A recursive robust Bayesian estimation in partially observed financial market

• Autorzy: Jianhui Huang
• Języki publikacji: EN

Abstrakty

EN

I propose a nonlinear Bayesian methodology to estimate the latent states which are partially observed in financial market. The distinguishable character of my methodology is that the recursive Bayesian estimation can be represented by some deterministic partial differential equation (PDE) (or evolution equation in the general case) parameterized by the underlying observation path. Unlike the traditional stochastic filtering equation, this dynamical representation is continuously dependent on the underlying observation path and thus it is robust to the modeling errors. Moreover, its advantages in financial econometrics are also discussed.

Kategorie tematyczne

• 62P05: Applications to actuarial sciences and financial mathematics
• 91B70: Stochastic models
• 60J60: Diffusion processes
• 62M05: Markov processes: estimation
• 60G35: Signal detection and filtering

• Wydawca: Institute of Mathematics Polish Academy of Sciences
• Czasopismo: Applicationes Mathematicae
• Rocznik: 2007
• Tom: 34
• Numer: 2
• Strony: 237-252

Daty

wydano

2007

Twórcy

autor

Jianhui Huang

• Department of Statistics, University of Alberta, Edmonton, AB, Canada T6G 2G1

Identyfikatory

DOI

10.4064/am34-2-8