Neural network models for conditional distribution under Bayesian analysis

被引：5

作者：

Miazhynskaia, Tatiana ^{[1
]}

Fruehwirth-Schnatter, Sylvia ^{[2
]}

Dorffner, Georg ^{[3
,4
]}

机构：

[1] Vienna Univ Technol, Inst Management Sci, A-1040 Vienna, Austria

[2] Johannes Kepler Univ Linz, Inst Appl Phys, A-4040 Linz, Austria

[3] Med Univ Vienna, Austrian Res Inst Artificial Intelligence, Vienna, Austria

[4] Med Univ Vienna, Dept Med Cybernet & Artificial Intelligence, Vienna, Austria

来源：

NEURAL COMPUTATION | 2008年 / 20卷 / 02期

关键词：

D O I：

10.1162/neco.2007.3182

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We use neural networks (NN) as a tool for a nonlinear autoregression to predict the second moment of the conditional density of return series. The NN models are compared to the popular econometric GARCH(1,1) model. We estimate the models in a Bayesian framework using Markov chain Monte Carlo posterior simulations. The interlinked aspects of the proposed Bayesian methodology are identification of NN hidden units and treatment of NN complexity based on model evidence. The empirical study includes the application of the designed strategy to market data, where we found a strong support for a nonlinear multilayer perceptron model with two hidden units.

引用

页码：504 / 522

页数：19

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