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Portfolio selection based on asymmetric Laplace distribution, coherent risk measure, and expectation-maximization estimation

  • Received: 11 June 2018 Accepted: 22 June 2018 Published: 09 October 2018
  • JEL Codes: G11

  • In this paper, portfolio selection problem is studied under Asymmetric Laplace Distribution (ALD) framework. Asymmetric Laplace distribution is able to capture tail-heaviness, skewness, and leptokurtosis observed in empirical financial data that cannot be explained by traditional Gaussian distribution. Under Asymmetric Laplace distribution framework, portfolio selection methods based on di erent risk measures are discussed. Moreover, we derived the Expectation-Maximization (EM) procedure for parameter estimation of Asymmetric Laplace distribution. Performance of the proposed method is illustrated via extensive simulation studies. Two real data examples are complemented to confirm that the Asymmetric Laplace distribution based portfolio selection models are effcient.

    Citation: Yue Shi, Chi Tim Ng, Ka-Fai Cedric Yiu. Portfolio selection based on asymmetric Laplace distribution, coherent risk measure, and expectation-maximization estimation[J]. Quantitative Finance and Economics, 2018, 2(4): 776-797. doi: 10.3934/QFE.2018.4.776

    Related Papers:

  • In this paper, portfolio selection problem is studied under Asymmetric Laplace Distribution (ALD) framework. Asymmetric Laplace distribution is able to capture tail-heaviness, skewness, and leptokurtosis observed in empirical financial data that cannot be explained by traditional Gaussian distribution. Under Asymmetric Laplace distribution framework, portfolio selection methods based on di erent risk measures are discussed. Moreover, we derived the Expectation-Maximization (EM) procedure for parameter estimation of Asymmetric Laplace distribution. Performance of the proposed method is illustrated via extensive simulation studies. Two real data examples are complemented to confirm that the Asymmetric Laplace distribution based portfolio selection models are effcient.


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