High-Dimensionality in Statistics and Portfolio Optimization

High-Dimensionality in Statistics and Portfolio Optimization
Glombek, Konstantin
Preis 43,00inkl. ges. MwSt.
ISBN 978-3-8441-0213-0
Bestell-Nr. 0213C
Gewicht 226 g
Warengruppe Multidisziplinäre Reihen
Sachgruppe Quantitative Ökonomie
Einband broschiert
Sprache Englisch
Umfang 148
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1. Introduction

2. Prerequisites
2.1. Random matrices and limiting spectral distributions
2.2. Covariance matrix testing
2.3. Portfolio optimization

3. Semicircle law of Tyler's M-estimator for scatter
3.1. Introduction
3.2. Tyler's M-estimator
3.3. Proof of the theorem
3.4. Outlook for future research

4. A Jarque-Bera test for sphericity of a large-dimensional covariance matrix
4.1. Introduction
4.2. Preliminaries
4.3. Statistical setting
4.4. Distribution of the test statistic
4.5. Properties of the test
4.6. Conclusion
4.7. Proofs

5. Statistical inference for high-dimensional portfolios
5.1. Introduction
5.2. Preliminaries
5.3. Inference under general asymptotics
5.4. Empirical study
5.5. Conclusion
5.6. Proofs

6. Outlook for related research

7. Summary

Über den Autor

Konstantin Glombek, born in 1980, studied Mathematics with a focus on Numerical Finance and Statistics at the University of Cologne. He finished his studies in 2009 and then became a member of the Graduate School of Risk Management at the University of Cologne. In 2012, he completed his doctoral studies in Statistics with distinction. He has written articles on high-dimensional data analysis, covariance matrix tests and portfolio optimization and has presented them at several international conferences.

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