Abstract:
In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast financial time-series. The main distinction of complex or high-order Markov Chains and simple first-ord yer ones is the existing of aftereffect or memory. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the «generalized state» as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the statistic properties of price returns.
Description:
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