📘 Home Assignment for the Data Scientist Position (Curves) at Argus Media Group
Python-based regression forecasting model for natural gas prices, utilizing historical market data to analyze trends and generate forward-looking price projections.
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.
Analyze and forecast natural gas prices using time series data, with seasonality decomposition and signal detection for trading strategy insights.
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