论文标题
广泛的自回归分数不对称的拉普拉斯分布和极端的下降风险预测
Generalized Autoregressive Score asymmetric Laplace Distribution and Extreme Downward Risk Prediction
论文作者
论文摘要
由于胸部分布，高峰值和较厚的尾巴以及财务回报数据的不对称性，因此很难描述传统分布。近年来，广泛的自回旋得分（GAS）已在许多领域使用并取得了良好的效果。在本文中，在广义自回旋评分（气）的框架下，改进了不对称的拉普拉斯分布（ALD），并提出了燃气模型，该模型具有时变参数的特征，可以描述峰值较厚的尾巴，偏见，偏见和不对称分布。该模型用于研究上海指数，深圳指数和SME董事会指数。发现：1）三个索引的分布参数和矩具有明显的时变特征和聚合特征。 2）与用于计算VAR和ES的常用模型相比，Gas-Ald模型具有很高的预测效应。
Due to the skessed distribution, high peak and thick tail and asymmetry of financial return data, it is difficult to describe the traditional distribution. In recent years, generalized autoregressive score (GAS) has been used in many fields and achieved good results. In this paper, under the framework of generalized autoregressive score (GAS), the asymmetric Laplace distribution (ALD) is improved, and the GAS-ALD model is proposed, which has the characteristics of time-varying parameters, can describe the peak thick tail, biased and asymmetric distribution. The model is used to study the Shanghai index, Shenzhen index and SME board index. It is found that: 1) the distribution parameters and moments of the three indexes have obvious time-varying characteristics and aggregation characteristics. 2) Compared with the commonly used models for calculating VaR and ES, the GAS-ALD model has a high prediction effect.
