IDEAS home Printed from https://ideas.repec.org/a/imx/journl/v16y2021i4a4.html
   My bibliography  Save this article

Conditional Probability of Jumps in Oil Prices

Author

Listed:
  • Arturo Lorenzo-Valdés

    (Universidad Popular Autónoma del Estado de Puebla, México)

Abstract

Esta investigación modela el comportamiento de rendimientos del petróleo. La volatilidad de estos rendimientos se describe con un proceso TGARCH. La probabilidad de saltos condicionales se incorpora mediante distribuciones de intensidad de salto uniformes, doble exponencial y normal. Descubrimos que la volatilidad de estos rendimientos sigue los hechos estilizados de leptocurtosis, efecto de apalancamiento y agrupamiento de volatilidad. La información anormal que causa los saltos provoca otro tipo de cambios inesperados en el siguiente período y la intensidad de los saltos tiene un efecto negativo en la probabilidad de saltos en el siguiente período. El modelo dinámico propuesto puede extenderse a otros mercados y a modelos de series de tiempo multivariadas considerando la dependencia entre los rendimientos de los mercados. La principal contribución del trabajo es la estimación de la probabilidad condicional de saltos en función del comportamiento anterior que conduce a una mejor descripción de la dinámica estocástica de los precios del petróleo. Esto será útil para tomar mejores decisiones con respecto al petróleo como activo subyacente en derivados o en la formulación de mejores políticas públicas.

Suggested Citation

  • Arturo Lorenzo-Valdés, 2021. "Conditional Probability of Jumps in Oil Prices," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(4), pages 1-14, Octubre -.
    • Handle: RePEc:imx:journl:v:16:y:2021:i:4:a:4
    as

