Econometric Model Using Arbitrage Pricing Theory and Quantile Regression to Estimate the Risk Factors Driving Crude Oil Returns
Sarit Maitra, Vivek Mishra, Sukanya Kundu, Manav Chopra

TL;DR
This paper introduces a novel econometric approach combining Arbitrage Pricing Theory and Quantile Regression to identify and analyze the key risk factors affecting crude oil returns, considering current global economic influences.
Contribution
It integrates APT and QR methods to analyze crude oil risk factors, incorporating recent global events like pandemics and geopolitical issues for a comprehensive risk assessment.
Findings
Key risk factors include industrial production, inflation, energy prices, yield curve shape, and policy uncertainty.
WTI return effects vary with market conditions and volatility levels.
Structural breaks due to global events significantly impact crude oil returns.
Abstract
This work adopts a novel approach to determine the risk and return of crude oil stocks by employing Arbitrage Pricing Theory (APT) and Quantile Regression (QR).The APT identifies the underlying risk factors likely to impact crude oil returns.Subsequently, QR estimates the relationship between the factors and the returns across different quantiles of the distribution. The West Texas Intermediate (WTI) crude oil price is used in this study as a benchmark for crude oil prices. WTI price fluctuations can have a significant impact on the performance of crude oil stocks and, subsequently, the global economy.To determine the proposed models stability, various statistical measures are used in this study.The results show that changes in WTI returns can have varying effects depending on market conditions and levels of volatility. The study highlights the impact of structural discontinuities on…
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Taxonomy
TopicsMarket Dynamics and Volatility
