| 1. |
- Kordestani, Arash, et al.
(författare)
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Effects of the COVID-19 pandemic on stock price performance of blockchain-based companies
- 2022
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Ingår i: Ekonomska Istrazivanja. - : Taylor & Francis Group. - 1331-677X .- 1848-9664. ; 35:1, s. 3206-3224
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Tidskriftsartikel (refereegranskat)abstract
- The price of a stock rises or falls in relation to a number of different factors, including changes to the economy brought about by pandemics. A few studies have already identified the effect of the COVID-19 pandemic on the stock market. However, empirical evidence is lacking on changes in stock price performance of blockchain-based companies as a result of the COVID-19 pandemic. We use the event study approach to estimate stock expected returns by applying an asset pricing model over a thirty-day event window around the announcement on March 11, 2020 by the World Health Organization (WHO) regarding the outbreak of the coronavirus (COVID-19) as a global pandemic, using a sample of S&P Global 1200 companies. Overall, our results indicate more sensitivity in blockchain-based companies' stock prices to the COVID-19 pandemic compared to those of non-blockchain-based companies. Cumulative abnormal returns show that the stock price of blockchain-based companies recover losses slower than non-blockchain companies. Our findings are important for investors and shareholders for future pandemics and events.
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| 2. |
- Lööf, Hans, 1961-, et al.
(författare)
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Incorporating ESG into Optimal Stock Portfolios for the Global Timber & Forestry Industry
- 2023
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Ingår i: Journal of Forest Economics. - : Now Publishers Inc.. - 1104-6899 .- 1618-1530. ; 38:2, s. 133-157
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Tidskriftsartikel (refereegranskat)abstract
- This paper investigates how optimal portfolios of timber & forestry stocks perform relative to the global S&P timber & forestry index when corporate social responsibility (CSR) is considered. We incorporate CSR in the construction of optimal portfolios by utilizing combined environmental, social, and governance (ESG) scores. Historical as well as copula-augmented predictive models and ESG-constrained optimization are used to analyze out-of-sample performance of various portfolio strategies over the period 2018–2021. The results of copula-based portfolio strategies are better than of the historical models. Another insight gained by this study is that socially responsible investments in forestry stocks are feasible without sacrificing risk-adjusted returns.
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| 3. |
- Lööf, Hans, 1961-, et al.
(författare)
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Is Corporate Social Responsibility investing a free lunch? : The relationship between ESG, tail risk, and upside potential of stocks before and during the COVID-19 crisis
- 2022
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Ingår i: Finance Research Letters. - : Elsevier BV. - 1544-6123 .- 1544-6131. ; 46, s. 102499-
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Tidskriftsartikel (refereegranskat)abstract
- Did Corporate Social Responsibility investing benefit shareholders during the COVID-19 pandemic crisis? Distinguishing between downside tail risk and upside reward potential of stock returns, we provide evidence from 5,073 stocks listed on stock markets in ten countries. The findings suggest that better ESG ratings are associated with lower downside risk, but also with lower upside return potential. Thus, ESG ratings helped investors to reduce their risk exposure to the market turmoil caused by the pandemic, while maintaining the fundamental trade-off between risk and reward.
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| 4. |
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| 5. |
- Sahamkhadam, Maziar, et al.
(författare)
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Copula-based Black-Litterman portfolio optimization
- 2020
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- We extend the Black-Litterman (BL) approach to incorporate tail dependency in portfolio optimization and estimate the posterior joint distribution of returns using vine copulas. Our novel copula-based BL (CBL) model leads to flexibility in modeling returns symmetric and asymmetric multivariate distribution from a range of copula families. Based on a sample of 30 stocks, we evaluate the performance of the suggested CBL approach and portfolio optimization technique using out-of-sample back-testing. 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.
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| 6. |
- Sahamkhadam, Maziar, et al.
