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Investigating dynamic conditional correlation between crude oil and fuels in non-linear framework: The financial and economic role of structural breaks

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  • Block, Alexander Souza
  • Righi, Marcelo Brutti
  • Schlender, Sérgio Guilherme
  • Coronel, Daniel Arruda

Abstract

To understand the crude oil volatility has been a challenge. The non-linear behavior, the skewed and leptokurtic returns, the presence of structural breaks and the constant political instability in suppliers' countries evidence the necessity of complex models to capture the market volatility. At the same time, crude oil is the raw material for several fuels such as jet fuel, gasoline, diesel and others, having a strong influence over their prices. Thus, this study aims to verify the presence of structural breaks in the volatility series and in the correlations between WTI return and the returns of Gasoline, Kerosene Jet Fuel, Diesel, Heating Oil, Propane and Natural Gas. To reach this objective, we identified which model presents the best fit to estimate the conditional mean between WTI and each fuel and we used a Copula–DCC–GARCH model to estimate the conditional volatility avoiding the frequently unrealistic presumptions of normality. Our main results indicate the necessity of a different model for each analyzed pair and the presence of at least one structural break in the conditional volatility and in the correlation between WTI and each fuel, usually preceded by a structural break in WTI return series.

Suggested Citation

  • Block, Alexander Souza & Righi, Marcelo Brutti & Schlender, Sérgio Guilherme & Coronel, Daniel Arruda, 2015. "Investigating dynamic conditional correlation between crude oil and fuels in non-linear framework: The financial and economic role of structural breaks," Energy Economics, Elsevier, vol. 49(C), pages 23-32.
    • Handle: RePEc:eee:eneeco:v:49:y:2015:i:c:p:23-32
    DOI: 10.1016/j.eneco.2015.01.011
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    Cited by:

    1. Yu, Wenhua & Yang, Kun & Wei, Yu & Lei, Likun, 2018. "Measuring Value-at-Risk and Expected Shortfall of crude oil portfolio using extreme value theory and vine copula," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1423-1433.
    2. Polanco Martínez, Josué M. & Abadie, Luis M. & Fernández-Macho, J., 2018. "A multi-resolution and multivariate analysis of the dynamic relationships between crude oil and petroleum-product prices," Applied Energy, Elsevier, vol. 228(C), pages 1550-1560.
    3. Marchese, Malvina & Kyriakou, Ioannis & Tamvakis, Michael & Di Iorio, Francesca, 2020. "Forecasting crude oil and refined products volatilities and correlations: New evidence from fractionally integrated multivariate GARCH models," Energy Economics, Elsevier, vol. 88(C).
    4. Tiwari, Aviral Kumar & Raheem, Ibrahim Dolapo & Kang, Sang Hoon, 2019. "Time-varying dynamic conditional correlation between stock and cryptocurrency markets using the copula-ADCC-EGARCH model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    5. Zhang, Yi, 2018. "Investigating dependencies among oil price and tanker market variables by copula-based multivariate models," Energy, Elsevier, vol. 161(C), pages 435-446.
    6. Pablo Cansado-Bravo & Carlos Rodríguez-Monroy, 2018. "Persistence of Oil Prices in Gas Import Prices and the Resilience of the Oil-Indexation Mechanism. The Case of Spanish Gas Import Prices," Energies, MDPI, vol. 11(12), pages 1-17, December.
    7. Trabelsi, Nader & Tiwari, Aviral Kumar & Hammoudeh, Shawkat, 2022. "Spillovers and directional predictability between international energy commodities and their implications for optimal portfolio and hedging," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).

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    More about this item

    Keywords

    Fuel conditional volatility; Non-linear framework; Structural breaks in conditional correlations;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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