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This work adopts a novel approach to determine the risk and return of crudeoil stocks by employing Arbitrage Pricing Theory (APT) and Quantile Regression(QR).The APT identifies the underlying risk factors likely to impact crude oilreturns.Subsequently, QR estimates the relationship between the factors and thereturns across different quantiles of the distribution. The West TexasIntermediate (WTI) crude oil price is used in this study as a benchmark forcrude oil prices. WTI price fluctuations can have a significant impact on theperformance of crude oil stocks and, subsequently, the global economy.Todetermine the proposed models stability, various statistical measures are usedin this study.The results show that changes in WTI returns can have varyingeffects depending on market conditions and levels of volatility. The studyhighlights the impact of structural discontinuities on returns, which can becaused by changes in the global economy and the demand for crude oil.Theinclusion of pandemic, geopolitical, and inflation-related explanatoryvariables add uniqueness to this study as it considers current global eventsthat can affect crude oil returns.Findings show that the key factors that posemajor risks to returns are industrial production, inflation, the global priceof energy, the shape of the yield curve, and global economic policyuncertainty.This implies that while making investing decisions in WTI futures,investors should pay particular attention to these elements

Econometric Model Using Arbitrage Pricing Theory and Quantile Regression to Estimate the Risk Factors Driving Crude Oil Returns