A recursive robust Bayesian estimation in partially observed financial market

Jianhui Huang. "A recursive robust Bayesian estimation in partially observed financial market." Applicationes Mathematicae 34.2 (2007): 237-252. <http://eudml.org/doc/279294>.

@article{JianhuiHuang2007,
abstract = {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.},
author = {Jianhui Huang},
journal = {Applicationes Mathematicae},
keywords = {asset value; gauge transform; incomplete information; latent state; partial observation; portfolio allocation, stochastic volatility},
language = {eng},
number = {2},
pages = {237-252},
title = {A recursive robust Bayesian estimation in partially observed financial market},
url = {http://eudml.org/doc/279294},
volume = {34},
year = {2007},
}

TY - JOUR
AU - Jianhui Huang
TI - A recursive robust Bayesian estimation in partially observed financial market
JO - Applicationes Mathematicae
PY - 2007
VL - 34
IS - 2
SP - 237
EP - 252
AB - 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.
LA - eng
KW - asset value; gauge transform; incomplete information; latent state; partial observation; portfolio allocation, stochastic volatility
UR - http://eudml.org/doc/279294
ER -