Article (Scientific journals)
Block bootstrap methods and the choice of stocks for the long run
Cogneau, Philippe; Zakamouline, Valeri
2013In Quantitative Finance, 13
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Keywords :
bootstrap; time-series data; long run; Serial dependence; Parameter estimation
Abstract :
[en] Financial advisors commonly recommend that the investment horizon should be rather long in order to benefit from the ‘time diversification’. In this case, in order to choose the optimal portfolio, it is necessary to estimate the risk and reward of several alternative portfolios over a long-run given a sample of observations over a short-run. Two interrelated obstacles in these estimations are lack of sufficient data and the uncertainty in the nature of the return generating process. To overcome these obstacles researchers rely heavily on block bootstrap methods. In this paper we demonstrate that the estimates provided by a block bootstrap method are generally biased and we propose two methods of bias reduction. We show that an improper use of a block bootstrap method usually causes underestimation of the risk of a portfolio whose returns are independent over time and overestimation of the risk of a portfolio whose returns are mean-reverting.
Disciplines :
Quantitative methods in economics & management
Author, co-author :
Cogneau, Philippe ;  Université de Liège - ULiège > Doct. sc. écon. & gest. (sc. gestion - Bologne)
Zakamouline, Valeri;  University of Agder (Norway)
Language :
English
Title :
Block bootstrap methods and the choice of stocks for the long run
Publication date :
2013
Journal title :
Quantitative Finance
ISSN :
1469-7688
eISSN :
1469-7696
Publisher :
Routledge
Volume :
13
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 28 August 2012

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