Modeling the persistence in expected returns

Dooruj Rambaccussing

Modeling the persistence in expected returns

Dooruj Rambaccussing (Lead / Corresponding author)

Research output: Contribution to conference › Paper › peer-review

Abstract

The major contribution of this paper is to explicitly model the persistence
in the time series of expected returns. The series of expected returns
is derived from a state space representation of the net present value identity
relating expected returns and expected dividend (earnings) growth to
the observed price dividend (earnings) ratio. The state space model is
adjusted in order to include the possibility of expected returns following
an autoregressive fractional integrated (ARFIMA) process which captures
the persistence of the process. The new ARFIMA model performs moderately
compared to the simple autoregressive process, which may be due
to the presence of different regimes and structural breaks. The expected
returns series is applied in three instances namely in predictive regressions,
analysing the relationship between consumption and discount rates and
also in a market timing strategy.

Original language	English
Number of pages	42
Publication status	Published - 2012
Event	Royal Economic Society - PhD meeting 2012 - Queen Mary University of London, London, United Kingdom Duration: 21 Jan 2012 → 22 Jan 2012 http://www.res.org.uk/view/2012postgraduateJob.html

Conference

Conference	Royal Economic Society - PhD meeting 2012
Country/Territory	United Kingdom
City	London
Period	21/01/12 → 22/01/12
Internet address	http://www.res.org.uk/view/2012postgraduateJob.html

Keywords

Expected Returns
Persistence
Present Value
State Space Modelling

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http://www.res.org.uk/view/postgraduateJob.html

Embargoed Document

Modeling persistence10 (1)
Submitted manuscript, 513 KB
Embargo ends: 3/12/99

Cite this

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title = "Modeling the persistence in expected returns",

abstract = "The major contribution of this paper is to explicitly model the persistencein the time series of expected returns. The series of expected returnsis derived from a state space representation of the net present value identityrelating expected returns and expected dividend (earnings) growth tothe observed price dividend (earnings) ratio. The state space model isadjusted in order to include the possibility of expected returns followingan autoregressive fractional integrated (ARFIMA) process which capturesthe persistence of the process. The new ARFIMA model performs moderatelycompared to the simple autoregressive process, which may be dueto the presence of different regimes and structural breaks. The expectedreturns series is applied in three instances namely in predictive regressions,analysing the relationship between consumption and discount rates andalso in a market timing strategy.",

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AU - Rambaccussing, Dooruj

PY - 2012

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N2 - The major contribution of this paper is to explicitly model the persistencein the time series of expected returns. The series of expected returnsis derived from a state space representation of the net present value identityrelating expected returns and expected dividend (earnings) growth tothe observed price dividend (earnings) ratio. The state space model isadjusted in order to include the possibility of expected returns followingan autoregressive fractional integrated (ARFIMA) process which capturesthe persistence of the process. The new ARFIMA model performs moderatelycompared to the simple autoregressive process, which may be dueto the presence of different regimes and structural breaks. The expectedreturns series is applied in three instances namely in predictive regressions,analysing the relationship between consumption and discount rates andalso in a market timing strategy.

AB - The major contribution of this paper is to explicitly model the persistencein the time series of expected returns. The series of expected returnsis derived from a state space representation of the net present value identityrelating expected returns and expected dividend (earnings) growth tothe observed price dividend (earnings) ratio. The state space model isadjusted in order to include the possibility of expected returns followingan autoregressive fractional integrated (ARFIMA) process which capturesthe persistence of the process. The new ARFIMA model performs moderatelycompared to the simple autoregressive process, which may be dueto the presence of different regimes and structural breaks. The expectedreturns series is applied in three instances namely in predictive regressions,analysing the relationship between consumption and discount rates andalso in a market timing strategy.

KW - Expected Returns

KW - Persistence

KW - Present Value

KW - State Space Modelling

M3 - Paper

T2 - Royal Economic Society - PhD meeting 2012

Y2 - 21 January 2012 through 22 January 2012

ER -

Modeling the persistence in expected returns

Abstract

Conference

Keywords

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Embargoed Document

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Cite this