It is widely known that the common risk-factors derived from PCA beyond the
first eigenportfolio are generally difficult to interpret and thus to use in
practical portfolio management. We explore a alternative approach (HPCA) which
makes strong use of the partition of the market into sectors. We show that this
approach leads to no loss of information with respect to PCA in the case of
equities (constituents of the S&P 500) and also that the associated common
factors admit simple interpretations. The model can also be used in markets in
which the sectors have asynchronous price information, such as single-name
credit default swaps, generalizing the works of Cont and Kan (2011) and Ivanov
(2016).
Beschreibung
Hierarchical PCA and Applications to Portfolio Management
%0 Generic
%1 avellaneda2019hierarchical
%A Avellaneda, Marco
%D 2019
%K correlation finance market mathematics statistics stock
%T Hierarchical PCA and Applications to Portfolio Management
%U http://arxiv.org/abs/1910.02310
%X It is widely known that the common risk-factors derived from PCA beyond the
first eigenportfolio are generally difficult to interpret and thus to use in
practical portfolio management. We explore a alternative approach (HPCA) which
makes strong use of the partition of the market into sectors. We show that this
approach leads to no loss of information with respect to PCA in the case of
equities (constituents of the S&P 500) and also that the associated common
factors admit simple interpretations. The model can also be used in markets in
which the sectors have asynchronous price information, such as single-name
credit default swaps, generalizing the works of Cont and Kan (2011) and Ivanov
(2016).
@misc{avellaneda2019hierarchical,
abstract = {It is widely known that the common risk-factors derived from PCA beyond the
first eigenportfolio are generally difficult to interpret and thus to use in
practical portfolio management. We explore a alternative approach (HPCA) which
makes strong use of the partition of the market into sectors. We show that this
approach leads to no loss of information with respect to PCA in the case of
equities (constituents of the S&P 500) and also that the associated common
factors admit simple interpretations. The model can also be used in markets in
which the sectors have asynchronous price information, such as single-name
credit default swaps, generalizing the works of Cont and Kan (2011) and Ivanov
(2016).},
added-at = {2019-10-09T16:06:04.000+0200},
author = {Avellaneda, Marco},
biburl = {https://www.bibsonomy.org/bibtex/2aa6eb2a8ac4302b6dd5339efcee0e3c5/public4mac},
description = {Hierarchical PCA and Applications to Portfolio Management},
interhash = {a4c838944e93d299421c061daa060d80},
intrahash = {aa6eb2a8ac4302b6dd5339efcee0e3c5},
keywords = {correlation finance market mathematics statistics stock},
note = {cite arxiv:1910.02310},
timestamp = {2019-10-09T16:06:04.000+0200},
title = {Hierarchical PCA and Applications to Portfolio Management},
url = {http://arxiv.org/abs/1910.02310},
year = 2019
}