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

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 different 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 efficient.