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The Second RP-PCA Factor and Crude Oil Price Predictability

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  • Qi Shi

Abstract

Although it is notoriously difficult to utilize financial ratios to forecast the crude oil market prices, our study challenges this perception and reveals that the second risk premium principal component analysis (RP-PCA) factor may contain statistically significant information for both in-sample and out-of-sample forecasts of future crude oil prices. Our evidence illustrates that the second RP-PCA factor substantially outperforms many other popular predictors (approximately 30 conventional predictors) in forecasting crude oil prices and generating adequate higher values of economic profits. We conduct a range of informative tests, including bootstrap simulation, success ratio tests, alternative out-of-sample evaluation periods, and structure break tests. Furthermore, we illustrate that the forecasting ability of the second RP-PCA factor may stem from its ability to forecast oil market sentiment. Our study presents a novel and indicatable financial instrument for policymakers to predict crude oil prices robustly. The theoretical motivation of this study links to Cochrane's (2005) framework for general candidate factors in asset pricing.

Suggested Citation

  • Qi Shi, 2024. "The Second RP-PCA Factor and Crude Oil Price Predictability," Prague Economic Papers, Prague University of Economics and Business, vol. 2024(6), pages 662-690.
  • Handle: RePEc:prg:jnlpep:v:2024:y:2024:i:6:id:879:p:662-690
    DOI: 10.18267/j.pep.879
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    References listed on IDEAS

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    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Shiu-Sheng Chen, 2014. "Forecasting Crude Oil Price Movements With Oil-Sensitive Stocks," Economic Inquiry, Western Economic Association International, vol. 52(2), pages 830-844, April.
    3. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
    4. He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021. "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, vol. 97(C).
    5. Ilan Cooper & Paulo Maio, 2019. "Asset Growth, Profitability, and Investment Opportunities," Management Science, INFORMS, vol. 65(9), pages 3988-4010, September.
    6. Martin Lettau & Sydney Ludvigson, 2001. "Consumption, Aggregate Wealth, and Expected Stock Returns," Journal of Finance, American Finance Association, vol. 56(3), pages 815-849, June.
    7. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    8. Yin, Libo & Yang, Qingyuan, 2016. "Predicting the oil prices: Do technical indicators help?," Energy Economics, Elsevier, vol. 56(C), pages 338-350.
    9. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    10. Kewei Hou & Haitao Mo & Chen Xue & Lu Zhang, 2021. "An Augmented q-Factor Model with Expected Growth [Abnormal returns to a fundamental analysis strategy]," Review of Finance, European Finance Association, vol. 25(1), pages 1-41.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    RP-PCA factor; forecasting; crude oil prices; economic profits; oil market sentiment; policymakers;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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