Generalized Mixture of Nonlinear and Nonparametric ARARCH:
Theory and Application 
Joseph Tadjuidje Kamgaing
Department of Mathematics, University of Kaiserslautern
Monday, October 9, 2006
4:10 p.m.5:00 p.m.
203 Engineering
ABSTRACT
We first introduce some conditions implying the asymptotic stability of the process and define a version of the likelihood function that takes into account the hidden process. Further, based on the likelihood function we investigate the behavior of feedforward neural networks for estimating
the autoregressive and volatility functions and for identifying the changepoints between different phases.
Since the hidden process is not observable we construct a version of the Expectation Maximization (EM) algorithm that accounts for solving the problem numerically.
We illustrate our results with some applications. For example, we construct a trading strategy that we apply to real data and compare the performance with that of a classical Buy and Hold Strategy.
