Building Efficient Frontier for Non-normal Asset Returns using Copulas
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Économisez 5%
The aim of this work is to provide a comprehensive empirical study of the evolution of dependence structure and tail dependence for a large set of developed and emerging markets.
Three methodological contributions are seriously developed.
First and based on well documented literature on empirical regularities, a novel Mean-CVaR-Copula approach is introduced to allow for non linearity dependence structure and deviation from normality hypothesis.
In order to do so, an alternative measure of diversification is involved to classical linear correlation model, which is suitable under normality hypothesis.
Indeed, the joint distribution is modeled using Copulas functions to capt non linear dependence across market.
Second, an extension of Kapil Agrawal’s approach (2008) is proposed to provide more reliable results based on Copula theory.
Third, a Copula theory methodology is developed to link long memory parameter (long range time dependence parameter) and Copula parameter (dependence parameter) in order to control financial crisis persistence and reduce their effect on assets allocation.
Hela Mzoughi Chargui is a doctor in management science and assistant at the University of Sousse.
His interests include actuarial, statistical and quantitative finance.
Hela Mzoughi Chargui is the author of numerous scientific publications in the fields of risk management and applied statistics.
Fiche technique
- Auteur
- Hela Mzoughi Chargui
- Langue
- Français
- Éditeur
- Éditions universitaires européennes
- Pays
Tunisie
The aim of this work is to provide a comprehensive empirical study of the evolution of dependence structure and tail dependence for a large set of developed and emerging markets.
Three methodological contributions are seriously developed.
First and based on well documented literature on empirical regularities, a novel Mean-CVaR-Copula approach is introduced to allow for non linearity dependence structure and deviation from normality hypothesis.
In order to do so, an alternative measure of diversification is involved to classical linear correlation model, which is suitable under normality hypothesis.
Indeed, the joint distribution is modeled using Copulas functions to capt non linear dependence across market.
Second, an extension of Kapil Agrawal’s approach (2008) is proposed to provide more reliable results based on Copula theory.
Third, a Copula theory methodology is developed to link long memory parameter (long range time dependence parameter) and Copula parameter (dependence parameter) in order to control financial crisis persistence and reduce their effect on assets allocation.