A multivariate semi-parametric portfolio risk optimization and forecasting framework

Giuseppe Storti and Chao Wang

MPRA Paper from University Library of Munich, Germany

Abstract: A new multivariate semi-parametric risk forecasting framework is proposed, to enable the portfolio Value-at-Risk (VaR) and Expected Shortfall (ES) optimization and forecasting. The proposed framework accounts for the dependence structure among asset returns, without assuming their distribution. A simulation study is conducted to evaluate the finite sample properties of the employed estimator for the proposed model. An empirically motivated portfolio optimization method, that can be utilized to optimize the portfolio VaR and ES, is developed. A forecasting study on 2.5% level evaluates the performance of the model in risk forecasting and portfolio optimization, based on the components of the Dow Jones index for the out-of-sample period from December 2016 to September 2021. Comparing to the standard models in the literature, the empirical results are favorable for the proposed model class, in particular the effectiveness of the proposed framework in portfolio risk optimization is demonstrated.

Keywords: semi-parametric; Value-at-Risk; Expected Shortfall; multivariate; portfolio optimization. (search for similar items in EconPapers)
JEL-codes: C14 C32 C51 C58 G17 (search for similar items in EconPapers)
Date: 2022-08
New Economics Papers: this item is included in nep-ecm and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:115266

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