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Forecasting Crude Oil Price Movements with Oil-Sensitive Stocks

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  • Chen, Shiu-Sheng

Abstract

This paper uses monthly data from 1984:M10 to 2012:M8 to show that oil-sensitive stock price indices, particularly those in the energy sector, have strong power in predicting nominal and real crude oil prices at short horizons (one-month-ahead predictions), using both in- and out-of-sample tests. In particular, the forecasts based on oil-sensitive stock price indices are able to outperform significantly the no-change forecasts. For example, using the NYSE Arca (AMEX) oil index as a predictor, the one-month-ahead forecasts for nominal crude oil prices reduce the mean squared prediction error by between 22% (for the West Texas Intermediate oil price) and 28% (for the Dubai oil price). Moreover, we find that the directional forecast based the AMEX oil index is ignificantly better than a 50:50 coin toss. The novelty of this analysis is that it proposes a new and valuable predictor that both reflects timely market information and is readily available for forecasting the spot oil price.

Suggested Citation

  • Chen, Shiu-Sheng, 2013. "Forecasting Crude Oil Price Movements with Oil-Sensitive Stocks," MPRA Paper 49240, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:49240
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    More about this item

    Keywords

    oil-sensitive stock prices; oil prices; out-of-sample prediction;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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