In economic studies and popular media, interest rates are routinely cited as a major factor behind commodity price fluctuations. At the same time, the transmission channels are far from transparent, leading to long-running debates on the sign and magnitude of interest rate effects. Purely empirical studies struggle to address these issues because of the complex interactions between interest rates, prices, supply changes, and aggregate demand. To move this debate to a solid footing, we extend the competitive storage model to include stochastically evolving interest rates. We establish general conditions for existence and uniqueness of solutions and provide a systematic theoretical and quantitative analysis of the interactions between interest rates and prices.

This paper constructs daily measures of the real interest rate and expected inflation using commodity futures prices and the term structure of Treasury yields. We find that commodity futures markets respond to surprise increases in the federal funds rate target by raising the inflation rate expected over the next three to nine months. There is no evidence that the real interest rate responds to surprises in the federal funds target. The data from the commodity futures markets are highly volatile. We show that one can substantially reduce the noise using limited information estimators such as the median change. Nevertheless, the basket of commodities actually traded daily is quite narrow and we do not know whether our observable rates are closely connected to the unobservable inflation and real rates that affect economy-wide consumption and investment decisions.

The balanced annual panel data for 32 sub-Saharan countries from 2000 to 2020 was used for this study. The countries and period of study was informed by availability of data of interest. Specifically, 11 agricultural commodity dependent countries, 7 energy commodity dependent countries and 14 mineral and metal ore dependent countries were selected (Appendix 1). The annual data comprised of agricultural commodity prices, global oil prices (GOP) and mineral and metal ore prices, export value of the dependent commodity, total export value of the country, real GDP (RGDP) and terms of trade (TOT). The data for export value of the dependent commodity, total export value of the country, real GDP and terms of trade was sourced from world bank database (World Development Indicators). Data for agricultural commodity prices, global oil prices (GOP) and mineral and metal ore prices are obtained from World Bank commodity price data portal. This study used data from global commodity prices from the World Bank's commodity price data site since the error term (endogenous) is connected with each country's commodity export price index. The pricing information covered agricultural products, world oil, minerals, and metal ores. One benefit of adopting international commodity prices, according to Deaton and Miller (1995), is that they are frequently unaffected by national activities. The utilization of studies on global commodity prices is an example (Tahar et al., 2021). The commodity dependency index of country i at time i was computed as the as the ratio of export value of the dependent commodity to the total export value of the country. The commodity price volatility is estimated using standard deviation from monthly commodity price index to incorporate monthly price variation (Aghion et al., 2009). This approach addresses challenges of within the year volatility inherent in the annual data. In footstep of Arezki et al. (2014) and Mondal & Khanam (2018), standard deviation is used in this study as a proxy of commodity price volatility. The standard deviation is used because of its simplicity and it is not conditioned on the unit of measurement.

CRB Index fell to 373.31 Index Points on July 14, 2025, down 0.01% from the previous day. Over the past month, CRB Index's price has fallen 1.86%, but it is still 10.05% higher than a year ago, 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 July of 2025.

Gold fell to 3,350.92 USD/t.oz on July 14, 2025, down 0.18% from the previous day. Over the past month, Gold's price has fallen 0.98%, but it is still 38.32% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on July of 2025.

The International Financial Statistics database covers about 200 countries and areas, with some aggregates calculated for selected regions, plus some world totals. Topics covered include balance of payments, commodity prices, exchange rates, fund position, government finance, industrial production, interest rates, international investment position, international liquidity, international transactions, labor statistics, money and banking, national accounts, population, prices, and real effective exchange rates.

Silver rose to 38.37 USD/t.oz on July 11, 2025, up 3.65% from the previous day. Over the past month, Silver's price has risen 5.59%, and is up 24.68% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Silver - values, historical data, forecasts and news - updated on July of 2025.

