Method and Device for Valuation of a Traded Commodity

Simple SummaryContent extracted from patent full text and abstract with AI.

The invention provides a method and device for valuing traded commodities using historical price data and a data processor. It calculates the expected future value of a commodity by analyzing patterns in historical data and applying a sparse grid regression technique. This approach transforms past price movements into attribute values that help predict future prices more accurately.

Use CasesContent extracted from patent full text and abstract with AI.

• Financial firms estimating the future value of commodities for trading strategies
• Commodity exchanges assessing price trends for risk management
• Investment managers evaluating commodity-backed portfolios
• Producers and consumers planning supply chain operations based on price forecasts
• Developing automated trading systems for commodity markets

BenefitsContent extracted from patent full text and abstract with AI.

• Improves accuracy in forecasting commodity prices
• Supports better decision-making in trading and investment
• Reduces reliance on manual or subjective valuation methods
• Facilitates automation in financial analysis and trading
• Enables scalable and repeatable analysis for multiple commodities

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Patent Abstract

A method and device for valuation of a traded commodity An embodiment of the invention relates to a method for valuation of a traded commodity by a data processor, wherein a relative or absolute future value of the traded commodity is computed by a determination of an expectation by the data processor, the method comprising the steps of: receiving an historical time series indicating the commodity's value over time in the data processor; transferring the historical time series of the commodity's value into attribute values of at least one attribute representative for internal features of the historical time series; and constructing a function predicting the future value of the commodity based on a sparse grid regression method which takes said attribute values into account.