Geodesic
A recursive robust Bayesian estimation in partially observed financial market
Jianhui Huang1
1 Department of Statistics University of Alberta Edmonton, AB, Canada T6G 2G1
Applicationes Mathematicae, Tome 34 (2007) no. 2, pp. 237-252 Cet article a éte moissonné depuis la source Institute of Mathematics Polish Academy of Sciences

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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.
DOI : 10.4064/am34-2-8
Keywords: propose nonlinear bayesian methodology estimate latent states which partially observed financial market distinguishable character methodology recursive bayesian estimation represented deterministic partial differential equation pde evolution equation general parameterized underlying observation path unlike traditional stochastic filtering equation dynamical representation continuously dependent underlying observation path robust modeling errors moreover its advantages financial econometrics discussed

Jianhui Huang 1

1 Department of Statistics University of Alberta Edmonton, AB, Canada T6G 2G1 
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Jianhui Huang. A recursive robust Bayesian estimation
 in partially observed financial market. Applicationes Mathematicae, Tome 34 (2007) no. 2, pp. 237-252. doi: 10.4064/am34-2-8

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