Capital Asset Pricing Model (Capm)

2543 words 11 pages

Capital asset pricing has always been an active area in the finance literature. Capital Asset Pricing Model (CAPM) is one of the economic models used to determine the market price for risk and the appropriate measure of risk for a single asset. The CAPM shows that the equilibrium rates of return on all risky assets are function of their covariance with the market portfolio. This theory helps us understand why expected returns change through time. Furthermore, this model is developed in a hypothetical world with many assumptions.

The Sharp-Lintner-Black CAPM states that the expected return of any capital asset is proportional to its systematic risk measured by the beta. (Iqbal and Brooks, 2007). Based on some simplifying
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Kim (1995, 1997) uses methodology to correct for selection bias and errors-invariables biases (measurement bias effects). With these corrections, he finds that beta has statistically significant explanatory power, but other variables such as firm size and book-to-market are also significant. In Kim (1997), firm size is significant when using monthly returns but is not significant when using quarterly returns.

Guan, Hansen, Leikam and Shaw (2007) uses methodological improvements to reduce potential measurement error in beta. Based on their study, they conclude that the presence of size, book-to market, and price earnings variables as explanatory variables for average cross-sectional returns is not inconsistent with a valid CAPM. These idiosyncratic variables are part of the definitional makeup of expected return and should be highly correlated with expected returns even if returns are generated by the CAPM. Furthermore, if beta is measured with error, then these idiosyncratic variables can appear as significant variables in an equation that also include beta. Moreover, Guan, Hansen, Leikam and Shaw hypothesize that as measurement error in beta is reduced, then the significance of idiosyncratic variables will decrease. The empirical evidence of the study is consistent with their hypothesis. As non-stationary error and assignment error in beta are reduced or eliminated, the significance of beta increases and that of size decreases.



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