Lada M. Kyj

Humboldt University of Berlin

Unter den Linden 6

Berlin, AK 10099

Germany

Quantitative Products Laboratory

Alexanderstrasse 5

Berlin, 10099

Germany

SCHOLARLY PAPERS

4

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CITATIONS
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22

Scholarly Papers (4)

1.

Covariance Estimation in Dynamic Portfolio Optimization: A Realized Single Factor Model

AFA 2010 Atlanta Meetings Paper
Number of pages: 38 Posted: 22 Mar 2009 Last Revised: 17 Oct 2009
Lada M. Kyj, Barbara Ostdiek and Katherine Ensor
Humboldt University of Berlin, Rice University - Jesse H. Jones Graduate School of Business and Rice University - George R. Brown School of Engineering
Downloads 594 (43,903)
Citation 5

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Factor Model, Realized Covariance, Volatilty Timing

2.

A Blocking and Regularization Approach to High Dimensional Realized Covariance Estimation

Journal of Applied Econometrics, Forthcoming
Number of pages: 30 Posted: 18 Oct 2009 Last Revised: 22 Aug 2010
University of Vienna - Department of Statistics and Operations Research, Humboldt University of Berlin and Deutsche Bank AG (London)
Downloads 289 (103,647)
Citation 28

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covariance estimation, blocking, realized kernel, regularization, microstructure noise, asynchronous trading

3.

The Merit of High-Frequency Data in Portfolio Allocation

Number of pages: 43 Posted: 12 Sep 2011
Nikolaus Hautsch, Lada M. Kyj and Peter Malec
University of Vienna - Department of Statistics and Operations Research, Humboldt University of Berlin and University of Cambridge - Faculty of Economics
Downloads 250 (120,697)
Citation 7

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spectral decomposition, mixing frequencies, factor model, blocked realized kernel, covariance prediction, portfolio optimization

4.

Do High-Frequency Data Improve High-Dimensional Portfolio Allocations?

Number of pages: 44 Posted: 05 Mar 2013
Nikolaus Hautsch, Lada M. Kyj and Peter Malec
University of Vienna - Department of Statistics and Operations Research, Humboldt University of Berlin and University of Cambridge - Faculty of Economics
Downloads 159 (183,907)
Citation 11

Abstract:

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portfolio optimization, spectral decomposition, regularization, blocked realized kernel, covariance prediction