Portfolio optimization through hybrid deep learning and genetic algorithms vine Copula-GARCH-EVT-CVaR model

Rihab Bedoui, Ramzi Benkraiem, Khaled Guesmi and Islem Kedidi ()
Additional contact information
Ramzi Benkraiem: Audencia Business School

Post-Print from HAL

Abstract: This study investigates the potential benefits of using the Conditional Value at Risk (CVaR) portfolio optimization approach with a GARCH model, Extreme Value Theory (EVT), and Vine Copula to obtain the optimal allocation decision for a portfolio consisting of Bitcoin, gold, oil, and stock indices. First, we fit a suitable GARCH model to the return series for each asset, followed by employing the Generalized Pareto Distribution (GPD) to model the innovation tails. Next, we construct a Vine Copula-GARCH-EVT model to capture the interdependence structure between the assets. To refine risk assessment, we combine our model with a Monte Carlo simulation and Mean-CVaR model to optimize the portfolio. In addition, we utilize a novel version of deep machine learning's genetic algorithm to address the optimization decision. This research contributes new evidence to the CVaR portfolio optimization approach and provides insights for portfolio managers seeking to optimize multi-asset portfolios.

Keywords: Vine copula; GARCH; EVT; CVaR; Portfolio optimization decision; NSGA-II deep machine learning genetic algorithm (search for similar items in EconPapers)
Date: 2023-12
Note: View the original document on HAL open archive server: https://audencia.hal.science/hal-04631234
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Published in Technological Forecasting and Social Change, 2023, 197, pp.122887. ⟨10.1016/j.techfore.2023.122887⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04631234

DOI: 10.1016/j.techfore.2023.122887

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().