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CRB Index fell to 383.47 Index Points on June 20, 2025, down 0.57% from the previous day. Over the past month, CRB Index's price has risen 5.05%, and is up 11.58% compared to the same time last year, 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 June of 2025.

Crude commodity prices refer to the rates at which crude oil is being traded in various markets. Today, the price of Brent crude oil is $60 per barrel, while the price of WTI crude oil is $58 per barrel. Factors such as supply and demand dynamics, geopolitical tensions, and economic indicators influence these prices, which are subject to constant fluctuations. Traders and investors closely monitor crude commodity prices to make informed decisions and assess market trends.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Abia, Borno, Yobe, Katsina, Kano, Kaduna, Gombe, Jigawa, Kebbi, Oyo, Sokoto, Zamfara, Lagos, Adamawa, Market Average

Russia Number of Trades: Commodities Market: Spot Trades data was reported at 0.070 Unit th in Nov 2017. This records an increase from the previous number of 0.060 Unit th for Oct 2017. Russia Number of Trades: Commodities Market: Spot Trades data is updated monthly, averaging 0.000 Unit th from Jul 2012 (Median) to Nov 2017, with 65 observations. The data reached an all-time high of 0.100 Unit th in Jun 2017 and a record low of 0.000 Unit th in Feb 2017. Russia Number of Trades: Commodities Market: Spot Trades data remains active status in CEIC and is reported by Moscow Exchange. The data is categorized under Global Database’s Russian Federation – Table RU.ZA010: Moscow Exchange: All Markets: Number of Trades.

Polypropylene rose to 7,000 CNY/T on June 9, 2025, up 0.24% from the previous day. Over the past month, Polypropylene's price has fallen 1.09%, and is down 7.76% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Polypropylene.

Discover what drives the fluctuating prices of aluminium in the global market and the critical factors that impact its supply and demand, production costs, and geopolitical events in this insightful article. Stay informed and make informed investment decisions!

Instructions for replications of the results in the paper “Cost pass-through in commodity markets with capacity constraints and international linkages” by Reinhard Ellwanger, Hinnerk Gnutzmann and Piotr Śpiewanowski (Journal of Applied Econometrics, 2024). Dataset and Variable Description Tables and Figures in the paper are based on several datasets, described below. Note that the ammonia price series are proprietary to Green Markets / Bloomberg and are not part of the replication package. To replicate results involving the ammonia price series, the series must be obtained directly from the provider to replace missing columns in the Excel files. Code Description All figures and tables except Table 3 (IV estimations) are produced in R. The main file is “Replication_file_part1.Rmd”. It relies on the auxiliary file “helper.R”. The simulations in the online appendix are produced by the file “Replication_file_IV_simulations.Rmd”. The results presented in Table 3 are produced in STATA. The data and code in Table 3 is contained in the folder “Table 3”. The variables are described in the STATA do-file.

Replication data for peer-reviewed article published in Journal of Applied Econometrics. Paper published online December 8, 2024.

Rapeseed rose to 485.54 EUR/T on June 6, 2025, up 1.15% from the previous day. Over the past month, Rapeseed's price has risen 3.47%, and is up 4.41% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Rapeseed Oil.

China Commodity Trading Market over 100 M Yuan: Number of Market: Other Special Market data was reported at 30.000 Unit in 2023. This records a decrease from the previous number of 35.000 Unit for 2022. China Commodity Trading Market over 100 M Yuan: Number of Market: Other Special Market data is updated yearly, averaging 44.500 Unit from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 60.000 Unit in 2012 and a record low of 30.000 Unit in 2023. China Commodity Trading Market over 100 M Yuan: Number of Market: Other Special Market data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.RJA: Commodity Trading Market over 100 Million Yuan: Number of Market.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Kayanza, Ruyigi, Bubanza, Karuzi, Bujumbura Mairie, Muramvya, Gitega, Rumonge, Bururi, Kirundo, Cankuzo, Cibitoke, Muyinga, Rutana, Bujumbura Rural, Makamba, Ngozi, Mwaro, Market Average

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Anbar, Babil, Baghdad, Basrah, Diyala, Dahuk, Erbil, Ninewa, Kerbala, Kirkuk, Missan, Muthanna, Najaf, Qadissiya, Salah al-Din, Sulaymaniyah, Thi-Qar, Wassit, Market Average

CN: Commodity Trading Market over 100 M Yuan: Turnover: Wholesale: Furniture Market data was reported at 80.965 RMB bn in 2023. This records a decrease from the previous number of 86.339 RMB bn for 2022. CN: Commodity Trading Market over 100 M Yuan: Turnover: Wholesale: Furniture Market data is updated yearly, averaging 75.978 RMB bn from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 106.497 RMB bn in 2017 and a record low of 28.846 RMB bn in 2008. CN: Commodity Trading Market over 100 M Yuan: Turnover: Wholesale: Furniture Market data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.RJA: Commodity Trading Market over 100 Million Yuan: Turnover: Wholesale.

HRC Steel rose to 880 USD/T on June 9, 2025, up 0.23% from the previous day. Over the past month, HRC Steel's price has fallen 1.12%, but it is still 21.38% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for HRC Steel.

ZENPULSAR’s data centric AI platform “PUMP” monitors in real time multiple social media networks to track activities related to financial and crypto assets and then analyse them. It detects emerging viral narratives likely to form trends and impact financial assets. PUMP clears out the noise of social media with unmatched speed and accuracy. It identifies viral narratives related to the assets you track, early signals we can spot and act on before the crowds and everyone else. ZENPULSAR’s technology is also leveraged by a variety of clients to manage critical events such as product launches, policy platform developments, reputation crisis management, and disinformation campaigns. We are providing time series social media data relevant to selected assets. The data is extracted from Twitter, Reddit, Seeking Alpha, Telegram, LinkedIn, Facebook, and Weibo.