    Download full text from publisher

    File URL: https://www.remef.org.mx/index.php/remef/article/view/490
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Neil A. Wilmot and Charles F. Mason, 2013. "Jump Processes in the Market for Crude Oil," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    2. S. G. Kou, 2002. "A Jump-Diffusion Model for Option Pricing," Management Science, INFORMS, vol. 48(8), pages 1086-1101, August.
    3. Liwei Fan & Huiping Li, 2015. "Volatility analysis and forecasting models of crude oil prices: a review," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 38(1/2/3), pages 5-17.
    4. Eduardo Schwartz & James E. Smith, 2000. "Short-Term Variations and Long-Term Dynamics in Commodity Prices," Management Science, INFORMS, vol. 46(7), pages 893-911, July.
    5. Jing Li & Henry Thompson, 2010. "A Note on the Oil Price Trend and GARCH Shocks," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 159-166.
    6. Elie Bouri, 2019. "The Effect of Jumps in the Crude Oil Market on the Sovereign Risks of Major Oil Exporters," Risks, MDPI, vol. 7(4), pages 1-15, December.
    7. Schwartz, Eduardo S, 1997. "The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging," Journal of Finance, American Finance Association, vol. 52(3), pages 923-973, July.
    8. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    9. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    10. Postali, Fernando A.S. & Picchetti, Paulo, 2006. "Geometric Brownian Motion and structural breaks in oil prices: A quantitative analysis," Energy Economics, Elsevier, vol. 28(4), pages 506-522, July.
    11. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    12. Charles, Amélie & Darné, Olivier, 2017. "Forecasting crude-oil market volatility: Further evidence with jumps," Energy Economics, Elsevier, vol. 67(C), pages 508-519.
    13. Narayan, Paresh Kumar & Narayan, Seema, 2007. "Modelling oil price volatility," Energy Policy, Elsevier, vol. 35(12), pages 6549-6553, December.
    14. Merton, Robert C., 1976. "Option pricing when underlying stock returns are discontinuous," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 125-144.
    15. Lee, Yen-Hsien & Hu, Hsu-Ning & Chiou, Jer-Shiou, 2010. "Jump dynamics with structural breaks for crude oil prices," Energy Economics, Elsevier, vol. 32(2), pages 343-350, March.
    16. Askari, Hossein & Krichene, Noureddine, 2008. "Oil price dynamics (2002-2006)," Energy Economics, Elsevier, vol. 30(5), pages 2134-2153, September.
    17. Malcolm P. Baker & E. Scott Mayfield & John E. Parsons, 1998. "Alternative Models of Uncertain Commodity Prices for Use with Modern Asset Pricing Methods," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 115-148.
    18. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Neil A. Wilmot and Charles F. Mason, 2013. "Jump Processes in the Market for Crude Oil," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    2. Mason, Charles F. & Wilmot, Neil A., 2020. "Jumps in the convenience yield of crude oil," Resource and Energy Economics, Elsevier, vol. 60(C).
    3. Mason, Charles F. & A. Wilmot, Neil, 2014. "Jump processes in natural gas markets," Energy Economics, Elsevier, vol. 46(S1), pages 69-79.
    4. Bei, Shuhua & Yang, Aijun & Pei, Haotian & Si, Xiaoli, 2023. "Price Risk Analysis using GARCH Family Models: Evidence from Shanghai Crude Oil Futures Market," Economic Modelling, Elsevier, vol. 125(C).
    5. Nomikos, Nikos & Andriosopoulos, Kostas, 2012. "Modelling energy spot prices: Empirical evidence from NYMEX," Energy Economics, Elsevier, vol. 34(4), pages 1153-1169.
    6. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    7. Bing-Huei Lin & Mao-Wei Hung & Jr-Yan Wang & Ping-Da Wu, 2013. "A lattice model for option pricing under GARCH-jump processes," Review of Derivatives Research, Springer, vol. 16(3), pages 295-329, October.
    8. Mohamed CHIKHI & Claude DIEBOLT & Tapas MISHRA, 2019. "Memory that Drives! New Insights into Forecasting Performance of Stock Prices from SEMIFARMA-AEGAS Model," Working Papers 07-19, Association Française de Cliométrie (AFC).
    9. Gronwald, Marc, 2012. "A characterization of oil price behavior — Evidence from jump models," Energy Economics, Elsevier, vol. 34(5), pages 1310-1317.
    10. Qian, Ya & Tu, Jun & Härdle, Wolfgang Karl, 2019. "Information Arrival, News Sentiment, Volatilities and Jumps of Intraday Returns," IRTG 1792 Discussion Papers 2019-002, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    11. Arouri, Mohamed El Hédi & Lahiani, Amine & Lévy, Aldo & Nguyen, Duc Khuong, 2012. "Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models," Energy Economics, Elsevier, vol. 34(1), pages 283-293.
    12. Chevallier, Julien & Ielpo, Florian, 2017. "Investigating the leverage effect in commodity markets with a recursive estimation approach," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 763-778.
    13. Wang, Yudong & Wu, Chongfeng & Yang, Li, 2016. "Forecasting crude oil market volatility: A Markov switching multifractal volatility approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 1-9.
    14. Neil A. Wilmot, 2019. "Heavy Metals: Might as Well Jump," IJFS, MDPI, vol. 7(2), pages 1-14, June.
    15. Mohamed CHIKHI & Claude DIEBOLT & Tapas MISHRA, 2019. "Does Predictive Ability of an Asset Price Rest in 'Memory'? Insights from a New Approach," Working Papers of BETA 2019-43, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    16. Cao, Wenbin & Guernsey, Scott B. & Linn, Scott C., 2018. "Evidence of infinite and finite jump processes in commodity futures prices: Crude oil and natural gas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 629-641.
    17. Afees A. Salisu & Ismail O. Fasanya, 2012. "Comparative Performance of Volatility Models for Oil Price," International Journal of Energy Economics and Policy, Econjournals, vol. 2(3), pages 167-183.
    18. Samet Gunay & Audil Rashid Khaki, 2018. "Best Fitting Fat Tail Distribution for the Volatilities of Energy Futures: Gev, Gat and Stable Distributions in GARCH and APARCH Models," JRFM, MDPI, vol. 11(2), pages 1-19, June.
    19. Mohamed Chikhi & Claude Diebolt & Tapas Mishra, 2019. "Measuring Success: Does Predictive Ability of an Asset Price Rest in 'Memory'? Insights from a New Approach," Working Papers 11-19, Association Française de Cliométrie (AFC).
    20. Bernard, Jean-Thomas & Khalaf, Lynda & Kichian, Maral & McMahon, Sébastien, 2008. "Oil Prices: Heavy Tails, Mean Reversion and the Convenience Yield," Cahiers de recherche 0801, GREEN.

    More about this item

    Keywords

    Precios del petróleo; TGARCH con saltos; Probabilidad condicional;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:imx:journl:v:16:y:2021:i:4:a:4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ricardo Mendoza (email available below). General contact details of provider: https://www.remef.org.mx/index.php/remef/index .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.