Neural network models for conditional distribution under bayesian analysis.
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.
Institute of Management Science, Vienna University of Technology, Vienna, Austria. tmiazhyn@pop.tuwien.ac.at
Tatiana
T
Miazhynskaia
Sylvia
S
Frühwirth-Schnatter
Georg
G
Dorffner
D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D005376:Financial Management; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics