CAPM

Assignment



Capital Asset Pricing Model

Introduction

The modern theory of decision making in uncertainty introduces a generic framework for measuring risk and performance of an asset held as part of a portfolio and market equilibrium conditions. This framework is called a model of pricing of capital assets or CAPM (the Capital Asset Pricing Model English). For this model the risk of action is divided into diversifiable risk or company-specific risk and undiversifiable risk or market. The latter risk is more important for the CAPM and is measured by its beta coefficient. This ratio relates the excess return of action regarding the risk-free rate and the excess market return over the risk free rate.

Huang & Litzenberger is an excellent textbook that covers econometric issues in testing the CAPM. They argue that whether the CAPM fulfils itself as a positive asset pricing model should not be judged by the realism of its assumptions. It may be impossible to model a long and detailed list of realistic assumptions, and this would at best be a mere institutional description that may not have any predictive value in itself. Consequently, the early tests on the CAPM focus on the predictive content of betas. They also highlight three conceptual problems associated with testing the CAPM. First, the CAPM implies relationships between ex ante risk premia and betas that are not directly observable. Second, these premia and betas are unlikely to be stationary over time. Third, many assets are not marketable and tests of the CAPM are invariably based on proxies for the market portfolio that excluded major classes of assets such as human capital, private businesses, and private real estates.

Discussion

Traditionally, the beta is obtained by means of a linear regression of two variables depending on the assumption that the yield in excess of the action, analyzed as a time series, has homoscedastic conditional variance. Although the CAPM and other models that measure the risk of an asset received severe criticism in recent years we find a class of models belonging to the theory of time series, trying to overcome the structural inefficiencies of financial models . Such models are called autoregressive conditional heteroskedasticity models (ARCH, GARCH and arch-m). The model arch and garch model, as background to the arch-m model, study the conditional variance in the time variable from variables lagged relationships. The arch model expresses the conditional variance linear function of lagged squared innovations and the garch model determines the conditional variance in terms of innovation and variance delayed several periods (Jagannathan and Wang, 1996, 3-53). An extension of the model arch and garch model is the arch-m model, which is mainly applied in the measurement of risk and expected return on a risky asset. This model does depend on the conditional mean of the conditional variance variable. The method used by the arch-m model to estimate the price of the risk variable is to introduce the conditional variance of financial time series as a regressor of the expected return on a risky ...