Unique ID: 2015109
| Division: | Office of Information and Regulatory Affairs |
|---|---|
| Issue Date: | February 13th 2019 |
| Last modified: | February 22nd 2019 |
Cost: FREE
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.
Statistical Area
Project Sources
| Type Of Institution: | National statistical office |
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| Big Data Source: | Other |
| Region: | North America |
| Country Area: | United States |
| Id Country Regional: | country |
Partnerships
| Other Partners: | Government institute |
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| Partnership Comments: | Financial Data Aggregator |
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: | 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
| Frequency Comments: | The financial data stretches back further, and exists for other commodities, for a higher price. |
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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
| 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: | Data visualization tools, Other |
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| Technologies Comments: | We use commonly available statistical tools, like R, to organize & visualize data, and estimate models. |
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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