.png){kind=icon}
返回
Constrained nonlinear programming for volatility estimation with GARCH models
.png){kind=icon}
PRE
AI
DOI：10.1137/S003614450140011.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes a constrained nonlinear programming view of generalized autoregressive conditional heteroskedasticity (GARCH) volatility estimation models in financial econometrics. These models are usually presented to the reader as unconstrained optimization models with recursive terms in the literature, whereas they actually fall into the domain of nonconvex nonlinear programming. Our results demonstrate that constrained nonlinear programming is a worthwhile exercise for GARCH models, especially for the bivariate and trivariate cases, as they offer a significant improvement in the quality of the solution of the optimization problem over the diagonal VECH and the BEKK representations of the multivariate GARCH model.
Keyword:
time series econometrics
constrained nonlinear programming
multivariate CARCH
volatility estimation
maximum likelihood estimation
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:
6.1
论文数:
888
被引数:
1.2W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏
查看更多内容