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A multivariate semi-parametric portfolio risk optimization and forecasting framework

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  • Giuseppe Storti
  • Chao Wang

Abstract

We develop a novel multivariate semi-parametric modelling approach to portfolio Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting. Differently from existing univariate semi-parametric approaches, the proposed framework involves explicit modelling of the dependence structure among portfolio asset returns through a dynamic conditional correlation (DCC) parameterization. Model parameters are estimated through a two-step procedure based on the minimization of strictly consistent scoring functions derived from the Asymmetric Laplace (AL) distribution. The proposed model is consequently denominated as DCC-AL. The performance of the DCC-AL in risk forecasting and portfolio optimization is assessed by means of a forecasting study on the components of the Dow Jones index for an out-of-sample period from December 2016 to September 2021. The empirical results support the proposed model class, comparing to a variety of existing approaches, in terms of risk forecasting and portfolio optimization.

Suggested Citation

  • Giuseppe Storti & Chao Wang, 2022. "A multivariate semi-parametric portfolio risk optimization and forecasting framework," Papers 2207.04595, arXiv.org, revised Feb 2023.
    • Handle: RePEc:arx:papers:2207.04595
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    References listed on IDEAS

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