This data is the month-end data of the time series from January 2009 to March 2023 for four commodities such as gold soybean crude oil and natural gas. These time series data can be used to estimate the market's short-term interest rate along with the Vasicek model and joint radiation term structure model., , , # Short-term interest rate estimates based on futures markets

Abstract: This data is the month-end data of the time series from January 2009 to March 2023 for four commodities such as gold soybean crude oil and natural gas. These time series data can be used to estimate the market short-term interest rate together with the Vasicek model and the joint radiation term structure model

Usage: The data in Table 1 and Table 2 can be read into the established interest rate estimation model code using python to estimate the short-term interest rate

Data structure: month-end time series data; The xlsx tables mainly include Table 1 and Table 2

Source: Bloomberg Data Terminal

Specific variable definition:

• The gold futures price is the futures price data from the end of January 2009 to the end of March 2023, in ounces per dollar
• Soybean futures prices are futures price data from the end of January 2009 to the end of March 2023, in tons per dollar
• Natural gas futures price...

This dataset offers end-of-day (EoD) pricing for a wide range of financial derivatives, including securities and interest rate futures. It focuses on key benchmarks such as SONIA (Sterling Overnight Index Average), SOFR (Secured Overnight Financing Rate), and €STR (Euro Short-Term Rate), covering major currencies: USD, GBP, and EUR as well as others. The data is crucial for financial institutions, analysts, and traders involved in interest rate hedging and risk management.

Key features of the dataset include:

End-of-Day Prices: Daily closing prices for interest rate futures across multiple currencies. Interest Rate Benchmarks: Data on SONIA, SOFR, and €STR futures, reflecting short-term interest rate movements. Cross-Currency Data: Pricing for USD, GBP, and EUR-denominated futures, allowing cross-market comparisons and analysis. Trading Volume & Open Interest: Insights into market activity and outstanding contract positions. This dataset supports accurate risk assessment, financial modeling, and investment strategy development in the global derivatives market.

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TAIBIR's latest secondary trading interest rate quotations. 1. The company's display order follows the order of specialized and comprehensive ticket brokers, and then the order of participant codes. 2. Secondary trading interest rate quotations refer to the short-term purchase and sale quotation interest rates of short-term securities or asset-based securities other than short-term beneficiary securities. (Taiwan Central Securities Depository)

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.

Analysis of the October 1, 2025, wheat futures market, detailing price declines across all major contracts, changes in trading volume, and an increase in open interest.

ABSTRACT We aim at obtaining a simple econometric model that allows us to build a confidence interval for the dispersion of the bids made by financial institutions at the central bank weekly auctions of short-term securities in Brazil. Under competitive conditions (e. g., no coalition between a few financial institutions) we assume that the bids’ dispersion is associated with the volatility of the daily interest rate futures prices and the daily interest rates that had prevailed during the days prior to the auction. Based on that assumption, our model succeeds in separating the two auctions with extremely high volatility. ln one of them, the high dispersion could be predicted using the other interest rate markets’ data; in the other the dispersion fell outside the confidence interval for the predicted dispersion. This can be used as empirical evidence of an attempt to comer the market that has indeed occurred at that date.

Context

Soy beans are a major agricultural crop.

Content

Compilation of Soybean prices and factors that effect soybean prices. Daily data. Temp columns are daily temperatures of the major U.S. grow areas. Production and Area are the annual counts from each country (2018 being the estimates). Prices of commodities are from CME futures and are NOT adjusted for inflation. Updates of these CME futures can be found on Quandl. Additional data could be added, such as, interest rates, country currency prices, country import data, country temperatures.

More raw data I used to assemble this.
https://github.com/MotorCityCobra/Soy_Data_Collection Browse my other projects and offer me a job.

Acknowledgements

https://www.quandl.com/

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The NSE Futures and Options (F&O) dataset is a collection of data related to derivatives traded on the National Stock Exchange of India. Derivatives, such as futures and options, are financial instruments whose value is derived from an underlying asset, such as stocks, indices, commodities, or currencies. The F&O segment allows traders and investors to speculate on or hedge against future price movements of these assets.

