Bayesian
Disorder Detection Models
Caio Natividade (Deutsche Bank)
25/03/11
Our method is based on the Bayesian
disorder detection framework. The
theory is to identify a likelihood function that detects switches in
Brownian motions from one with upward drift to one with a downward
drift or one with no drift. The Brownian motions are parameterised by
first and second moments defined through in-sample optimisation.
The detection mechanism doesn't act
alone, it is helped by a peak-trough identification algorithm,
highlighted in a paper by Rodionov.
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[Last modified: Mar. 15th 2011 by Kostas
Kalogeropoulos]