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HMM based scenario generation for an investment optimisation problem

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  • Christina Erlwein
  • Gautam Mitra
  • Diana Roman

Abstract

The Geometric Brownian motion (GBM) is a standard method for modelling financial time series. An important criticism of this method is that the parameters of the GBM are assumed to be constants; due to this fact, important features of the time series, like extreme behaviour or volatility clustering cannot be captured. We propose an approach by which the parameters of the GBM are able to switch between regimes, more precisely they are governed by a hidden Markov chain. Thus, we model the financial time series via a hidden Markov model (HMM) with a GBM in each state. Using this approach, we generate scenarios for a financial portfolio optimisation problem in which the portfolio CVaR is minimised. Numerical results are presented. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Christina Erlwein & Gautam Mitra & Diana Roman, 2012. "HMM based scenario generation for an investment optimisation problem," Annals of Operations Research, Springer, vol. 193(1), pages 173-192, March.
    • Handle: RePEc:spr:annopr:v:193:y:2012:i:1:p:173-192:10.1007/s10479-011-0865-8
    DOI: 10.1007/s10479-011-0865-8
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    References listed on IDEAS

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    Cited by:

    1. Davari-Ardakani, Hamed & Aminnayeri, Majid & Seifi, Abbas, 2014. "A study on modeling the dynamics of statistically dependent returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 35-51.
    2. Ponomareva, K. & Roman, D. & Date, P., 2015. "An algorithm for moment-matching scenario generation with application to financial portfolio optimisation," European Journal of Operational Research, Elsevier, vol. 240(3), pages 678-687.

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