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A semi-parametric marginalized dynamic conditional correlation framework

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  • Giuseppe Storti
  • Chao Wang

Abstract

We develop a novel multivariate semi-parametric framework for joint portfolio Value-at-Risk and Expected Shortfall forecasting. Unlike existing univariate semi-parametric approaches, the proposed framework explicitly models the dependence structure among portfolio asset returns through a marginalized dynamic conditional correlation (DCC) parameterization. To estimate the model, a two-step procedure based on the minimization of a strictly consistent scoring function derived from the Asymmetric Laplace distribution is developed. This procedure allows to simultaneously estimate the marginalized DCC parameters and the portfolio risk factors. The performance of the proposed model in risk forecasting and portfolio allocation is evaluated 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 effectiveness of the proposed framework compared to a variety of existing approaches.

Suggested Citation

  • Giuseppe Storti & Chao Wang, 2022. "A semi-parametric marginalized dynamic conditional correlation framework," Papers 2207.04595, arXiv.org, revised Jul 2024.
    • Handle: RePEc:arx:papers:2207.04595
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    References listed on IDEAS

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