Abstract
In the presence of uncertainty of asset returns, choosing an appropriate risk measure and determining the optimal weights of assets in a portfolio remain formidable and challenging problems. In this paper, we propose and study a mean-conditional value at risk-skewness portfolio optimization model based on the asymmetric Laplace distribution, which is suitable for describing the leptokurtosis, fat-tail, and skewness characteristics of financial assets. In addition, skewness is added into the portfolio optimization model to meet the diverse needs of investors. To solve this multi-objective problem, we suggest a simplified model with exactly the same solution. This modified model greatly reduces the complexity of the problem. Therefore, the mean-conditional value at risk-skewness model can be correspondingly solved. In order to illustrate the method, we provide an application concerning the portfolio allocation of 19 constituent stocks of S&P 500 index using our model. We show that this model could make important contributions to research on investment decision making.
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Notes
The top 20 constituents of S&P 500 by index weight in 2012 are: AAPL, XOM, CVX, MSFT, IBM, GE, JNJ, PFE, PG, INTC, PM, CSCO, GOOG, KO, WMT, ORCL, ABT, MRK, T, COP. However, historical prices for PM is incomplete (dated from March 17, 2008), so PM is excluded in our analysis.
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Acknowledgments
The authors thank Professor XIAOJUN SHI and Doctor XIN JIANG for their helpful comments. We are also grateful for the anonymous reviewers’ constructive suggestions. This work is supported by the National Natural Science Foundation of China under Grants No. 71373017 and No. 71371022
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Zhao, S., Lu, Q., Han, L. et al. A mean-CVaR-skewness portfolio optimization model based on asymmetric Laplace distribution. Ann Oper Res 226, 727–739 (2015). https://doi.org/10.1007/s10479-014-1654-y
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DOI: https://doi.org/10.1007/s10479-014-1654-y