Generalized Mixture of Nonlinear and Nonparametric AR-ARCH:
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 feed-forward neural networks for estimating
the autoregressive and volatility functions and for identifying the change-points 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.



 

 


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