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What can we learn from the history of gasoline crack spreads?: Long memory, structural breaks and modeling implications

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  • Wang, Yudong
  • Wu, Chongfeng
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    Abstract

    In this paper, we investigate the long-range auto-correlations of crack spreads using a nonparametric method, named detrended moving average (MF-DMA). We find that the auto-correlations display multiscaling behaviors and are dominated by the anti-persistence (mean-reversion) in the long-term. Moreover, the auto-correlations are multifractal, indicating that various small and large fluctuations display different scaling behaviors. Using a technique of rolling windows, we find that some extreme events can drive the degree of anti-persistence and the multifractality (complexity) to rise up. In other words, these events have negative impacts on market efficiency. However, the effects of these events are not alike. We also detect long-range auto-correlations in crack spread volatilities and find a strong persistent behavior and multifractality. Finally, we discuss the modeling implications of the findings on long-range auto-correlated patterns. Our results indicate that ARFIMA-GARCH models can capture the major dynamics of large fluctuations. For small fluctuations, they are misspecified. Interestingly, we find that the strong long-range auto-correlated behaviors do not imply that ARFIMA model which takes long memory into account can outperform random walk model in the sense of out-of-sample prediction. The major reason may be that market complexity exploited in this paper causes the low predictability of ARFIMA model.

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    Bibliographic Info

    Article provided by Elsevier in its journal Economic Modelling.

    Volume (Year): 29 (2012)
    Issue (Month): 2 ()
    Pages: 349-360

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    Handle: RePEc:eee:ecmode:v:29:y:2012:i:2:p:349-360

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    Web page: http://www.elsevier.com/locate/inca/30411

    Related research

    Keywords: Crack spread; Multifractal detrended moving average; Long-range auto-correlations; Modeling implications;

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    Cited by:
    1. Liu, Li & Wan, Jieqiu, 2012. "A study of Shanghai fuel oil futures price volatility based on high frequency data: Long-range dependence, modeling and forecasting," Economic Modelling, Elsevier, vol. 29(6), pages 2245-2253.
    2. Abbas Valadkhani & Martin O'Brien & Amir Arjomandi, 2013. "Examining the nature of the relationship between Tapis crude oil and Singapore petrol prices," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 36(1), pages 27-41.

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