Silver fell to 57.28 USD/t.oz on December 2, 2025, down 1.22% from the previous day. Over the past month, Silver's price has risen 19.11%, and is up 84.81% 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 December of 2025.

Dataset of historical annual silver prices from 1970 to 2022, including significant events and acts that impacted silver prices.

Silver Prices - Historical chart and current data through 2025.

Stay informed with real-time charts of international precious metal prices. Monitor spot prices for Silver in USD, GBP, and EUR. Access live updates here >>

This dataset contains the predicted prices of the asset Silver over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

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.

View monthly updates and historical trends for Silver Price. from United States. Source: World Bank. Track economic data with YCharts analytics.

Context

I'm creating a new website (centralbankanalytics.com) in which I need this type of data. I didn't found it easily available as I had to scrape it from an interactive graph, so now I upload it here for everyone

Content

In this dataset you can find real and nominal silver prices since 1791 to 2020. The explanation of the differences between real and nominal prices are:

· Nominal values are the current monetary values. · Real values are adjusted for inflation and show prices/wages at constant prices. · Real values give a better guide to what you can actually buy and the opportunity costs you face.

Example of real vs nominal:

· If you receive an 8% increase in your wages from £100 to £108, this is the nominal increase. · However, if inflation is 2%, then the real increase in wages is (8-2%) 6%. · The real wage is a better guide to how your living standards changes. It shows what you are actually able to buy with the extra increase in wages. · If wages increased 80%, but inflation was also 80%, the real increase in wages would be 0% – in effect, despite the monetary increase in wages of 80%, the amount of goods and services you could buy would be the same.

Hope this dataset is useful for you! Any questions or answers do not hesitate in contact me.

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.

Philadelphia Gold and Silver Index: The Future of Precious Metals?

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.

Philadelphia Gold and Silver Index: A Beacon of Precious Metal Value?

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 dataset contains the predicted prices of the asset Pure Silver over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Hong Kong Local Silver Prices data was reported at 1,131.000 USD/10 oz in Oct 2018. This records an increase from the previous number of 1,117.000 USD/10 oz for Sep 2018. Hong Kong Local Silver Prices data is updated monthly, averaging 500.000 USD/10 oz from Jan 1981 (Median) to Oct 2018, with 454 observations. The data reached an all-time high of 3,761.000 USD/10 oz in Apr 2011 and a record low of 277.000 USD/10 oz in Feb 1993. Hong Kong Local Silver Prices data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR – Table HK.P003: Gold and Silver Prices.

United States Import Value by Product: Silver: Bullion, Ag Content data was reported at 151,000.000 USD th in Apr 2018. This records an increase from the previous number of 135,000.000 USD th for Mar 2018. United States Import Value by Product: Silver: Bullion, Ag Content data is updated monthly, averaging 200,490.000 USD th from Jan 2007 (Median) to Apr 2018, with 136 observations. The data reached an all-time high of 656,000.000 USD th in Mar 2011 and a record low of 34,100.000 USD th in Feb 2009. United States Import Value by Product: Silver: Bullion, Ag Content data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.WB006: Import Value.

Copper rose to 5.19 USD/Lbs on December 2, 2025, up 0.35% from the previous day. Over the past month, Copper's price has risen 3.22%, and is up 25.36% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Copper - values, historical data, forecasts and news - updated on December of 2025.

In 2024, the average nominal price of silver in India was ****** Indian rupees for one kilogram, which was an increase of over ****** rupees from the previous year, and the highest figure during the period of consideration.

Rhodium rose to 8,050 USD/t oz. on December 2, 2025, up 0.94% from the previous day. Over the past month, Rhodium's price has fallen 1.23%, but it is still 75.96% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Rhodium - values, historical data, forecasts and news - updated on December of 2025.

Snapshot img { margin: 10px !important; } Silver Iodide Market Size 2025-2029

The silver iodide market size is forecast to increase by USD 116.2 million, at a CAGR of 6.8% between 2024 and 2029.

The global silver iodide market is expanding, largely due to the increased use of silver iodide for cloud seeding at airports for fog dissipation, which enhances aviation safety and operational efficiency. This application leverages the material's ice nucleation properties to clear visibility, reducing flight delays. Concurrently, a significant trend is the rising demand for silver iodide in pharmaceutical and medical applications, where its antimicrobial action is valued. This dual demand from both atmospheric science and the sanitizer market is creating a diverse growth landscape for this versatile inorganic compound, which shares supply chain elements with the potassium iodide market.Despite these positive demand signals, the market's stability is challenged by fluctuating raw material prices, particularly the cost of silver, which directly impacts the production cost structure. This volatility introduces uncertainty for manufacturers in the silver salt market and can affect pricing for end-users across all applications, from weather modification to antiseptic formulation. Companies must navigate this economic pressure through strategic sourcing and inventory management to maintain profitability and competitiveness. This financial dynamic requires careful management to ensure the long-term health of the broader iodine and silver nitrate derivative industries.

What will be the Size of the Silver Iodide Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019 - 2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe global silver iodide market is characterized by a dynamic interplay between established applications and emerging technological frontiers. The material's ice nucleation properties remain central to its use as a cloud seeding agent for precipitation enhancement and fog dissipation, forming the bedrock of demand. Simultaneously, its photosensitive material characteristics and antimicrobial action are being leveraged in specialized sectors. The ongoing refinement of weather modification techniques and a focus on raw material sourcing efficiency reflect the maturity of its primary application, while R&D into its semiconductor grade and scintillator grade capabilities points toward future diversification.Advancements are continually shaping the industry's trajectory, with a focus on optimizing the crystalline structure for more effective heterogeneous nucleation in supercooled clouds. The synthesis of high-purity inorganic compounds is critical for applications in diagnostic imaging and as an antiseptic formulation in the sanitizer market. The production cost structure is heavily influenced by the silver salt market, demanding strategic supply chain management. This evolution is driven by the need for innovative solutions in water resource management and medical treatments, highlighting the material's expanding role beyond its historical use in the photographic film industry.

How is this Silver Iodide Industry segmented?

The silver iodide industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments. ApplicationCloud seedingAntiseptic materialPhotosensitive materialOthersGrade typeNucleation gradeScintillator gradeSemiconductor gradeDistribution channelOfflineOnlineGeographyAPACChinaIndiaIndonesiaJapanAustraliaThailandNorth AmericaUSCanadaMexicoEuropeRussiaGermanyFranceSpainUKItalyMiddle East and AfricaUAESouth AfricaEgyptQatarSouth AmericaBrazilArgentinaColombiaRest of World (ROW)

By Application Insights

The cloud seeding segment is estimated to witness significant growth during the forecast period.The cloud seeding segment is central to the market, driven by the escalating global need for effective water resource management. This application utilizes silver iodide for its superior ice nucleation properties to induce precipitation enhancement, addressing water scarcity in agriculture and municipalities. This segment, representing over 44% of the application market, is primarily sustained by government-funded weather modification programs aimed at combating drought. The focus remains on leveraging its crystalline structure for efficient ice crystal formation.Technological advancements and large-scale operational deployments define this segment's momentum. Governments and private entities are investing in sophisticated delivery systems, including ground-based generators and aircraft, to optimize the dispersion of the cloud seeding agent. The goal is to improve the efficiency of heterogeneous nucleation and ma

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.

Silver's Uncertain Future?

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 dataset contains the predicted prices of the asset Base Silver over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

LME Index rose to 4,700 Index Points on October 29, 2025, up 0.79% from the previous day. Over the past month, LME Index's price has risen 7.33%, and is up 13.22% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. LME Index - values, historical data, forecasts and news - updated on December of 2025.