Abstract

http://ssrn.com/abstract=1806447
 
 

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Block Bootstrap Methods and the Choice of Stocks for the Long Run


Philippe Cogneau


University of Liege

Valeriy Zakamulin


University of Agder - Faculty of Economics

November 24, 2011

Forthcoming, Quantitative Finance

Abstract:     
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.

Number of Pages in PDF File: 24

Keywords: long-run, time-series data, serial dependence, parameter estimation, bootstrap, block bootstrap

JEL Classification: C13, C14, C15, G11

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Date posted: April 11, 2011 ; Last revised: November 18, 2012

Suggested Citation

Cogneau, Philippe and Zakamulin, Valeriy, Block Bootstrap Methods and the Choice of Stocks for the Long Run (November 24, 2011). Forthcoming, Quantitative Finance. Available at SSRN: http://ssrn.com/abstract=1806447 or http://dx.doi.org/10.2139/ssrn.1806447

Contact Information

Philippe Cogneau
University of Liege ( email )
B-4000 Liege
Belgium
Valeriy Zakamulin (Contact Author)
University of Agder - Faculty of Economics ( email )
School of Management
Service Box 422
Kristiansand, N-4604
Norway
+47 38141039 (Phone)
Feedback to SSRN


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