The paper proposes the use a Markov chain to model and predict the distributional behaviour of a portfolio of returns. In particular, it describes an algorithm to compute the distribution of returns that follow a markovian tree. This approach reduces the computational complexity as compared to the classic markovian approach, since the tree recombines at each temporal step. Furthermore, the paper compares ex-post the assumption that returns follow either a geometric Brownian motion or a Markov chain. Finally, it discusses some possible financial applications of the proposed approach.

Distributional Approximation of Asset Returns with Nonparametric Markovian Trees

IAQUINTA, Gaetano;ORTOBELLI LOZZA, Sergio
2006-01-01

Abstract

The paper proposes the use a Markov chain to model and predict the distributional behaviour of a portfolio of returns. In particular, it describes an algorithm to compute the distribution of returns that follow a markovian tree. This approach reduces the computational complexity as compared to the classic markovian approach, since the tree recombines at each temporal step. Furthermore, the paper compares ex-post the assumption that returns follow either a geometric Brownian motion or a Markov chain. Finally, it discusses some possible financial applications of the proposed approach.
journal article - articolo
2006
Iaquinta, Gaetano; ORTOBELLI LOZZA, Sergio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/19484
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