Quotation Miazhynskaia, Tatiana, Frühwirth-Schnatter, Sylvia, Dorffner, Georg. 2008. Neural network models for conditional distribution under Bayesian analysis. Neural Computation 20 (2): 504-522.


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Abstract

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.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Neural Computation
Citation Index SCI
Language English
Title Neural network models for conditional distribution under Bayesian analysis
Volume 20
Number 2
Year 2008
Page from 504
Page to 522
URL http://www.mitpressjournals.org/doi/abs/10.1162/neco.2007.3182

Associations

People
Frühwirth-Schnatter, Sylvia (Details)
External
Dorffner, Georg
Miazhynskaia, Tatiana
Organization
Institute for Statistics and Mathematics IN (Details)
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