图 书 馆
波动性建模所需的多输出异方差高斯法的非参数混合物Nonparametric Mixtures of Multi-Output Heteroscedastic Gaussian Processes for Volatility Modeling |
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| 课程网址: | http://videolectures.net/nipsworkshops2012_platanios_processes/ |
| 主讲教师: | Platanios Emmanouil A |
| 开课单位: | 伦敦帝国学院 |
| 开课时间: | 2013-01-16 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this work, we present a nonparametric Bayesian method for multivariate volatility modeling. Our approach is based on postulation of a novel mixture of multioutput heteroscedastic Gaussian processes to model the covariance matrices of multiple assets. Specifically, we use the Pitman-Yor process prior as the nonparametric prior imposed over the components of our model, which are taken as multioutput heteroscedastic Gaussian processes obtained by introducing appropriate convolution kernels that combine simple heteroscedastic Gaussian processes under a multioutput scheme. We exhibit the efficacy of our approach in a volatility prediction task. |
| 关 键 词: | 非参数; 贝叶斯方法; 多元波动模型; 协方差矩阵 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-30：yumf |
| 阅读次数: | 52 |