A Higher-Order Hidden Markov Chain-Modulated Model for Asset Allocation

  • Xiaojing Xi
  • Rogemar Mamon
  • Matt Davison


This paper presents an analysis of asset allocation strategies when the asset returns are governed by a discrete-time higher-order hidden Markov model (HOHMM), also called the weak hidden Markov model. We assume the drifts and volatilities of the asset returns switch over time according to the state of the HOHMM, in which the probability of the current state depends on the information from previous time-steps. The “switching” and “mixed” strategies are studied. We use a multivariate filtering technique in conjunction with the EM algorithm to obtain estimates of model parameter at a given time. This, in turn, aids investors in determining the optimal investment strategy for the next time step. Numerical implementation is applied to data on Russell 3000 value and growth indices. We benchmark the respective performances of portfolio using three classical investment measures.


Markov chain Regime-switching Filtering Investment strategy Portfolio performance 


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© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Department of Applied MathematicsUniversity of Western OntarioLondonCanada
  2. 2.Department of Statistical and Actuarial SciencesUniversity of Western OntarioLondonCanada
  3. 3.Richard Ivey School of BusinessUniversity of Western OntarioLondonCanada

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