Multilayer feedforward networks are universal approximators
Neural Networks
A practical Bayesian framework for backpropagation networks
Neural Computation
Issues in Bayesian analysis of neural network models
Neural Computation
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Evaluating volatility forecasts in option pricing in the context of a simulated options market
Computational Statistics & Data Analysis
An option pricing formula for the GARCH diffusion model
Computational Statistics & Data Analysis
Bootstrap prediction for returns and volatilities in GARCH models
Computational Statistics & Data Analysis
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Neural networks provide a tool for describing non-linearity in volatility processes of financial data and help to answer the question ''how much'' non-linearity is present in the data. Non-linearity is studied under three different specifications of the conditional distribution: Gaussian, Student-t and mixture of Gaussians. To rank the volatility models, a Bayesian framework is adopted to perform a Bayesian model selection within the different classes of models. In the empirical analysis, the return series of the Dow Jones Industrial Average index, FTSE 100 and NIKKEI 225 indices over a period of 16 years are studied. The results show different behavior across the three markets. In general, if a statistical model accounts for non-normality and explains most of the fat tails in the conditional distribution, then there is less need for complex non-linear specifications.