### Abstract

Original language | English |
---|---|

Place of Publication | Tilburg |

Publisher | NETSPAR |

Number of pages | 48 |

Publication status | Published - Mar 2016 |

### Publication series

Name | Netspar Academic Paper |
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Volume | DP 03/2016-020 |

### Fingerprint

### Keywords

- factor pricing models
- risk-return models
- omitted factors
- misspecified models

### Cite this

*Linear Factor Models and the Estimation of Expected Returns*. (Netspar Academic Paper; Vol. DP 03/2016-020). Tilburg: NETSPAR.

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**Linear Factor Models and the Estimation of Expected Returns.** / Sarisoy, Cisil; de Goeij, Peter; Werker, Bas.

Research output: Working paper › Discussion paper › Other research output

TY - UNPB

T1 - Linear Factor Models and the Estimation of Expected Returns

AU - Sarisoy, Cisil

AU - de Goeij, Peter

AU - Werker, Bas

PY - 2016/3

Y1 - 2016/3

N2 - Linear factor models of asset pricing imply a linear relationship between expected returns of assets and exposures to one or more sources of risk. We show that exploiting this linear relationship leads to statistical gains of up to 31% in variances when estimating expected returns on individual assets over historical averages. When the factors are weakly correlated with assets, i.e. β’s are small, and the interest is in estimating expected excess returns, that is risk premiums, on individual assets rather than the prices of risk, the Generalized Method of Moment estimators of risk premiums does lead to reliable inference, i.e. limiting variances suffer from neither lack of identification nor unboundedness. If the factor model is misspecified in the sense of an omitted factor, we show that factor model-based estimates may be inconsistent. However, we show that adding an alpha to the model capturing mispricing only leads to consistent estimators in case of traded factors. Moreover, our simulation experiment documents that using the more precise estimates of expected returns based on factor{models rather than the historical averages translates into significant improvements in the out-of-sample performances of the optimal portfolios.

AB - Linear factor models of asset pricing imply a linear relationship between expected returns of assets and exposures to one or more sources of risk. We show that exploiting this linear relationship leads to statistical gains of up to 31% in variances when estimating expected returns on individual assets over historical averages. When the factors are weakly correlated with assets, i.e. β’s are small, and the interest is in estimating expected excess returns, that is risk premiums, on individual assets rather than the prices of risk, the Generalized Method of Moment estimators of risk premiums does lead to reliable inference, i.e. limiting variances suffer from neither lack of identification nor unboundedness. If the factor model is misspecified in the sense of an omitted factor, we show that factor model-based estimates may be inconsistent. However, we show that adding an alpha to the model capturing mispricing only leads to consistent estimators in case of traded factors. Moreover, our simulation experiment documents that using the more precise estimates of expected returns based on factor{models rather than the historical averages translates into significant improvements in the out-of-sample performances of the optimal portfolios.

KW - factor pricing models

KW - risk-return models

KW - omitted factors

KW - misspecified models

M3 - Discussion paper

T3 - Netspar Academic Paper

BT - Linear Factor Models and the Estimation of Expected Returns

PB - NETSPAR

CY - Tilburg

ER -