Key Components of the NSE Futures and Options Dataset: 1. Futures Data: Futures Contracts: Agreements to buy or sell an underlying asset at a predetermined price at a future date. Underlying Asset: The asset on which the contract is based (e.g., individual stocks, stock indices like NIFTY, commodities). Contract Specifications: Expiry Date: The date on which the contract will expire. Contract Price: The agreed-upon price for the asset. Lot Size: The quantity of the underlying asset that each contract represents. Open Interest: The total number of outstanding (unsettled) contracts. Volume: The number of contracts traded during a specific period. Settlement Price: The final price of the contract upon expiry.

1. Options Data: Options Contracts: These give the buyer the right (but not the obligation) to buy (Call Option) or sell (Put Option) an underlying asset at a predetermined price before or at a certain expiration date. Option Types: Call Option: Gives the holder the right to buy the asset. Put Option: Gives the holder the right to sell the asset. Strike Price: The price at which the holder of the option can buy/sell the underlying asset. Expiry Date: The date by which the option must be exercised. Premium: The price paid by the option buyer to acquire the option contract. Implied Volatility: A measure of the market’s expectation of the underlying asset's volatility. Greeks: Quantities representing the sensitivity of the option’s price to various factors: Delta: Sensitivity to price changes in the underlying asset. Theta: Sensitivity to time decay (as the option approaches expiry). Vega: Sensitivity to changes in the asset's volatility. Gamma: The rate of change in Delta. Open Interest: Total number of outstanding options contracts. Volume: The number of option contracts traded during a specific period.

2. Option Chain: An option chain is a table showing all available option contracts for a particular stock or index. It includes strike prices, premiums (call and put), open interest, and volume for different expiry dates.

3. Index Derivatives: Futures and options on stock indices like NIFTY 50, Bank NIFTY, etc. These contracts track the performance of the index as the underlying asset.

Key Metrics in F&O Data: Open Interest (OI): The total number of open contracts (both bought and sold) that have not been settled. This helps gauge market participation and liquidity. Price (Premium): In options, the premium is the cost of buying the contract. In futures, the price reflects the contract value. Strike Price: Particularly important for options, it is the price at which the option can be exercised. Expiry Date: Futures and options contracts have specific expiration dates, typically the last Thursday of the month for monthly contracts. Trading Volume: The number of contracts traded within a given period, which can indicate the level of activity in a particular contract.

Use of NSE F&O Data: Speculation: Traders use F&O to speculate on future price movements of stocks, indices, or commodities. Hedging: Investors use F&O to hedge against adverse price movements in their portfolio (for example, buying put options to protect against a market downturn). Arbitrage: Taking advantage of price differences between the underlying asset and its derivative (futures or options).

Data Types: Historical Data: Contains past data on prices, volumes, open interest, etc. for futures and options contracts. Traders use this to analyze trends, patterns, and volatility. Real-time Data: Provides live updates on the price, open interest, and trading volume of contracts. This data is crucial for day traders and high-frequency traders.

How Traders and Analysts Use This Data: Price Action Analysis: Studying how the price of the futures or options contracts changes over time. Open Interest Analysis: A rising OI indicates new money coming into the market, while falling OI can indicate exiting positions. Option Greeks: Traders analyze the Greeks to manage risk and position sizing in options trading. Volatility Analysis: By analyzing implied and historical volatility, traders can gauge market sentiment and potential price swings.

This dataset provides a comprehensive collection of time-series data related to economic indicators in Indonesia and global commodity markets, designed for insightful analysis into price trends, market dynamics, and consumer interest. The data spans various categories, offering a multi-faceted view of factors influencing commodity prices and economic behavior.

Data Categories and Contents:

The dataset is organized into several key categories: - Currency Exchange Rates * data/Mata Uang/: This directory contains historical daily exchange rate data for several currencies against the US Dollar, including Malaysian Ringgit (MYRUSD=X.csv), Singapore Dollar (SGDUSD=X.csv), Thai Baht (THBUSD=X.csv), and Indonesian Rupiah (USDIDR=X.csv). Each file typically includes columns such as Date, Price, Adj Close, Close, High, Low, and Volume.

• Indonesian Food Prices

  • data/Harga Bahan Pangan (Cleaned)/: This folder provides cleaned daily price data for essential food commodities across various provinces in Indonesia. Commodities include:

    • Shallots (Bawang Merah.csv)
    • Garlic (Bawang Putih Bonggol.csv)
    • Medium Rice (Beras Medium.csv)
    • Premium Rice (Beras Premium.csv)
    • Curly Red Chili (Cabai Merah Keriting.csv)
    • Red Cayenne Pepper (Cabai Rawit Merah.csv)
    • Broiler Chicken Meat (Daging Ayam Ras.csv)
    • Pure Beef (Daging Sapi Murni.csv)
    • Consumption Sugar (Gula Konsumsi.csv)
    • Bulk Cooking Oil (Minyak Goreng Curah.csv)
    • Simple Packaged Cooking Oil (Minyak Goreng Kemasan Sederhana.csv)
    • Chicken Eggs (Telur Ayam Ras.csv)
    • Bulk Wheat Flour (Tepung Terigu (Curah).csv) Each file contains daily prices (in Indonesian Rupiah) for individual provinces across Indonesia, with columns for Date and each province.

  • data/Harga Bahan Pangan/: This directory contains raw daily price data for various food commodities, organized by province in separate CSV files (e.g., Aceh.xlsx - Aceh.csv, Bali.xlsx - Bali.csv, Banten.xlsx - Banten.csv). These files likely contain similar price information as their "Cleaned" counterparts but might require additional processing.

• Google Trends Search Interest

  • data/Google Trend/: This extensive collection includes Google Trends search interest data for various commodities and their related terms, categorized by province and for Indonesia nationally. These files (e.g., tepung terigu/Aceh.csv, telur ayam/Indonesia.csv, minyak goreng/Bali.csv, gula/Jawa Barat.csv, daging sapi/DKI Jakarta.csv, daging ayam/Jawa Timur.csv, daging/Sumatera Utara.csv, cabai rawit/Indonesia.csv, cabai merah/Jawa Barat.csv, cabai/Jawa Tengah.csv, beras/Jawa Timur.csv, bawang putih/Jawa Tengah.csv, bawang merah/Jawa Barat.csv, bawang/DKI Jakarta.csv) show the popularity of search queries over time, with columns for Day and search interest values.

• Global Commodity Prices

  • data/Global Commodity Price/: This section includes historical futures data for key global commodities:
    • Crude Oil WTI (Crude Oil WTI Futures Historical Data.csv)
    • Natural Gas (Natural Gas Futures Historical Data.csv)
    • Newcastle Coal (Newcastle Coal Futures Historical Data.csv)
    • Palm Oil (Palm Oil Futures Historical Data.csv)
    • US Sugar #11 (US Sugar #11 Futures Historical Data.csv)
    • US Wheat (US Wheat Futures Historical Data.csv) These files provide daily data including Date, Price, Open, High, Low, Vol., and Change %.

Potential Use Cases:

This dataset can be utilized for a variety of analytical tasks, including:

• Time Series Analysis: Forecast future prices of food commodities, currencies, and global energy resources.
• Economic Impact Studies: Analyze the correlation between global commodity prices, currency exchange rates, and local food prices in Indonesia.
• Market Trend Analysis: Identify and visualize trends in consumer interest for specific food items using Google Trends data.
• Regional Economic Disparities: Compare food prices and search interest across different Indonesian provinces to identify regional variations.
• Predictive Modeling: Develop models to predict inflation, economic stability, or consumer behavior based on the interplay of these diverse data points.

The combination of local Indonesian market data with global commodity and search interest data makes this dataset particularly valuable for researchers and analysts interested in economic forecasting and market analysis.

Gold fell to 4,199.97 USD/t.oz on December 2, 2025, down 0.75% from the previous day. Over the past month, Gold's price has risen 4.93%, and is up 58.92% compared to the same time last year, 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 December of 2025.

Prices for Australia 3Y including live quotes, historical charts and news. Australia 3Y was last updated by Trading Economics this December 2 of 2025.