(författare)
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Copula-based Black–Litterman portfolio optimization
- 2022
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Ingår i: European Journal of Operational Research. - : Elsevier. - 0377-2217 .- 1872-6860. ; 297:3, s. 1055-1070
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Tidskriftsartikel (refereegranskat)abstract
- We extend the Black-Litterman (BL) approach to incorporate tail dependency in portfolio optimization and estimate the posterior joint distribution of returns using vine copulas. Our novel copula-based BL (CBL) model leads to flexibility in modeling returns symmetric and asymmetric multivariate distribution from a range of copula families. Based on a sample of the Eurostoxx 50 constituents (also for S&P 100 as robustness check), we evaluate the performance of the suggested CBL approach and portfolio optimization technique using out-of-sample back-testing. 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.
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| 7. |
- Sahamkhadam, Maziar
(författare)
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Copula-based Portfolio Optimization
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 8. |
- Sahamkhadam, Maziar
(författare)
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Dynamic copula-based expectile portfolios
- 2021
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Ingår i: Journal of Asset Management. - : Springer. - 1470-8272 .- 1479-179X. ; 22, s. 209-223
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Tidskriftsartikel (refereegranskat)abstract
- This study investigates expectile Value-at-Risk (EVaR) as a risk measure in dynamic copula-based portfolio optimization, compared with the common variance and CVaR. To estimate the dependence structure between asset returns, the canonical vine copula augmented with the generalized additive models (GAMC-vine) is used. Applying multivariate conditional distributions from the GAMC-vine model, step-ahead asset return forecasts are obtained and used to construct dynamic copula-based EVaR portfolios. Using ten S&P 500 industry sectors, EVaR leads to a min-risk dynamic GAMC-vine portfolio that achieves higher out-of-sample average return and risk-adjusted ratios. Furthermore, EVaR shows a better portfolio ranking than CVaR. Moreover, the copula-based variance and EVaR portfolios show higher-order stochastic dominance compared to CVaR strategies. Finally, a subsample stochastic dominance analysis reveals that, in overall, the risk minimization does not benefit from the choice of risk modeling. However, the dynamic copula model leads to optimal portfolios that dominate the equally weighted benchmark more often compared to those from historical approach.
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| 9. |
- Sahamkhadam, Maziar, et al.
(författare)
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Portfolio optimization based on forecasting models using vine copulas : An empirical assessment for global financial crises
- 2023
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Ingår i: Journal of Forecasting. - : John Wiley & Sons. - 0277-6693 .- 1099-131X. ; 42:8, s. 2139-2166
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Tidskriftsartikel (refereegranskat)abstract
- We employ and examine vine copulas in modeling symmetric and asymmetric dependency structures and forecasting financial returns from 2001 to 2022, a period that includes the 2008 financial crisis, the 2011 European sovereign debt crisis, the 2020 COVID-19 pandemic crisis, and the 2022 Russian invasion of Ukraine with the resulting energy crisis. We analyze the asset allocations performed and test different portfolio strategies, such as maximum Sharpe ratio, minimum variance, and minimum conditional value at risk. Using international financial market indices, we specify the regular, drawable, and canonical vine copulas, such as the Gaussian, Student's t, Clayton, Frank, Joe, Gumbel, and mixed copulas, and analyze both in-sample and out-of-sample portfolio performances. Out-of-sample portfolio back-testing shows that vine copulas reduce portfolio risk better than the benchmark portfolio strategies and also better than simple multivariate copulas. Overall, we find that mixed vine copula models perform best with regard to risk reduction, both for the entire period 2001–2022 and during financial crisis periods. Thus, a mixture of symmetric and asymmetric copula families works best in terms of portfolio risk reduction irrespective of the chosen optimization approach.
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| 10. |
- Sahamkhadam, Maziar, et al.
(författare)
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Portfolio optimization based on GARCH-EVT-Copula forecasting models
- 2018
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 34:3, s. 497-506
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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