Empirical Tests of Asset Pricing Models with Individual Assets: Resolving the Errors-in-Variables Bias in Risk Premium Estimation

In the rational expectations equilibrium of this paper, agents have private information and differing information partitions and therefore assign differing conditional distributions to asset payoffs and other economic variables relevant to their investment choices. Standard asset pricing models typically do not recognize the impact of these differing information partitions, and empirical tests based on these models thus measure asset riskiness in a way that may not be relevant to any of the agents' decisions. I show how this can lead to distorted estimates of investment risk and how it can make the equity premium appear difficult to explain.

Download Full-text

The Bias in Two-Pass Regression Tests of Asset-Pricing Models in Presence of Idiosyncratic Errors with Cross-Sectional Dependence

Review of Pacific Basin Financial Markets and Policies ◽

10.1142/s0219091519500127 ◽

2019 ◽

Vol 22 (02) ◽

pp. 1950012

Author(s):

Thomas Gramespacher ◽

Armin Bänziger

Keyword(s):

Asset Pricing ◽

Risk Premium ◽

Measurement Errors ◽

Factor Model ◽

Pricing Models ◽

Time Series Regression ◽

Cross Sectional ◽

Asset Pricing Models ◽

Slope Estimator ◽

Regression Tests

In two-pass regression-tests of asset-pricing models, cross-sectional correlations in the errors of the first-pass time-series regression lead to correlated measurement errors in the betas used as explanatory variables in the second-pass cross-sectional regression. The slope estimator of the second-pass regression is an estimate for the factor risk-premium and its significance is decisive for the validity of the pricing model. While it is well known that the slope estimator is downward biased in presence of uncorrelated measurement errors, we show in this paper that the correlations seen in empirical return data substantially suppress this bias. For the case of a single-factor model, we calculate the bias of the OLS slope estimator in the presence of correlated measurement errors with a first-order Taylor-approximation in the size of the errors. We show that the bias increases with the size of the errors, but decreases the more the errors are correlated. We illustrate and validate our result using a simulation approach based on empirical data commonly used in asset-pricing tests.

Download Full-text

International Real Estate Review

International Real Estate Review ◽

10.53383/100096 ◽

2008 ◽

Vol 11 (2) ◽

pp. 32-46

Author(s):

John Okunev ◽

◽

Patrick J. Wilson ◽

Keyword(s):

Asset Pricing ◽

Real Estate ◽

Risk Premium ◽

Time Variation ◽

Trading Strategies ◽

Real Estate Investment Trust ◽

Pricing Models ◽

Real Estate Investment ◽

Risk Premiums ◽

Asset Pricing Models

This study presents further evidence of the predictability of excess equity REIT (real estate investment trust) returns . Recent evidence on forecasting excess returns using fundamental variables has resulted in diminishing returns from the 1990’s onward. Trading strategies based on these forecasts have not significantly outperformed the buy/hold strategy of the 1990’s. We have developed an alternative strategy that is based on the time variation of the risk premium of investors. Our results indicate that it is possible to outperform the buy/hold strategy by modeling the time variation of the risk premium. By modeling the dynamic behavior of the risk premium, we are able to implicitly capture economic risk premiums that are not captured by conventional multi beta asset pricing models.

Download Full-text

Asset pricing models with errors-in-variables

Journal of Empirical Finance ◽

10.1016/j.jempfin.2008.01.002 ◽

2008 ◽

Vol 15 (4) ◽

pp. 778-788 ◽

Cited By ~ 13

Author(s):

Benoît Carmichael ◽

Alain Coën

Keyword(s):

Asset Pricing ◽

Errors In Variables ◽

Pricing Models ◽

Asset Pricing Models

Download Full-text

A Synthesis of Two Factor Estimation Methods

Journal of Financial and Quantitative Analysis ◽

10.1017/s0022109015000307 ◽

2015 ◽

Vol 50 (4) ◽

pp. 825-842 ◽

Cited By ~ 4

Author(s):

Gregory Connor ◽

Robert A. Korajczyk ◽

Robert T. Uhlaner

Keyword(s):

Fixed Point ◽

Asset Pricing ◽

Risk Premia ◽

Estimation Methods ◽

Errors In Variables ◽

Pricing Models ◽

Cross Sectional ◽

Asset Pricing Models ◽

The Common ◽

Factor Estimation

AbstractTwo-pass cross-sectional regression (TPCSR) is frequently used in estimating factor risk premia. Recent papers argue that the common practice of grouping assets into portfolios to reduce the errors-in-variables (EIV) problem leads to loss of efficiency and masks potential deviations from asset pricing models. One solution that allows the use of individual assets while overcoming the EIV problem is iterated TPCSR (ITPCSR). ITPCSR converges to a fixed point regardless of the initial factors chosen. ITPCSR is intimately linked to the asymptotic principal components (APC) method of estimating factors since the ITPCSR estimates are the APC estimates, up to a rotation.

Download Full-text