Skip to main content
SpringerLink
Log in

Estimating Oil Price Value at Risk Using Belief Functions

  • Chapter
  • First Online:
Econometrics of Risk

Part of the book series: Studies in Computational Intelligence ((SCI,volume 583))

Abstract

We consider extreme value theory to study extreme price movements in crude oil market. Autoregressive-Moving-Average models are developed to describe daily log return of crude oil price. Peak-over-threshold models are then used to model the log return forecasting errors (residuals). The maximum residuals are expressed in terms of value-at-risk or return level corresponding to accepted levels of risk so that appropriate risk measures can be taken. A likelihood-based belief function is constructed to quantify estimation uncertainty. As a result, we can assess the plausibility of various assertions about the value-at-risk of the idiosyncratic shocks in the world crude oil market.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Balkema, A.A., De Haan, L.: Residual life time at great age. Ann. Probab. 2, 792–804 (1974)

    Article  MATH  Google Scholar 

  2. Ben Abdallah, N., Mouhous-Voyneau, N., Denoeux, T.: Combining statistical and expert evidence using belief functions: application to centennial sea level estimation taking into account climate change. Int. J. Approx. Reason. 55(1), 341–354 (2014)

    Article  MathSciNet  Google Scholar 

  3. Birnbaum, A.: On the foundations of statistical inference. J. Am. Stat. Assoc. 57(298), 269–306 (1962)

    Article  MATH  MathSciNet  Google Scholar 

  4. Brooks, C.: Introductory Econometrics for Finance. Cambridge University Press, Cambridge (2014)

    Google Scholar 

  5. Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38(2), 325–339 (1967)

    Article  MATH  MathSciNet  Google Scholar 

  6. Dempster, A.P.: A generalization of Bayesian inference. J. R. Stat. Soc. Ser. B (Methodol.) 205–247 (1968)

    Google Scholar 

  7. Denoe, T.: Likelihood-based belief function: justification and some extensions to low-quality data. Int. J. Approx. Reason. 55(7), 1535–1547 (2014)

    Article  Google Scholar 

  8. Edwards, A.W.F.: Likelihood. Johns Hopkins University Press, Baltimore (1992) (expanded edition)

    Google Scholar 

  9. Embrechts, P., Klüppelberg, C., Mikosch, T.: Modelling Extremal Events: for Insurance and Finance. Springer, Berlin (1997)

    Book  MATH  Google Scholar 

  10. Embrechts, P., Frey, R., McNeil, A.: Quantitative Risk Management, vol. 10. Princeton Series in Finance, Princeton (2005)

    Google Scholar 

  11. Enders, W.: Applied Econometric Time Series. Wiley, NewYork (2008)

    Google Scholar 

  12. Gilli, M.: An application of extreme value theory for measuring financial risk. Comput. Econ. 27(2–3), 207–228 (2006)

    Article  MATH  Google Scholar 

  13. Kanjanatarakul, O., Sriboonchitta, S., Denoeux, T.: Forecasting using belief functions: an application to marketing econometrics. Int. J. Approx. Reason. 55(5), 1113–1128 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  14. Krehbiel, T., Adkins, L.C.: Price risk in the nynex energy complex: an extreme value approach. J. Future Mark. 25, 309–337 (2005)

    Article  Google Scholar 

  15. Marimoutou, V., Raggad, B., Trabelsi, A.: Extreme value theory and value at risk: application to oil market. Energy Econ. 31(4), 519–530 (2009)

    Article  Google Scholar 

  16. McNeil, A.J.: Extreme value theory for risk managers. Working paper, ETH Zurich (1999)

    Google Scholar 

  17. Pickands, J.: Statistical inference using extreme order statistics. Ann. Stat. 3, 119–131 (1975)

    Article  MATH  MathSciNet  Google Scholar 

  18. Reiss, R.D., Thomas, M.: Statistical Analysis of Extreme Values with Applications to Insurance Finance. Hydrology and Other Fields. Birkhauser (1997)

    Google Scholar 

  19. Ren, F., Giles, D.E.: Extreme value analysis of daily Canadian crude oil prices. Appl. Finan. Econ. 20, 941–954 (2010)

    Article  Google Scholar 

  20. Shafer, G.: A Mathematical Theory of Evidence, vol. 1. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  21. Shafer, G.: Allocations of probability. Ann. Probab. 7, 827–839 (1979)

    Article  MATH  MathSciNet  Google Scholar 

  22. Tsay, R.S.: Analysis of Financial Time Series, vol. 543. Wiley, New York (2005)

    Book  MATH  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Prof. T. Denœux, Prof. Dr. Hung T. Nguyen and Supanika Leurcharusmee for helpful comments and suggestions.

Author information

Authors and Affiliations

  1. Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand

    Panisara Phochanachan, Jirakom Sirisrisakulchai & Songsak Sriboonchitta

Authors
  1. Panisara Phochanachan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jirakom Sirisrisakulchai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Songsak Sriboonchitta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Panisara Phochanachan .

Editor information

Editors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Van-Nam Huynh

  2. Department of Computer Science, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  3. Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand

    Songsak Sriboonchitta

  4. Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand

    Komsan Suriya

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Phochanachan, P., Sirisrisakulchai, J., Sriboonchitta, S. (2015). Estimating Oil Price Value at Risk Using Belief Functions. In: Huynh, VN., Kreinovich, V., Sriboonchitta, S., Suriya, K. (eds) Econometrics of Risk. Studies in Computational Intelligence, vol 583. Springer, Cham. https://doi.org/10.1007/978-3-319-13449-9_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-13449-9_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13448-2

  • Online ISBN: 978-3-319-13449-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation