Joseph Tadjuidje Kamgaing
We consider time series switching between different dynamics or phases, e.g. a
generalized mixture of first order nonlinear AR-ARCH models with two dynamics
Since the process is not observable, we design a version of the Expectation Maximization algorithm that account for solving the problem numerically. In fact, this algorithm consists of assuming in the Expectation step that the parameters of the networks functions are known and to estimate the . Considering now the the parameters of the networks functions are derived in theMaximization step. Both steps are iterated until a stopping criterion is satisfied.
Based on these estimations, we construct a trading strategy that we
apply on real life data and compare the results with those of the
classical Buy and Hold strategy.