Asset pricing models with errors-in-variables

Carmichæl, Benoı̂t; Coën, Alain

doi:10.1016/j.jempfin.2008.01.002

Cited by 27 publications

(22 citation statements)

References 39 publications

(53 reference statements)

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“…When the literature is examined, it is seen that very small number of studies paid regard to the power of adding the momentum factor to SMB and HML factors of Fama and French (WML-winners minus losers) in explaining stock returns. (Carhart 1997, Jegadeesh 2000, Liew and Vassalou 2000, Kim and Kim 2003 Asian Journal of Finance & Accounting ISSN 1946-052X 2013 Masmoudi; Suret 2004, Bello 2007, Carmichael and Coën 2008Li;So 2009;Unlu, 2012). The four factor model was first tested by Carhart (1997).…”

Section: The Four-factor Modelmentioning

confidence: 99%

Evidence to Support Multifactor Asset Pricing Models: The Case of The Istanbul Stock Exchange

Ünlü

2013

AJFA

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The objective of this study is to test, instead of the single factor CAPM, the power of three factor, four factor and five factor models to explain stock returns for the Istanbul Stock Exchange (ISE). The test results of all models in the ISE indicate that the models are applicable. During the period covering July 1992-June 2011 it is consequently established that, in addition to the market risk, size, book to market ratio, momentum and liquidity factors also constitute significant risk factors that affect the expected stock returns in the ISE and that the risk premiums belonging to these five factors are priced by the market.

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Section: The Four-factor Modelmentioning

confidence: 99%

Evidence to Support Multifactor Asset Pricing Models: The Case of The Istanbul Stock Exchange

Ünlü

2013

AJFA

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“…They converge to their true value if true factors are observable. For a formal proof, see Carmichael and Coën (2008). variables.…”

Section: Introductionmentioning

confidence: 99%

Risk and performance estimation in hedge funds revisited: Evidence from errors in variables

Coën

Hübner

2009

Journal of Empirical Finance

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“…We use the method developed by Dagenais and Dagenais (1997) and applied by Coën and Racicot (2007) and Carmichael and Coën (2008), thereby creating new regressors that account for the estimated measurement errors using the higher moments, that is, the (cross) skewness and the (cross) kurtosis. To compute the EIV series corresponding to each selected benchmark, we use the following and we estimate k (artificial) OLS regressions : 8 with where n is the number of observations; i is a ( n × 1) vector where the elements are all one; I n and I k are identity matrices of order n and k , respectively; F denotes a ( n × k ) matrix containing the k selected benchmarks; * is the Hadamard element‐by‐element matrix multiplication operator; the matrix f stands for the matrix F calculated in mean deviation; is a ( n × k ) matrix containing estimators of the true benchmarks; and is a ( n × k ) matrix standing for the estimated matrix of error terms (estimates of EIV).…”

Section: Errors‐in‐variablesmentioning

confidence: 99%

“…3 3 Carmichael and Coën (2008) show that the Dagenais and Dagenais (1997) instrumental variables estimator is appropriate to assess the EIV problem for linear asset pricing models. …”

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confidence: 99%

Dynamic Hedge Fund Style Analysis With Errors‐in‐variables

Bodson

Coën

Hübner

2010

J of Financial Research

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We revisit the traditional return-based style analysis in the presence of time-varying exposures and errors-in-variables (EIV). We apply a benchmark selection algorithm using the Kalman filter and compute the estimated EIV of the selected benchmarks. We adjust them by subtracting their EIV from the initial return series to obtain an estimate of the true uncontaminated benchmarks. Finally, we run the Kalman filter on these adjusted regressors. Analyzing EDHEC alternative index styles, we show that this technique improves the factor loadings and allows more precise identification of the return sources of the considered hedge fund strategy. Copyright (c) 2010 The Southern Finance Association and the Southwestern Finance Association.

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Asset pricing models with errors-in-variables

Cited by 27 publications

References 39 publications

Evidence to Support Multifactor Asset Pricing Models: The Case of The Istanbul Stock Exchange

Evidence to Support Multifactor Asset Pricing Models: The Case of The Istanbul Stock Exchange

Risk and performance estimation in hedge funds revisited: Evidence from errors in variables

Dynamic Hedge Fund Style Analysis With Errors‐in‐variables

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