Laplace distribution is popular in the field of economics and finance. Still, data sets often show a lack of symmetry and a tendency of being bounded from either side of their support. In view of this, we introduce a new family of skew distribution using the skewing mechanism of Azzalini (1985), namely, skew-symmetric-Laplace-uniform distribution (SSLUD). Here uniform distribution is used not only to introduce skewness in Laplace distribution but also to restrict distribution support on one side of the real line. This paper provides a comprehensive description of the essential distributional properties of SSLUD. Estimators of the parameter are obtained using the method of moments and the method of maximum likelihood. The finite sample and asymptotic properties of these estimators are studied using simulation. It is observed that the maximum likelihood estimator is better than the moment estimator through a simulation study. Finally, an application of SSLUD to real-life data on the daily percentage change in the price of NIFTY 50, an Indian stock market index, is presented.

翻译：拉普拉斯分布在经济学与金融学领域应用广泛。然而，数据集常呈现非对称性，且其支撑集往往存在单侧有界趋势。鉴于此，我们基于Azzalini（1985）的偏斜机制，引入了一类新的偏斜分布族，即偏斜对称拉普拉斯-均匀分布（SSLUD）。此处采用均匀分布不仅是为了在拉普拉斯分布中引入偏斜性，同时也为了将分布支撑集限制在实轴的单侧。本文系统阐述了SSLUD的基本分布性质，并分别采用矩估计法和极大似然估计法获得了参数估计量。通过模拟研究考察了这些估计量的有限样本性质与渐近性质。模拟研究表明，极大似然估计量优于矩估计量。最后，将SSLUD应用于印度股市指数NIFTY 50的日价格百分比变化实际数据进行了实证分析。