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Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications

Author

Listed:
  • Liyuan Chen

    (University of York)

  • Paola Zerilli

    (University of York)

  • Christopher F Baum

    (Boston College
    German Institute for Economic Research (DIW Berlin))

Abstract
The crude oil markets have been quite volatile and risky in the past few decades due to the large fluctuations of oil prices. We contribute to the current debate by testing for the existence of the leverage effect when considering daily spot returns in the WTI and Brent crude oil markets and by studying the direct impact of the leverage effect on measures of risk such as VaR and CVaR. More specifically, we model spot crude oil returns using Stochastic Volatility (SV) models with various distributions of the errors. We find that the introduction of the leverage effect in the traditional SV model with Normally distributed errors is capable of adequately estimating risk for conservative oil suppliers in both the WTI and Brent markets while it tends to overestimate risk for more speculative oil suppliers. Our results also show that the choice of financial regulators, both on the supply and on the demand side, would not be affected by the introduction of leverage. Focusing instead on firm’s internal risk management, our results show that the introduction of leverage would be useful for firms who are on the demand side for oil, who use VaR for risk management and who are particularly worried about the magnitude of the losses exceeding VaR while wanting to minimize the opportunity cost of capital. Using the same logic, firms who are on the supply side would be better off not considering the leverage effect.

Suggested Citation

  • Liyuan Chen & Paola Zerilli & Christopher F Baum, 2018. "Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications," Boston College Working Papers in Economics 953, Boston College Department of Economics.
    • Handle: RePEc:boc:bocoec:953
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    References listed on IDEAS

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    Cited by:

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    4. Virbickaitė, Audronė & Nguyen, Hoang & Tran, Minh-Ngoc, 2023. "Bayesian predictive distributions of oil returns using mixed data sampling volatility models," Resources Policy, Elsevier, vol. 86(PA).
    5. Jo-Hui & Chen & Sabbor Hussain, 2022. "Jump Dynamics and Leverage Effect: Evidences from Energy Exchange Traded Fund (ETFs)," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(6), pages 1-7.
    6. Jun Dong & Yaoyu Zhang & Yuanyuan Wang & Yao Liu, 2021. "A Two-Stage Optimal Dispatching Model for Micro Energy Grid Considering the Dual Goals of Economy and Environmental Protection under CVaR," Sustainability, MDPI, vol. 13(18), pages 1-28, September.
    7. Zhang, Yue-Jun & Bouri, Elie & Gupta, Rangan & Ma, Shu-Jiao, 2021. "Risk spillover between Bitcoin and conventional financial markets: An expectile-based approach," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    8. Wang, Delu & Tong, Xian & Wang, Yadong, 2020. "An early risk warning system for Outward Foreign Direct Investment in Mineral Resource-based enterprises using multi-classifiers fusion," Resources Policy, Elsevier, vol. 66(C).
    9. 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.
    10. Virbickaitė, Audronė & Ausín, M. Concepción & Galeano, Pedro, 2020. "Copula stochastic volatility in oil returns: Approximate Bayesian computation with volatility prediction," Energy Economics, Elsevier, vol. 92(C).
    11. Jose Arreola Hernandez & Sang Hoon Kang & Seong‐Min Yoon, 2022. "Nonlinear spillover and portfolio allocation characteristics of energy equity sectors: Evidence from the United States and Canada," Review of International Economics, Wiley Blackwell, vol. 30(1), pages 1-33, February.
    12. Tiwari, Aviral Kumar & Sharma, Gagan Deep & Rao, Amar & Hossain, Mohammad Razib & Dev, Dhairya, 2024. "Unraveling the crystal ball: Machine learning models for crude oil and natural gas volatility forecasting," Energy Economics, Elsevier, vol. 134(C).

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    More about this item

    Keywords

    Value-at-Risk; Conditional Value-at-Risk; Asymmetric Laplace distribution; Stochastic volatility model; Bayesian Markov Chain Monte Carlo; leverage effect;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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