Optimal Portfolio under Non-Extensive Statistical Mechanics and Value-at-Risk Constraints
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In this study, we consider the optimal portfolio selection problem with a value-at-risk constraint in the non-extensive statistical mechanics framework. We propose a portfolio selection model, which is suitable not only for normal return distributions, but also for non-normal return distributions. Using Chinese stock data, under the normal and q-Gaussian return distributions, we provide empirical results. The results indicate that portfolio selections under the q-Gaussian return distributions are considerably different from those under the normal return distributions. Moreover, by using the q-Gaussian distribution, the underestimated portfolio risk can be effectively avoided.
- College of Finance and Mathematics, West Anhui University, Lu'an, Anhui, China
- Financial Risk Intelligent Control and Prevention Institute of West Anhui University, Lu'an, Anhui, China
- School of Mathematics and Information Sciences, Henan Normal University, Xinxiang, Henan, China
- School of Economics and Management, Nanjing University of Science and Technology, Nanjing, Jiangsu, China
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