Comparison of Score-Driven Equity-Gold Portfolios During the COVID-19 Pandemic Using Model Confidence Sets

Astrid Ayala, Szabolcs Blazsek and Licht Adrian
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Licht Adrian: School of Business, Universidad Francisco Marroquín, Guatemala City, Guatemala

Studies in Nonlinear Dynamics & Econometrics, 2023, vol. 27, issue 5, 705-731

Abstract: Gold may have a hedge, safe haven, or diversifier property when added to stock portfolios. Motivated by the favorable statistical properties and out-of-sample performance of score-driven models, we investigate for equity-gold portfolios whether score-driven mean, volatility, and copula models can improve the performances of DCC (dynamic conditional correlation) portfolios, the naïve portfolio strategy, and the Standard & Poor’s 500 (S&P 500) index. We consider 2880 score-driven portfolio strategies. We use score-driven Clayton, rotated Clayton, Frank, Gaussian, Gumbel, rotated Gumbel, Plackett, and Student’s t copulas. We use several classical and score-driven models of marginal distribution. We use weekly, monthly, quarterly, semi-annual, and annual updates of portfolio weights. We use minimum-variance, maximum Sharpe ratio, and maximum utility function strategies. We use rolling data-windows for portfolio optimization for the COVID-19 investment period of February 2020 to September 2021. We classify competing portfolios by using a new robust multi-step model confidence set (MCS) test approach and provide evidence of the superiority of score-driven portfolios.

Keywords: dynamic conditional score (DCS) models; generalized autoregressive score (GAS) models; score-driven copulas; equity-gold portfolios; model confidence set (MCS) (search for similar items in EconPapers)
JEL-codes: C32 C52 C58 G11 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/snde-2022-0107

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