IDEAS home Printed from https://ideas.repec.org/a/bla/opecrv/v30y2006i3p151-169.html
   My bibliography  Save this article

Forecasting volatility for options valuation

Author

Listed:
  • Mahdjouba Belaifa
  • Kimio Morimune

Abstract

The petroleum sector plays a neuralgic role in the basement of world economies, and market actors (producers, intermediates, as well as consumers) are continuously subjected to the dynamics of unstable oil market. Huge amounts are being invested along the production chain to make one barrel of crude oil available to the end user. Adding to that are the effect of geopolitical dynamics as well as geological risks as expressed in terms of low chances of successful discoveries. In addition, fiscal regimes and regulations, technology and environmental concerns are also among some of the major factors that contribute to the substantial risk in the oil industry and render the market structure vulnerable to crises. The management of these vulnerabilities require modern tools to reduce risk to a certain level, which unfortunately is a non‐zero value. The aim of this paper is, therefore, to provide a modern technique to capture the oil price stochastic volatility that can be implemented to value the exposure of an investor, a company, a corporate or a Government. The paper first analyses the regional dependence on oil prices, through a historical perspective and then looks at the evolution of pricing environment since the large price jumps of the 1970s. The main causes of oil prices volatility are treated in the third part of the paper. The rest of the article deals with volatility models and forecasts used in risk management, with an implication for pricing derivatives.

Suggested Citation

  • Mahdjouba Belaifa & Kimio Morimune, 2006. "Forecasting volatility for options valuation," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 30(3), pages 151-169, September.
  • Handle: RePEc:bla:opecrv:v:30:y:2006:i:3:p:151-169
    DOI: 10.1111/j.1468-0076.2006.00166.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1468-0076.2006.00166.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1468-0076.2006.00166.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    2. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
    3. 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. Ataurima Arellano, Miguel & Rodríguez, Gabriel, 2020. "Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    2. Dinghai Xu & Tony S. Wirjanto, 2008. "An Empirical Characteristic Function Approach to VaR under a Mixture of Normal Distribution with Time-Varying Volatility," Working Papers 08008, University of Waterloo, Department of Economics.
    3. Sang Hoon Kang & Seong-Min Yoon, 2010. "Sudden Changes and Persistence in Volatility of Korean Equity Sector Returns," Korean Economic Review, Korean Economic Association, vol. 26, pages 431-451.
    4. repec:zbw:rwirep:0243 is not listed on IDEAS
    5. Alistair Mees & Berndt Pilgram, 2000. "Non-Linear Markov Modelling Using Canonical Variate Analysis: Forecasting Exchange Rate Volatility," Econometric Society World Congress 2000 Contributed Papers 1162, Econometric Society.
    6. Kuang‐Liang Chang & Chi‐Wei He, 2010. "Does The Magnitude Of The Effect Of Inflation Uncertainty On Output Growth Depend On The Level Of Inflation?," Manchester School, University of Manchester, vol. 78(2), pages 126-148, March.
    7. He, Xue-Zhong & Li, Kai & Santi, Caterina & Shi, Lei, 2022. "Social interaction, volatility clustering, and momentum," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 125-149.
    8. Pelletier, Denis, 2006. "Regime switching for dynamic correlations," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 445-473.
    9. Tuysuz, Sukriye, 2007. "The asymmetric impact of macroeconomic announcements on U.S. Government bond rate level and volatility," MPRA Paper 5381, University Library of Munich, Germany.
    10. Dinghai Xu, 2021. "A study on volatility spurious almost integration effect: A threshold realized GARCH approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4104-4126, July.
    11. Charfeddine, Lanouar & Ajmi, Ahdi Noomen, 2013. "The Tunisian stock market index volatility: Long memory vs. switching regime," Emerging Markets Review, Elsevier, vol. 16(C), pages 170-182.
    12. Issler, João Victor, 1999. "Estimating and forecasting the volatility of Brazilian finance series using arch models (Preliminary Version)," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 347, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    13. Mihaela Craioveanu & Eric Hillebrand, 2012. "Level changes in volatility models," Annals of Finance, Springer, vol. 8(2), pages 277-308, May.
    14. Christiansen, Charlotte, 2008. "Level-ARCH short rate models with regime switching: Bivariate modeling of US and European short rates," International Review of Financial Analysis, Elsevier, vol. 17(5), pages 925-948, December.
    15. Fong, Wai Mun & See, Kim Hock, 2002. "A Markov switching model of the conditional volatility of crude oil futures prices," Energy Economics, Elsevier, vol. 24(1), pages 71-95, January.
    16. A. B. M. Rabiul Alam Beg & Sajid Anwar, 2014. "Detecting volatility persistence in GARCH models in the presence of the leverage effect," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2205-2213, December.
    17. Maurício Yoshinori Une & Marcelo Savino Portugal, 2005. "Fear of disruption: a model of Markov-switching regimes for the Brazilian country risk conditional volatility," Econometrics 0509005, University Library of Munich, Germany.
    18. Halkos, George E. & Tsirivis, Apostolos S., 2019. "Effective energy commodity risk management: Econometric modeling of price volatility," Economic Analysis and Policy, Elsevier, vol. 63(C), pages 234-250.
    19. Aurea Grané & Helena Veiga, 2012. "Asymmetry, realised volatility and stock return risk estimates," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 11(2), pages 147-164, August.
    20. Pedro Nielsen Rotta & Pedro L. Valls Pereira, 2016. "Analysis of contagion from the dynamic conditional correlation model with Markov Regime switching," Applied Economics, Taylor & Francis Journals, vol. 48(25), pages 2367-2382, May.
    21. Kiyotaka Satoyoshi & Hidetoshi Mitsui, 2011. "Empirical Study of Nikkei 225 Options with the Markov Switching GARCH Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 18(1), pages 55-68, March.

    More about this item

    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:bla:opecrv:v:30:y:2006:i:3:p:151-169. 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: Wiley Content Delivery (email available below). General contact details of provider: http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291753-0237 .

    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.