Bond portfolio optimization using dynamic factor models

João F. Caldeira; Guilherme V. Moura; André A.P. Santos

doi:10.1016/j.jempfin.2016.03.004

Bond portfolio optimization using dynamic factor models

João F. Caldeira^*, Guilherme V. Moura, André A.P. Santos

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract / Description of output

A general class of dynamic factor models is used to obtain optimal bond portfolios, and to develop a duration-constrained mean-variance optimization, which can be used to improve bond indexing. An empirical application involving two large data sets of U.S. Treasuries shows that the proposed portfolio policy outperforms a set of yield curve strategies used in bond desks. Additionally, we propose a dynamic rule to switch among alternative bond investment strategies, and find that the benefits of such dynamic rule are even more pronounced when the set of available policies is augmented with the proposed mean-variance portfolios.

Original language	English
Pages (from-to)	128-158
Number of pages	31
Journal	Journal of Empirical Finance
Volume	37
Early online date	24 Mar 2016
DOIs	https://doi.org/10.1016/j.jempfin.2016.03.004
Publication status	Published - 1 Jun 2016

Keywords / Materials (for Non-textual outputs)

bond indexing
C53
dynamic policy selection
E43
G17
Kalman filter
out-of-sample evaluation
portfolio optimization
yield curve forecasts

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10.1016/j.jempfin.2016.03.004

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@article{d84e1ef511d84d8a8dfecd583370017e,

title = "Bond portfolio optimization using dynamic factor models",

abstract = "A general class of dynamic factor models is used to obtain optimal bond portfolios, and to develop a duration-constrained mean-variance optimization, which can be used to improve bond indexing. An empirical application involving two large data sets of U.S. Treasuries shows that the proposed portfolio policy outperforms a set of yield curve strategies used in bond desks. Additionally, we propose a dynamic rule to switch among alternative bond investment strategies, and find that the benefits of such dynamic rule are even more pronounced when the set of available policies is augmented with the proposed mean-variance portfolios.",

keywords = "bond indexing, C53, dynamic policy selection, E43, G17, Kalman filter, out-of-sample evaluation, portfolio optimization, yield curve forecasts",

author = "Caldeira, {Jo{\~a}o F.} and Moura, {Guilherme V.} and Santos, {Andr{\'e} A.P.}",

note = "Funding Information: Jo{\~a}o F. Caldeira gratefully acknowledges support provided by CNPq under grants 309899/2015-0 and 485561/2013-1 .Guilherme V. Moura gratefully acknowledges support provided by CNPq under grants 475230/2012-4 , and 308346/2013-0 . Andr{\'e} A. P. Santos gratefully acknowledges supports provided by CNPq under grants 408723/2013-0 , 406493/2012-0 , and 307320/2013-8 . The authors thank the editor, the associate editor, and two anonymous reviewers for detailed comments on our manuscript. Publisher Copyright: {\textcopyright} 2016 Elsevier B.V.. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.",

year = "2016",

month = jun,

day = "1",

doi = "10.1016/j.jempfin.2016.03.004",

language = "English",

volume = "37",

pages = "128--158",

journal = "Journal of Empirical Finance",

issn = "0927-5398",

publisher = "Elsevier",

}

TY - JOUR

T1 - Bond portfolio optimization using dynamic factor models

AU - Caldeira, João F.

AU - Moura, Guilherme V.

AU - Santos, André A.P.

N1 - Funding Information: João F. Caldeira gratefully acknowledges support provided by CNPq under grants 309899/2015-0 and 485561/2013-1 .Guilherme V. Moura gratefully acknowledges support provided by CNPq under grants 475230/2012-4 , and 308346/2013-0 . André A. P. Santos gratefully acknowledges supports provided by CNPq under grants 408723/2013-0 , 406493/2012-0 , and 307320/2013-8 . The authors thank the editor, the associate editor, and two anonymous reviewers for detailed comments on our manuscript. Publisher Copyright: © 2016 Elsevier B.V.. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.

PY - 2016/6/1

Y1 - 2016/6/1

N2 - A general class of dynamic factor models is used to obtain optimal bond portfolios, and to develop a duration-constrained mean-variance optimization, which can be used to improve bond indexing. An empirical application involving two large data sets of U.S. Treasuries shows that the proposed portfolio policy outperforms a set of yield curve strategies used in bond desks. Additionally, we propose a dynamic rule to switch among alternative bond investment strategies, and find that the benefits of such dynamic rule are even more pronounced when the set of available policies is augmented with the proposed mean-variance portfolios.

AB - A general class of dynamic factor models is used to obtain optimal bond portfolios, and to develop a duration-constrained mean-variance optimization, which can be used to improve bond indexing. An empirical application involving two large data sets of U.S. Treasuries shows that the proposed portfolio policy outperforms a set of yield curve strategies used in bond desks. Additionally, we propose a dynamic rule to switch among alternative bond investment strategies, and find that the benefits of such dynamic rule are even more pronounced when the set of available policies is augmented with the proposed mean-variance portfolios.

KW - bond indexing

KW - C53

KW - dynamic policy selection

KW - E43

KW - G17

KW - Kalman filter

KW - out-of-sample evaluation

KW - portfolio optimization

KW - yield curve forecasts

UR - http://www.scopus.com/inward/record.url?scp=84962356064&partnerID=8YFLogxK

U2 - 10.1016/j.jempfin.2016.03.004

DO - 10.1016/j.jempfin.2016.03.004

M3 - Article

AN - SCOPUS:84962356064

SN - 0927-5398

VL - 37

SP - 128

EP - 158

JO - Journal of Empirical Finance

JF - Journal of Empirical Finance

ER -

Bond portfolio optimization using dynamic factor models

Abstract / Description of output

Keywords / Materials (for Non-textual outputs)

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