.png){kind=icon}
返回
Multivariate semi-nonparametric distributions with dynamic conditional correlations
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijforecast.2010.02.005.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper generalizes the Dynamic Conditional Correlation (DCC) model of Engle (2002), incorporating a flexible non-Gaussian distribution based on Gram-Charlier expansions. The resulting semi-nonparametric-DCC (SNP-DCC) model allows estimation in two stages and deals with the negativity problem which is inherent in truncated SNP densities. We test the performance of a SNP-DCC model with respect to the (Gaussian)-DCC through an empirical application of density forecasting for portfolio returns. Our results show that the proposed multivariate model provides a better in-sample fit and forecast of the portfolio returns distribution, and thus is useful for financial risk forecasting and evaluation. (C) 2010 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keyword:
Density forecasts
Financial markets
GARCH models
Multivariate time series
Semi-nonparametric methods
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.1
论文数:
3.1K
被引数:
9.9K
机构
引用论文
.png){kind=icon}
分享收藏分享收藏
查看更多内容