Commodity Volatility Tool
Ithar
Problem
There is significant uncertainty in commodities markets due to unpredictable volatility. Few solutions incorporate factors for commodities from a perspective in the Middle East, particularly Saudi Arabia. Uncertainty in price changes lead to high commodity prices in oil and gas, copper, steel, phosphate, aluminum and zinc.
Solution
Ithar as model will provide superior volatility prediction with integrated APIs dependent on clients' needs.
Clients get a dashboard that provides ability to engage, change scenarios, forecast when to order commodity based on best price and transportation costs.
Value Proposition
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No one has developed a volatility model including indices, weather forecast, geopolitical risks, technological enhancements and consumer behaviour while maintaining traditional volatility determination
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Details for each statistical method on distinct page
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User-friendly solution, potentially to be co-developed with some clients from private and public sector
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Founder is exclusively developing for Saudi Arabia
How
Ithar uses specialized statistical methods tailored to specific use cases and purposes. The system also requires development of NLP (natural language processing) and RNN (recurring neural networks) components. Quantum computing to maximise computational power is being explored. However, the state of quantum computing and its limitations will most likely not be resolved short-term.
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HCM and Bayesian interference for hierarchical, complex, non-linear relationships with temporal and cross-sectional dimensions of risk transmission
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Reinforcement Learning
NLP
RNN -
How quantums can interact with environment while not causing loss of information and how eavesdropping attempt will not break entanglement to be detectable should be solved
Commodities to be included
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Crude Oil, LNG, Natural Gas
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Copper, Steel, Aluminium and Zinc
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Agriculture Commodity Inclusion
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Weather prediction for phosphate usage, Steel and (Zinc Demand)