency of the observation is daily. Moving average series are also typically included. The fund normally invests at least 80% of its net assets in investment grade, U.S. dollar denominated fixed, variable, and floating rate debt or cash equivalents. The advisor typically seeks to maintain an effective duration of one year or less, although, under certain market conditions, such as in periods of significant volatility in interest rates and spreads, its duration may be longer than one year. The fund's portfolio, under normal market conditions, will have an average credit rating of at least A or equivalent.

Annual averages (average annual financing of overall industry in billion rupees).