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Portfolio optimization based on GARCH-EVT-Copula forecasting models
Linnaeus University, School of Business and Economics, Department of Management Accounting and Logistics.
Linnaeus University, School of Business and Economics, Department of Management Accounting and Logistics. Jönköping university, Sweden.
Åbo Akad Univ, Finland.
2018 (English)In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 34, no 3, p. 497-506Article in journal (Refereed) Published
Abstract [en]

This study uses GARCH-EVT-copula and ARMA-GARCH-EVT-copula models to perform out-of-sample forecasts and simulate one-day-ahead returns for ten stock indexes. We construct optimal portfolios based on the global minimum variance (GMV), minimum conditional value-at-risk (Min-CVaR) and certainty equivalence tangency (CET) criteria, and model the dependence structure between stock market returns by employing elliptical (Student-t and Gaussian) and Archimedean (Clayton, Frank and Gumbel) copulas. We analyze the performances of 288 risk modeling portfolio strategies using out-of-sample back-testing. Our main finding is that the CET portfolio, based on ARMA-GARCH-EVT-copula forecasts, outperforms the benchmark portfolio based on historical returns. The regression analyses show that GARCH-EVT forecasting models, which use Gaussian or Student-t copulas, are best at reducing the portfolio risk. (C) 2018 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 34, no 3, p. 497-506
Keywords [en]
GARCH models, Extreme value theory, Copula models, Conditional value-at-risk, Portfolio optimization
National Category
Economics and Business
Research subject
Economy
Identifiers
URN: urn:nbn:se:lnu:diva-79013DOI: 10.1016/j.ijforecast.2018.02.004ISI: 000449722000008Scopus ID: 2-s2.0-85046680082OAI: oai:DiVA.org:lnu-79013DiVA, id: diva2:1268532
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2021-11-26Bibliographically approved
In thesis
1. Copula-based Portfolio Optimization
Open this publication in new window or tab >>Copula-based Portfolio Optimization
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis studies and develops copula-based portfolio optimization. The overall purpose is to clarify the effects of copula modeling for portfolio allocation andsuggest novel approaches for copula-based optimization. The thesis is a compilation of five papers. The first and second papers study and introduce copula-based methods; the third, fourth, and fifth papers extend their applications to the Black-Litterman (BL) approach, expectile Value-at-Risk (EVaR), and multicriteria optimization, respectively.

The first paper focuses on applying copula-based forecasting models and studying tail dependence and how the risk model choice affects asset allocation. Using international stock markets, an analysis of the performance of several risk modeling portfolio strategies indicates that GARCH-EVT forecasting models, which use Gaussian or Student-t copulas, are best at reducing portfolio risk.

In the second paper, vine copulas are applied to study portfolio strategies during the global financial and COVID-19 crises. Overall, we find that the Student-t drawable vine copula models perform best with regard to risk reduction, both for the entire 2005–2012 period as well as during the global financial crisis. For the COVID-19 crisis, however, we find that the asymmetric Joe C-vine copula model performs bestin reducing downside portfolio risk.

The third paper includes a methodological contribution in that it incorporates dependency structure modeling with the BL approach and applying tail constraintsin reward-risk maximization. Our empirical analysis and robustness check indicate better performance for the CBL portfolios in terms of lower tail risk and higher risk-adjusted returns compared to the benchmark strategies.

The fourth paper investigates EVaR as the risk measure in dynamic copula-based portfolio optimization and compares it to the common variance and conditional Value-at-Risk (CVaR). Using ten S&P 500 industry sectors, EVaR leads to a min-risk dynamic generalized additive models (GAMC-vine) portfolio that achieves higher out-of-sample average return and risk-adjusted ratios. Furthermore, EVaR shows a better portfolio ranking than CVaR and the copula-based variance and EVaR portfolios show higher-order stochastic dominance over CVaR strategies.

The fifth paper develops a copula-based multi-objective portfolio (MOP) optimization. Applying the copula-based multi-objective portfolio optimization (MOP) optimization model, we investigate the impacts of objective functions and several multivariate risk models on portfolio performance. In general, there isevidence that the copula-based multicriteria portfolios perform better than those produced using the other predictive models in terms of the downside risk. With regard to portfolio attributes, the dividend yield and beta coefficient significantly reduce portfolio tail risk measures.

Place, publisher, year, edition, pages
Linnaeus University Press, 2021
Series
Linnaeus University Dissertations ; 416/2021
Keywords
Copula, portfolio optimization, conditional Value-at-Risk, vine copulas, asymmetric tail dependence, Black-Litterman approach, expectile Value-at-Risk, multiobjective portfolios
National Category
Business Administration
Research subject
Economy, Business administration
Identifiers
urn:nbn:se:lnu:diva-106641 (URN)9789189283794 (ISBN)9789189283800 (ISBN)
Public defence
2021-09-03, 09:15 (English)
Opponent
Supervisors
Available from: 2021-08-30 Created: 2021-08-30 Last updated: 2024-03-06Bibliographically approved

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Sahamkhadam, MaziarStephan, Andreas

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