In this thesis the problem of model uncertainty is under scrutiny along
with its implications in attaining optimal forecastability. To account for
that averaging techniques are adopted including Bayesian model averaging,
Bayesian Approximation and Thick Modelling. After an introductory
chapter and a second one where some of the most celebrated conditional-volatility
modelling proposals are discussed the third chapter investigates
volatility forecasting and its direct association to option pricing. Some novel
approaches to perform averaging are suggested here including variations of
the predetermined methods together with more sophisticated algorithmic
propositions such as Neural Networks. The fourth chapter extends the focal
point of averaging to the whole predictive volatility density as this can
be inferred first from derivatives on the underlying volatility index and
second directly from the asset class under consideration (here the equity
index) using bootstrap based - GARCH type models. The fifth chapter introduces
some widely used variable selection techniques to the Finance continuum
while averaging schemes once more are used in order to avoid model
misspeci cation risk. Extensions to a nonlinear regression framework are
also suggested while investment strategies are implemented in all chapters
substantiating the ultimate supremacy of averaging schemes against single
model alternatives. The last chapter concludes the research and makes some
future suggestions for additional investigation.