Unique ID: 2015109

Division: Office of Information and Regulatory Affairs
Issue Date: February 13th 2019
Last modified: February 22nd 2019
Collaborative

Real-time price discovery in commodity futures markets

Using high-frequency market trading data for real-time prices in agriculture commodities

This project uses high-frequency market trading data to study information shocks and resulting price, volatility, and market quality effects in important agricultural markets.

Project Objective:

Scientific / research

Project Outcomes:

Several journal articles and agency research reports.

Publications Comments:

No publications yet, but several journal articles are in draft stage.

Project Sources
Project Sources
Type Of Institution: National statistical office
Big Data Source: Other
Region: North America
Country Area: United States
Id Country Regional: country
Partnerships
Partnerships
Other Partners: Government institute
Partnership Comments: Financial Data Aggregator
Accessing Data
Accessing Data
Data Access Rights: Broader access rights
Intermediary Comments: All data preparation is done in-house.
Data Access Comments: Expanding the scope of the research is possible, but not being currently considered.
Data Coverage
Data Coverage
Data Coverage: Only a portion of all data
Cost Implication: Commercial
Cost Comments: The data aggregator sells the data for a fee.
Coverage Geo Comments: The markets/time periods studied are captured in detail.
Coverage Period: 2009-2014
Project Details
Project Details
Frequency Comments: The financial data stretches back further, and exists for other commodities, for a higher price.
Data Quality
Data Quality
Quality Framework: Quality of source/input
Quality Aspects Evaluated: Completeness, Usability, Time Factors, Accuracy, including selectivity
Validation Comments: Several methods are used to identify outliers. For example, trading that occurs on weekends and holidays when markets are known to be closed, extreme price differences, zero-volume trades, etc., are identified and shared with the data provider. These ar
Quality Framework Comments: Data are filtered for obvious errors, and these are discussed with the provider.
Quality Assessment Comments: Data accuracy and usability are crucial early steps in our research cycle.
Methodology
Methodology
Methods Used: Traditional statistical methods, Data visualization methods
Methods Comments: Parametric and non-parametric statistical methods are applied to address research questions. Summary data are plotted visually for exploration purposes.
Technologies
Technologies
Technologies: Data visualization tools, Other
Technologies Comments: We use commonly available statistical tools, like R, to organize & visualize data, and estimate models.
Other
Other
Income Level: High-income
Iso: US
Timeframe To Produce Indicator: NA
Frequency Comments: The financial data stretches back further, and exists for other commodities, for a higher price.
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