Financial Derivatives Market Outlook

The global financial derivatives market size was valued at approximately USD 25 trillion in 2023 and is projected to reach USD 40 trillion by 2032, growing at a CAGR of 5.6% during the forecast period. The primary growth factor driving this market is the increasing demand for risk management tools and hedging strategies, particularly in volatile economic conditions. As businesses seek to protect themselves from fluctuations in interest rates, currency exchange rates, and commodity prices, the utilization of financial derivatives becomes increasingly critical. This growing need for financial stability and predictability is propelling the adoption of financial derivatives globally.

One of the significant growth factors for the financial derivatives market is the rising globalization of trade and investment. The interconnectedness of the global economy has heightened the exposure of firms to various financial risks, such as currency and interest rate risks. Consequently, there is a growing demand for derivatives as effective tools for managing these exposures. Additionally, advancements in financial markets infrastructure and technology have facilitated easier access to derivative products, further supporting market growth. These advancements include electronic trading platforms, sophisticated risk management software, and improved regulatory frameworks, all of which have streamlined the trading and utilization of derivatives.

Another key driver for the financial derivatives market is the increasing sophistication of institutional investors. Entities such as pension funds, mutual funds, and hedge funds are employing complex strategies involving derivatives to enhance returns and manage portfolio risks. The growing presence of hedge funds in particular, which are known for their aggressive derivative strategies, has notably contributed to market expansion. Moreover, the continuous development of new derivative products tailored to meet the specific needs of these sophisticated investors has led to a more dynamic and diverse market landscape.

The regulatory environment also plays a crucial role in shaping the financial derivatives market. Post-2008 financial crisis reforms, such as the Dodd-Frank Act and the European Market Infrastructure Regulation (EMIR), have mandated greater transparency and reduced counterparty risks in derivatives trading. While these regulations have initially posed challenges, they have ultimately fostered a more robust and trustworthy market. Improved regulatory oversight has instilled confidence among market participants, leading to increased participation and growth. Moreover, ongoing regulatory advancements continue to evolve, ensuring the market adapts to new financial realities and risks.

Type Analysis

The financial derivatives market is segmented by type into futures, options, swaps, and forwards. Futures contracts, which are standardized agreements to buy or sell an asset at a predetermined price at a specified future date, constitute a substantial portion of the market due to their widespread use in hedging against price volatility in various underlying assets, such as commodities, currencies, and indices. The growing volume of trade in commodities and the need for price stability among producers and consumers have significantly boosted the demand for futures contracts. Additionally, the advent of electronic trading platforms has made trading futures more accessible and efficient, contributing to the segment's growth.

Options, which grant the holder the right but not the obligation to buy or sell an asset at a predetermined price before or at the expiration date, are another crucial segment of the financial derivatives market. The flexibility they offer, combined with the potential for high returns, makes options particularly attractive to both individual and institutional investors. The use of options in speculative strategies, as well as in risk management to hedge against unfavorable price movements, has seen steady growth. The development of exchange-traded options has further enhanced transparency and liquidity in this segment, attracting more participants.

Swaps, which involve the exchange of cash flows or liabilities between parties, have gained prominence, especially interest rate swaps and currency swaps. Interest rate swaps allow entities to manage exposure to fluctuations in interest rates, which is particularly relevant in enviro

High price volatility among various commodities and the recent lowering of interest rates has fueled strong growth among commodity contracts intermediation brokers. While the national economy has continued to recover following a period of high inflationary pressures, recent rate cuts by the Federal Reserve and continued price volatility of oil and agricultural products strengthened commodity contracts’ popularity. Short-term contracts and future continue to facilitate interest among brokers, with revenue growing at a CAGR of 4.6% to an estimated $21.8 billion through the end of 2024, including an estimated 2.3% boost in 2024 alone. Profit continues to remain steady, as higher price volatility and lower interest rates continue to facilitate favorable market conditions for commodity traders. Banks, once outsized players in the industry, have significantly downsized or completely ended their commodity trading activities. This has put significant downward pressure on revenue as these institutions have been forced to limit proprietary trading due to the Volcker rule, enacted prior to the current period. The decreased presence of banks in the industry has allowed smaller players to enter the industry, exacerbating fragmentation among various service groups. The inflationary spike played a key role in buoying growth, with recent geopolitical conflicts in the Middle East and Europe strengthening commodity price volatility. Moving forward, commodity contract intermediaries face a less certain landscape, as anticipated declines in global oil prices and the agricultural price index will dampen the popularity of long-term commodity trades. Increased demand for metal and energy products and the low inventories of metal commodities are expected to sustain a significant revenue stream for brokers. However, further uncertainty surrounding rising tensions in the Middle East will impact the types of trades made by commodity traders. Greater automation and adoption of new technologies such as blockchain will offer a workflow enhancement in the longer term. Nonetheless, an expected decline in global oil prices is poised to cause revenue to fall at a CAGR of 1.0% to an estimated $20.8 billion through the end of 2029.

