Computational Economics

, Volume 36, Issue 4, pp 283–307 | Cite as

The Role of Additional Information in Option Pricing: Estimation Issues for the State Space Model



We consider two competing financial state space models and investigate whether additional information in the form of option price data is helpful to the estimation of either the unobservable state variable (volatility) or the unknown parameters in the model. The complete discussion of the estimation problem in the presence of additional information involves decisions about filtering methods, the quality of the new information, the correlation between state variables and out-of-sample forecast performance. It is found that the state variable estimation is more sensitive than the parameter estimation to the correlation, information quality and the assumed linearity or non-linearity of the underlying model. As a result of the investigation of these factors, the particle filter is shown to be an attractive method for computing posterior distributions for these models.


Kalman filter Particle filter Stochastic volatility model Volatility forecasting 


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Copyright information

© Springer Science+Business Media, LLC. 2010

Authors and Affiliations

  • Ren-Her Wang
    • 1
  • John A. D. Aston
    • 2
    • 3
  • Cheng-Der Fuh
    • 3
    • 4
  1. 1.Department of Banking and FinanceTamkang UniversityTamsuiTaiwan, ROC
  2. 2.Department of StatisticsUniversity of WarwickCoventryUK
  3. 3.Institute of Statistical ScienceAcademia SinicaTaipeiTaiwan, ROC
  4. 4.Graduate Institute of StatisticsNational Central UniversityJhong-LiTaiwan, ROC

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