英文标题：
《Forward-looking portfolio selection with multivariate non-Gaussian
  models and the Esscher transform》
---
作者：
Michele Leonardo Bianchi and Gian Luca Tassinari
---
最新提交年份：
2018
---
英文摘要：
  In this study we suggest a portfolio selection framework based on option-implied information and multivariate non-Gaussian models. The proposed models incorporate skewness, kurtosis and more complex dependence structures among stocks log-returns than the simple correlation matrix. The two models considered are a multivariate extension of the normal tempered stable (NTS) model and the generalized hyperbolic (GH) model, respectively, and the connection between the historical measure P and the risk-neutral measure Q is given by the Esscher transform. We consider an estimation method that simultaneously calibrate the time series of univariate log-returns and the univariate observed volatility smile. To calibrate the models, there is no need of liquid multivariate derivative quotes. The method is applied to fit a 50-dimensional series of stock returns, to evaluate widely known portfolio risk measures and to perform a portfolio selection analysis.
---
中文摘要：
在这项研究中，我们提出了一个基于期权隐含信息和多元非高斯模型的投资组合选择框架。与简单的相关矩阵相比，所提出的模型包含了股票对数收益率之间的偏度、峰度和更复杂的依赖结构。这两个模型分别是正态回火稳定（NTS）模型和广义双曲线（GH）模型的多元扩展，历史测度P和风险中性测度Q之间的关系由Esscher变换给出。我们考虑一种同时校准单变量对数收益率时间序列和单变量观测波动率微笑的估计方法。为了校准模型，不需要液体多元导数报价。该方法用于拟合50维股票收益率序列，评估广为人知的投资组合风险度量，并进行投资组合选择分析。
---
分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
--

---
PDF下载：
-->