PREDMAC A Stochastic Monte Carlo-Driven Predictive Analytics Framework Integrating Regression Modelling for Macroeconomic Factor Evaluation and Forecasting of WTI Crude Oil Price Volatility

Abstract

Introduction: WTI crude oil prices are influenced by multiple macroeconomic factors, primarily related to supply. This study employs regression equations and Monte Carlo simulations to analyze these dependencies and predict price trends.

Objectives: This research aims to establish the relationship between WTI crude oil prices and eight key macroeconomic indicators. Historical data from the past 15 months is compiled to support this analysis.

Methods: A multiple regression model is used to quantify the impact of macroeconomic variables. Multicollinearity is assessed to ensure model robustness, and Monte Carlo simulations are applied for scenario generation.

Results: Findings reveal that certain macroeconomic variables significantly affect crude oil price fluctuations. The model successfully captures dependencies and provides insights into potential price movements.

Conclusions: This study enhances understanding of crude oil price dynamics, aiding investors and policymakers in decision-making. The proposed framework can be extended for further analysis of commodity markets.