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

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Journal of Mathematical Modelling and Algorithms in Operations Research

Abstract

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.

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Authors and Affiliations

  1. Department of Applied Mathematics, University of Western Ontario, London, ON, Canada

    Xiaojing Xi, Rogemar Mamon & Matt Davison

  2. Department of Statistical and Actuarial Sciences, University of Western Ontario, 1151 Richmond Street, London, ON, Canada, N6A 5B7

    Rogemar Mamon & Matt Davison

  3. Richard Ivey School of Business, University of Western Ontario, London, ON, Canada

    Matt Davison

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  1. Xiaojing Xi
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  2. Rogemar Mamon
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  3. Matt Davison
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Correspondence to Rogemar Mamon.

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Xi, X., Mamon, R. & Davison, M. A Higher-Order Hidden Markov Chain-Modulated Model for Asset Allocation. J Math Model Algor 13, 59–85 (2014). https://doi.org/10.1007/s10852-012-9214-4

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  • DOI: https://doi.org/10.1007/s10852-012-9214-4

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