英文标题：
《Semi-parametric time series modelling with autocopulas》
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作者：
Antony Ware, Ilnaz Asadzadeh
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最新提交年份：
2015
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英文摘要：
In this paper we present an application of the use of autocopulas for modelling financial time series showing serial dependencies that are not necessarily linear. The approach presented here is semi-parametric in that it is characterized by a non-parametric autocopula and parametric marginals. One advantage of using autocopulas is that they provide a general representation of the auto-dependency of the time series, in particular making it possible to study the interdependence of values of the series at different extremes separately. The specific time series that is studied here comes from daily cash flows involving the product of daily natural gas price and daily temperature deviations from normal levels. Seasonality is captured by using a time dependent normal inverse Gaussian (NIG) distribution fitted to the raw values.
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中文摘要：
在本文中，我们提出了一个应用程序的使用自动拟合金融时间序列建模显示序列依赖性，不一定是线性的。这里介绍的方法是半参数的，因为它的特点是非参数自动填充和参数边缘。使用autocopulas的一个优点是，它们提供了时间序列自相关性的一般表示，特别是使人们能够分别研究不同极端情况下序列值的相互依赖性。本文研究的具体时间序列来自每日现金流，涉及每日天然气价格和每日温度偏离正常水平的乘积。季节性是通过使用与时间相关的正态逆高斯（NIG）分布拟合原始值来捕捉的。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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