Journal of Risk

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A regime-switching factor model for mean–variance optimization

Giorgio Costa and Roy H. Kwon

  • We formulate a novel Markov regime-switching factor model to describe the cyclical nature of asset returns in modern financial markets.
  • Maintaining a linear factor model structure allows us to derive a set of probability-weighted regime-dependent parameters, namely the asset expected returns and their corresponding covariance matrix, that implicitly incorporate the properties of the different market regimes.
  • These parameters can be used as inputs during Mean-Variance Optimization to construct portfolios that are better aligned with the current market regime.

We formulate a novel Markov regime-switching factor model to describe the cyclical nature of asset returns in modern financial markets. Maintaining a factor model structure allows us to easily derive the asset expected returns and their corresponding covariance matrix. By design, these two parameters are calibrated to better describe the properties of the different market regimes. In turn, these regime-dependent parameters serve as the inputs during mean–variance optimization, thereby constructing portfolios adapted to the current market environment. Through this formulation, the proposed model allows for the construction of large, realistic portfolios at no additional computational cost during optimization. Moreover, the viability of this model can be significantly improved by periodically rebalancing the portfolio, ensuring proper alignment between the estimated parameters and the transient market regimes. An out-of-sample computational experiment over a long investment horizon shows that the proposed regime-dependent portfolios are better aligned with the market environment, yielding a higher ex post rate of return and lower volatility than competing portfolios.

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