• DocumentCode
    2076547
  • Title

    Valuing the Overseas Mergers and Acquisitions Price Risk of Chinese Oil Companies: Based on VaR Modeling

  • Author

    Zhang, Yixiang ; Cheng, Jinhua

  • Author_Institution
    Sch. of Econ. & Manage., Wuhan Univ. of Sci. & Eng., Wuhan, China
  • fYear
    2009
  • fDate
    20-22 Sept. 2009
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    This papers analysis the market risk Chinese oil companies facing in overseas mergers and acquisitions (M & A) by using historical simulation ARMA forecasting (HSAF) as the basic analysis method and the spot price of WTI crude oil as the basic analysis variables. It makes the conclusions that the VaR predictive value is much greater than the actual value under the confidence level of 97.6 percent; and, at the most time, the predictive value is 1-2 times than the actual value. This shows that China´s oil companies are facing with a great deal of risk when carrying out overseas mergers and acquisitions. Finally, discuss the avoidance way from strengthening market risk assessment in host country, reducing the interest rate and exchange rate risks, determining the ways mergers and acquisitions will take carefully, establishing overseas strategic alliances, entering the region on strategic choice.
  • Keywords
    autoregressive moving average processes; economic indicators; petroleum industry; value engineering; Chinese oil companies; VaR modeling; VaR predictive value; WTI crude oil; acquisitions price risk; exchange rate risk; historical simulation ARMA forecasting; interest rate risk; market risk; overseas mergers; Analytical models; Corporate acquisitions; Economic forecasting; Economic indicators; Exchange rates; Petroleum; Predictive models; Reactive power; Risk analysis; Risk management;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Management and Service Science, 2009. MASS '09. International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-4638-4
  • Electronic_ISBN
    978-1-4244-4639-1
  • Type

    conf

  • DOI
    10.1109/ICMSS.2009.5301166
  • Filename
    5301166