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.

Nickel Commodity Price Outlook: DJ Commodity Nickel Index Faces Volatility

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

ABSTRACT The paper discusses the determination of inflation in Brazil, especially after the great recession of 2015-2016, to assess the adequacy of manipulating interest rates to control the rise in prices due to permanent cost pressure. The burden of using the interest rate to fight cost inflation is to create a highly conventional level of the real interest rate, which benefits the rentier class in a financialized economy. In the light of the post-Keynesian macroeconomics, a high-interest rate convention keeps the economy with a low growth rate and a low investment rate, which in the case of the Brazilian economy has resulted in a regression in the productive matrix and productivity stagnation, and both contribute to perpetuating cost pressures on prices. The empirical analysis corroborates the discussion about recent inflation having its origin in cost pressures over which the interest rate impact for its control is limited. We complement the empirical analysis by testing the response to the SELIC interest rate of the variables used to explain the fluctuation of market prices and administered prices: commodity price index, exchange rate and activity level. As expected, the impact of an increase in the interest rate appreciates the exchange rate, favouring inflation control and reducing the level of activity but has no impact on the commodity price index.

This annual report is a compendium of historical statistics covering the agriculture, fisheries, food and forestry sectors. It provides a set of comprehensive statistical tables on Australian and world prices, production, consumption, stocks and trade for 19 rural commodities. The commodities covered include grains and oilseeds, livestock, livestock products, food, wool, horticulture, forestry products and fisheries products. This report also contains statistics on agricultural water use and macroeconomic indicators such as economic growth, employment, balance of trade, exchange rates and interest rates.

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.

Commodity Industrial Metals Index: The Future of Global Manufacturing?

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.

SGI Commodities Optimix TR index Sees Moderate Growth Ahead.

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.

DJ Commodity Zinc Index Forecast

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

Predictions for the S&P/TSX index indicate a potential for positive returns, driven by improving economic conditions, corporate earnings growth, and a favorable interest rate environment. However, risks include geopolitical uncertainty, volatility in commodity prices, and potential interest rate increases. Investors should exercise caution and diversify their portfolios to mitigate these risks.

Futures Trading Service Market Outlook

The global futures trading service market size was valued at USD 5.2 billion in 2023 and is projected to reach USD 10.8 billion by 2032, growing at a CAGR of 8.5% during the forecast period. The significant growth in market size can be attributed to increased trading activities, technological advancements in trading platforms, and rising interest from individual and institutional investors alike.

A major growth factor for the futures trading service market is the rising prevalence of advanced trading platforms and technologies. Technological advancements have made futures trading more accessible and efficient, enabling traders to execute complex strategies with greater ease. The integration of artificial intelligence and machine learning into trading algorithms has also enhanced decision-making processes, resulting in improved trading outcomes and increased market participation.

Another key driver is the increased participation of institutional investors. As financial markets become more interconnected, institutional investors are increasingly turning to futures trading to hedge against market volatility and optimize their portfolios. The availability of diverse asset classes within futures trading, including commodities, financials, and indices, provides these investors with a wide range of options to manage their risk exposure effectively.

Moreover, the growing interest among individual investors is fueling market expansion. The democratization of trading platforms has lowered entry barriers, allowing retail traders to participate in futures markets. Educational resources and advisory services provided by brokerage firms further support individual investors in navigating the complexities of futures trading, thereby contributing to market growth.

Commodity Services play a pivotal role in the futures trading market, offering a wide range of opportunities for both hedgers and speculators. These services encompass the trading of various commodities such as agricultural products, energy resources, and precious metals. The inherent volatility in commodity prices makes futures contracts an attractive tool for managing risk and securing price stability. As global demand for commodities continues to rise, driven by factors like population growth and industrialization, the importance of robust commodity services in futures trading becomes increasingly evident. These services not only facilitate efficient price discovery but also provide a platform for market participants to capitalize on price movements and achieve their financial objectives.

In terms of regional outlook, North America holds the largest market share due to the presence of major financial institutions and advanced trading infrastructure. The Asia Pacific region is expected to witness the highest growth rate, driven by increasing economic development, rising disposable incomes, and the expansion of financial markets in countries like China and India. Europe also shows significant potential, with well-established financial hubs such as London and Frankfurt contributing to market growth.

Service Type Analysis

The futures trading service market can be segmented by service type into brokerage services, trading platforms, advisory services, and others. Brokerage services dominate the market, providing essential intermediary functions that facilitate trading activities. These services are crucial for both individual and institutional investors, offering benefits such as access to diverse markets, real-time data, and personalized customer support. The competitive landscape among brokerage firms is intense, with key players continuously enhancing their offerings to attract and retain clients.

Trading platforms are another significant segment within the futures trading service market. These platforms offer a suite of tools and features that enable traders to execute trades, monitor market conditions, and analyze trends. The evolution of trading platforms from desktop-based applications to web-based and mobile solutions has made it easier for traders to engage with the market anytime and anywhere. Features such as automated trading, advanced charting, and customizable interfaces are driving the adoption of these platforms among traders.

Advisory services play a critical role in guiding investors through the complexities of futures trading. These services provide expert anal

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.

Will the Commodity Grains Index Drive Future Prices?

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

Discover the latest shifts in corn futures as volume and open interest see significant changes, affecting global commodity trends.

Stock and commodity exchanges can benefit from various sources of revenue, ranging from fees charged through the purchasing and selling of stocks and commodities to the listing of companies on exchanges with IPOs. Yet, this hasn't meant exchanges have been free of challenges, with many companies looking to more attractive overseas markets in countries like the US that embrace stronger growth. The most notable culprits have been ARM and CRH, refusing to put up with the increasingly cheaper valuations offered by UK stock exchanges. Stock and commodity exchange revenue is expected to boom at a compound annual rate of 11.5% over the five years through 2024-25 to £15.4 billion. Boosted by the London Stock Exchange Group's Refinitiv purchase in 2021-22, the growth numbers seem inflated. The industry saw ample consolidations, aided by MiFID II's initiation in 2018. However, M&As have now decreased because of high borrowing costs. New reporting demands have bumped up regulatory costs, resulting in thinner profits. Banks, aligning with Basel IV, are pulling back on investments. Post-COVID market turbulence fuelled trades, but it's slowing down with economic stabilisation. The inflation slowdown pushes investors towards higher-value securities, boosting trade value despite lower volumes. The weak pound has been beneficial for revenue, especially for the LSEG, bolstered by dollar-earning companies in the FTSE 100. Stock and commodity exchange industry revenue is expected to show a moderate increase of 1.3% in 2024-25. Revenue is forecast to climb at a compound annual rate of 4.1% over the five years through 2029-30 to £18.8 billion. The cautious descent of interest rates from the Bank of England will slow down volatility and ensure greater business confidence in the UK. This will bring back up consolidation activity to support revenue growth, reviving the digital information and exchange markets. The most pressing concern for the industry will be potential limitations on access to the EEA for the clearing segment of the industry, which could shatter short-term growth and keep the tap running for companies exiting UK exchanges.