Modelling financial time series using nonlinear and nonparametric bayesian methods
Abstract
The scope of the thesis is to provide an original contribution in modeling financial time series (i.e financial returns). We propose/extend univariate and multivariate models in order to better explain the financial returns and capture the well known stylized facts which characterize them. In particular we propose a Threshold Normal Mixture Garch model, in order to model the fat tails and estimate more efficiently the tail risk. We develop a Threshold Regression Model to examine/identify the nonlinear risk exposures in Hedge fund strategies and to test the existence of market timing abilities. Moreover we propose a new Multivariate Garch Model which allows the different series (i.e Hedge funds) to be affected by different predictors (risk factors). For all these models we consider the problem of estimation, model selection and prediction by using Markov Chain Monte Carlo methods and Bayesian techniques.Moreover we use MCMC methods in the context of Bayesian nonparametric inference in o ...
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