📘 Home Assignment for the Data Scientist Position (Curves) at Argus Media Group
Replicates and extends Joëts et al. (2016) on the nonlinear effects of macroeconomic uncertainty on commodity price volatility using a Threshold VAR framework with modern uncertainty proxies (VIX, JLN, CISS).
Henry Hub natural-gas hedging backtest using EIA spot/futures data, with procurement-cost analysis, VaR/ES, hedge effectiveness, and Winter Storm Uri stress testing.
AI-powered commodity price forecasting and market intelligence system using NLP, Machine Learning, Deep Learning, and Gemini LLM for trend analysis, sentiment analysis, and price prediction.
Python-based regression forecasting model for natural gas prices, utilizing historical market data to analyze trends and generate forward-looking price projections.
Analyze and forecast natural gas prices using time series data, with seasonality decomposition and signal detection for trading strategy insights.
Futures positioning and seasonal spread research using COT data and expiry-aligned commodity futures.
Turn weekly corn futures into readable market regimes — and next-week forecasts — with Hidden Markov Models, regime-switching dynamics, and real-world drivers like oil and the dollar.
End-of-month, monthly average, and mixed-frequency spot prices, and futures forecasts for 17 primary commodities. Accompanies LCERPA Working Paper 2024-3.
🌊 ENSO Macro Risk Desk - A Bloomberg-style terminal mapping El Niño/La Niña to commodity & sector risk, with Granger+CCM causal testing. Live on HF Spaces.
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