Advanced Search
MyIDEAS: Login

CAViaR-based forecast for oil price risk

Contents:

Author Info

  • Huang, Dashan
  • Yu, Baimin
  • Fabozzi, Frank J.
  • Fukushima, Masao

Abstract

As a benchmark for measuring market risk, value-at-risk (VaR) reduces the risk associated with any kind of asset to just a number (amount in terms of a currency), which can be well understood by regulators, board members, and other interested parties. This paper employs a new VaR approach due to Engle and Manganelli [Engle, R.F., Manganelli, S., 2004. CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles. Journal of Business and Economic Statistics 22, 367-381] to forecasting oil price risk. In doing so, we provide two original contributions by introducing a new exponentially weighted moving average CAViaR model and developing a mixed data regression model for multi-period VaR prediction.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://www.sciencedirect.com/science/article/B6V7G-4V8VS73-1/2/2701f9a5adb9310ad894c922f580daf9
Download Restriction: Full text for ScienceDirect subscribers only

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Bibliographic Info

Article provided by Elsevier in its journal Energy Economics.

Volume (Year): 31 (2009)
Issue (Month): 4 (July)
Pages: 511-518

as in new window
Handle: RePEc:eee:eneeco:v:31:y:2009:i:4:p:511-518

Contact details of provider:
Web page: http://www.elsevier.com/locate/eneco

Related research

Keywords: VaR CAViaR Oil price risk Mixed data regression;

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. George Kouretas & Leonidas Zarangas, 2005. "Conditional autoregressive valu at risk by regression quantile: Estimatingmarket risk for major stock markets," Working Papers 0521, University of Crete, Department of Economics.
  2. Kolos, Sergey P. & Ronn, Ehud I., 2008. "Estimating the commodity market price of risk for energy prices," Energy Economics, Elsevier, vol. 30(2), pages 621-641, March.
  3. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
  4. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
  5. Engle, Robert F & Manganelli, Simone, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series qt06m3d6nv, Department of Economics, UC San Diego.
  6. Kuper, Gerard H., 2002. "Measuring oil price volatility," CCSO Working Papers 200208, University of Groningen, CCSO Centre for Economic Research.
  7. Engle, Robert F. & Manganelli, Simone, 2001. "Value at risk models in finance," Working Paper Series 0075, European Central Bank.
  8. Giot, Pierre & Laurent, Sebastien, 2003. "Market risk in commodity markets: a VaR approach," Energy Economics, Elsevier, vol. 25(5), pages 435-457, September.
  9. Sadorsky, Perry, 1999. "Oil price shocks and stock market activity," Energy Economics, Elsevier, vol. 21(5), pages 449-469, October.
  10. Komunjer, Ivana, 2002. "Quasi-Maximum Likelihood Estimation for Conditional Quantiles," Working Papers 1139, California Institute of Technology, Division of the Humanities and Social Sciences.
  11. Danielsson, Jon & Morimoto, Yuji, 2000. "Forecasting Extreme Financial Risk: A Critical Analysis of Practical Methods for the Japanese Market," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 18(2), pages 25-48, December.
  12. Gourieroux, C. & Jasiak, J., 2008. "Dynamic quantile models," Journal of Econometrics, Elsevier, vol. 147(1), pages 198-205, November.
  13. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464, October.
  14. David Cabedo, J. & Moya, Ismael, 2003. "Estimating oil price 'Value at Risk' using the historical simulation approach," Energy Economics, Elsevier, vol. 25(3), pages 239-253, May.
  15. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  16. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
  17. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
  18. Morana, Claudio, 2001. "A semiparametric approach to short-term oil price forecasting," Energy Economics, Elsevier, vol. 23(3), pages 325-338, May.
  19. Keith Kuester & Stefan Mittnik & Marc S. Paolella, 2006. "Value-at-Risk Prediction: A Comparison of Alternative Strategies," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(1), pages 53-89.
  20. Bertail, Patrice & Haefke, Christian & Politis, D.N.Dimitris N. & White, Halbert, 2004. "Subsampling the distribution of diverging statistics with applications to finance," Journal of Econometrics, Elsevier, vol. 120(2), pages 295-326, June.
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Nomikos, Nikos K. & Pouliasis, Panos K., 2011. "Forecasting petroleum futures markets volatility: The role of regimes and market conditions," Energy Economics, Elsevier, vol. 33(2), pages 321-337, March.
  2. Prat, Georges & Uctum, Remzi, 2011. "Modelling oil price expectations: Evidence from survey data," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(3), pages 236-247, June.
  3. Ghorbel, Ahmed & Trabelsi, Abdelwahed, 2014. "Energy portfolio risk management using time-varying extreme value copula methods," Economic Modelling, Elsevier, vol. 38(C), pages 470-485.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:31:y:2009:i:4:p:511-518. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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