📘 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).
End-of-month, monthly average, and mixed-frequency spot prices, and futures forecasts for 17 primary commodities. Accompanies LCERPA Working Paper 2024-3.
Python-based regression forecasting model for natural gas prices, utilizing historical market data to analyze trends and generate forward-looking price projections.
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.
Analyze and forecast natural gas prices using time series data, with seasonality decomposition and signal detection for trading strategy insights.
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