• DocumentCode
    3038208
  • Title

    The Compensation Model for Default-Risk of Corporate Bonds in China under Kalman Filter

  • Author

    Liang, Kaihao ; Lai, Kin Keung

  • Author_Institution
    Dept. of Math., Ji´´nan Univ., Guangzhou, China
  • fYear
    2009
  • fDate
    24-26 July 2009
  • Firstpage
    410
  • Lastpage
    413
  • Abstract
    The default compensation of corporate bonds is a significant part of risk management. In this research, algorithm of the Kalman filter is applied in modeling of jump-risk compensation. Default probability and default intensity are two important variables for the jump-risk compensation. In the modeling process, the parameter method of maximum likelihood estimation is used to obtain the default probability, and the default intensity under real measure is transformed into default intensity under equivalent martingale measure, which could be obtained from the differential equations under the equivalent martingale measure. The compensation model is established by solving the default probability function.
  • Keywords
    Kalman filters; difference equations; financial management; maximum likelihood estimation; pricing; probability; risk management; China corporate bond; Kalman filter; corporate finance; default probability function; default-risk management; differential equation; equivalent martingale measure; jump-risk compensation model; maximum likelihood estimation; parameter method; pricing model; Design methodology; Differential equations; Economic indicators; Mathematical model; Mathematics; Maximum likelihood estimation; Moment methods; Risk management; Stochastic processes; Yield estimation; Kalman filter; compensation; corporate bond; default probability; default risk;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Business Intelligence and Financial Engineering, 2009. BIFE '09. International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-0-7695-3705-4
  • Type

    conf

  • DOI
    10.1109/BIFE.2009.99
  • Filename
    5208857