Integrated deep neural networks with Copula-ARMA-GARCH-Stable models for CVaR portfolio optimization

This research combines modern deep neural networks with traditional econometric methods, integrating Copula models with ARMA-GARCH-Stable to improve accuracy in predicting asset returns, volatilities and capturing heavy tails and asymmetries.

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Surprisingly, the proposed model outperforms traditional methods and beats the market index in US stocks, offering enhanced risk management through improved Mean-Variance and Conditional Value at Risk portfolio optimization strategies.

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Extensive data analysis and simulations reveal that while innovation drives remarkable forecasting accuracy, the models complexity might hinder practical deployment, particularly in less resource-rich environments requiring simpler implementation strategies overall.

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This paper presents a novel integration of deep neural networks with traditional econometric models, including $Copula$, $ARMA$-$GARCH$, and $Stable$ distributions. It uniquely addresses heavy-tailed asset returns and enhances $CVaR$ portfolio optimization. The methodology is innovative and compelling, offering a fresh, rigorous perspective in quantitative finance, advancing market risk and returns.

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