HMM based scenario generation for an investment optimisation problem

Christina Erlwein (), Gautam Mitra () and Diana Roman ()

Annals of Operations Research, 2012, vol. 193, issue 1, 173-192

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

Keywords: Scenario generation; Hidden Markov model; Geometric Brownian motion; Asset allocation; Optimal parameter estimation (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1007/s10479-011-0865-8

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