Data Science project for forecasting steel and crude oil prices
This report focuses on forecasting steel and crude oil prices by analyzing daily trade data. It examines how economic and geopolitical influences affect these key commodities and uses advanced statistical techniques to select the best forecasting models. The findings offer insights into the future of commodity prices and the dynamics of international trade networks.
A brief explanation for each Python file included:
probability_distribution.py: This file is used for distribution analysis. It assesses the probability distribution of monthly returns.
oil_logreturns.py: This script forecasts the monthly log returns of oil prices.
steel_logreturns.py: Similar to the oil log returns script, this forecasts the monthly log returns for steel prices.
volatility_forecasting.py: This script is forecasting volatility of both steel and oil prices.