Book chapter
Approaches to High‐Dimensional Covariance and Precision Matrix Estimations
Financial Signal Processing and Machine Learning, pp.100-134
John Wiley & Sons, Ltd
2016
DOI: 10.1002/9781118745540.ch6
Abstract
This chapter introduces several recent developments for estimating large covariance and precision matrices without assuming the covariance matrix to be sparse. It explains two methods for covariance estimation: namely covariance estimation via factor analysis, and precision Matrix Estimation and Graphical Models. The low rank plus sparse representation holds on the population covariance matrix. The chapter presents several applications of these methods, including graph estimation for gene expression data, and several financial applications. It then shows how estimating covariance matrices of high‐dimensional asset excess returns play a central role in applications of portfolio allocations and in risk management. The chapter explains the factor pricing model, which is one of the most fundamental results in finance. It elucidates estimating risks of large portfolios and large panel test of factor pricing models. The chapter illustrates the recent developments of efficient estimations in panel data models.
Details
- Title: Subtitle
- Approaches to High‐Dimensional Covariance and Precision Matrix Estimations
- Creators
- Jianqing Fan - Princeton UniversityYuan Liao - University of Maryland, College ParkHan Liu - Princeton University
- Contributors
- Ali N Akansu (Editor) - New Jersey Institute of TechnologySanjeev R Kulkarni (Editor) - Princeton UniversityDmitry Malioutov (Editor) - IBM T.J. Watson Research Center, USA
- Resource Type
- Book chapter
- Publication Details
- Financial Signal Processing and Machine Learning, pp.100-134
- DOI
- 10.1002/9781118745540.ch6
- Publisher
- John Wiley & Sons, Ltd; Chichester, UK
- Number of pages
- 35
- Language
- English
- Date published
- 2016
- Academic Unit
- Economics
- Record Identifier
- 9984936839302771
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