The data provided can be split into 4 categories: 1. Data describing sentiment of social media posts a. Number of social media posts with bullish/bearish sentiment towards a target asset per period b. Number of upvotes/downvotes, likes, replies, comments, cross-posts of the posts with bullish/bearish sentiment towards target asset per period 2. Data describing activity of social media accounts a. Number of social media posts per period 3. Data describing engagement of social media accounts a. Number of likes and upvotes/downvotes per period b. Number of replies and comments to the posts per period c. Number of retweets and cross-posts per period 4. Data describing credibility of social media accounts a. Number of Social media posts done by accounts identified as bots/not bots per period b. Number of Upvotes/downvotes, likes, replies, comments, cross-posts of the posts done by accounts identified as bots/non-bots per period c. Number of social media posts done by accounts identified as influencers/market analysts per period d. Number of upvotes/downvotes, likes, replies, comments, cross-posts of the posts done by accounts influencers/market analysts per period

Data analytics methodology

Selection of asset-relevant social media posts: This task is done via iterative usage of information retrieval methods such as keyword extraction and topic modelling (LDA, BERTopic, etc.). We extract the keywords for each asset that are commonly used by people. Because a person who wants to influence public opinion on an asset must provide a specific name for the target asset, such as relevant codes or common names, the keywords they choose will help us to identify them. Also, there are fine-tuned models to help us to determine the truth about the financial topics. By combining these methods and models, we can focus on the data to seek the alpha or identify critical events from different influencers.

Financial-related classification: To filter the key samples from large amounts of posts and news, we employ one of the state-of-art NLP models (Roberta-XLM) to achieve the best performance. There were already some pre-trained models focused on the news containing traditional assets such as bonds, FX, and stocks. By using weak-supervision learning and the additional internal data related to less traditional assets like crypto (added via such techniques as pseudo-labelling), our fine-tuned classifier can achieve great accuracy and precision. This is a binary classification to predict whether the post is related to finance or not.

Account classification: To classify an account as a bot or as an authentic user, we apply a combination of the following techniques: ● NLP-based content analysis - we employ transformer models like google MT5 or XLM-Roberta trained on bot post datasets. ● Heuristics-based features (speed of posting, statistical characteristics based on NER analysis results, etc). Those features are fed to the Support Vector machine classifier. ● The format of recent posts from the same user. Many bots have templates for different posts by putting the text together and transforming it. The model can extract features from the format to improve the model. ● Analysis of network topology (bots have a different one from human accounts), specifically betweenness centrality characteristics of an account within an account network (Katz centrality, Pagerank). To classify an account as an influencer or a market analyst, or an abnormal user we apply a combination of the following techniques: ● NLP-based content analysis - transformer models like google MT5 or XLM-Roberta trained on influencer post datasets. ● Analysis of the account following network characteristics of an account, specifically betweenness centrality, within the account network (Katz centrality, Pagerank, Eigenvector centrality). ● Number of followers/reddit karma thresholds.

Sentiment detection: We utilise transformer-based models (FinBert, CryptoBert and CryptoRoberta) finetuned on our internal datasets. The model was trained on cryptocurrency and stock data collected fr...

China Commodity Trading Market over 100 M Yuan: Turnover by Category: Cotton and Linen data was reported at 30.344 RMB bn in 2020. This records a decrease from the previous number of 31.052 RMB bn for 2019. China Commodity Trading Market over 100 M Yuan: Turnover by Category: Cotton and Linen data is updated yearly, averaging 45.639 RMB bn from Dec 2008 to 2020, with 13 observations. The data reached an all-time high of 57.262 RMB bn in 2011 and a record low of 30.344 RMB bn in 2020. China Commodity Trading Market over 100 M Yuan: Turnover by Category: Cotton and Linen data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.RJA: Commodity Trading Market over 100 Million Yuan: Turnover by Category.

Urals Oil fell to 72.18 USD/Bbl on June 23, 2025, down 0.08% from the previous day. Over the past month, Urals Oil's price has risen 25.20%, but it is still 9.56% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Urals Crude.

China Commodity Trading Market over 100 M Yuan: Number of Market: Decoration Materials Market data was reported at 163.000 Unit in 2023. This stayed constant from the previous number of 163.000 Unit for 2022. China Commodity Trading Market over 100 M Yuan: Number of Market: Decoration Materials Market data is updated yearly, averaging 205.000 Unit from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 252.000 Unit in 2013 and a record low of 153.000 Unit in 2008. China Commodity Trading Market over 100 M Yuan: Number of Market: Decoration Materials Market data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.RJA: Commodity Trading Market over 100 Million Yuan: Number of Market.

China Commodity Trading Market over 100 M Yuan: Turnover: Retail: Cloth and Textiles Market data was reported at 0.416 RMB bn in 2023. This records a decrease from the previous number of 0.422 RMB bn for 2022. China Commodity Trading Market over 100 M Yuan: Turnover: Retail: Cloth and Textiles Market data is updated yearly, averaging 1.764 RMB bn from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 7.231 RMB bn in 2009 and a record low of 0.416 RMB bn in 2023. China Commodity Trading Market over 100 M Yuan: Turnover: Retail: Cloth and Textiles Market data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.RJA: Commodity Trading Market over 100 Million Yuan: Turnover: Retail.

The table presents the correlation among illiquidity series and volatility series for all financial markets. The sample runs from January 1, 2010 to March 22, 2021.